University of California San Diego
Public University • US
Showing 127 courses from University of California San Diego
Linux Tools for Text Processing
University of California San Diego (via Coursera)
Many fields, such as data science, bioinformatics, and Linux systems administration, require the manipulation of textual data. Tasks include extracting fields or records meeting certain conditions from structured data (e.g., comma-separated files), combining content from multiple files, applying systematic changes to all lines of a document, sorting or randomizing data, and splitting larger files into smaller files. While these operations could be done by hand, they tend to be time-consuming, tedious and, worst of all, error prone. In this course we systematically explore the text processing tools found in Linux and Linux-like environments that enable you to simplify and automate these tasks. We’ll begin with the simplest utilities, covering the features of head, tail, paste, nl, sort, shuffle, split, tr and cut. We’ll then move onto the tools grep, awk and sed, which provide much more powerful capabilities for searching and manipulation. We conclude with an introduction to regular expressions (regexes) and explain how they can be used to specify richer and more complex patterns. Regex topics will include quantifiers, wildcards, anchors, character classes, grouping and alternation, along with advanced concepts such as word boundaries, lazy and greedy matching, and regex flavors.
Finding Hidden Messages in DNA (Bioinformatics I)
University of California San Diego (via Coursera)
Named a top 50 MOOC of all time by Class Central! This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. In the first half of the course, we investigate DNA replication, and ask the question, where in the genome does DNA replication begin? We will see that we can answer this question for many bacteria using only some straightforward algorithms to look for hidden messages in the genome. In the second half of the course, we examine a different biological question, when we ask which DNA patterns play the role of molecular clocks. The cells in your body manage to maintain a circadian rhythm, but how is this achieved on the level of DNA? Once again, we will see that by knowing which hidden messages to look for, we can start to understand the amazingly complex language of DNA. Perhaps surprisingly, we will apply randomized algorithms, which roll dice and flip coins in order to solve problems. Finally, you will get your hands dirty and apply existing software tools to find recurring biological motifs within genes that are responsible for helping Mycobacterium tuberculosis go "dormant" within a host for many years before causing an active infection.
Information Design
University of California San Diego (via Coursera)
A blank canvas is full of possibility. If you have an idea for a user experience, how do you turn it into a beautiful and effective user interface? This covers covers principles of visual design so that you can effectively organize and present information with your interfaces. You'll learn concrete strategies to create user interfaces, including key lessons in typography, information architecture, layout, color, and more. You’ll learn particular issues that arise in new device contexts, such as mobile and responsive interfaces. You will learn how to apply these design principles in a modern context of increasingly diverse form factors - from tablets, to walls, to watches.
Internet of Things Capstone V2: Build a Mobile Surveillance System
University of California San Diego (via Coursera)
In the Capstone project for the Internet of Things specialization, you will design and build your own system that uses at least 2 sensors, at least 1 communication protocol and at least 1 actuator. You will have a chance to revisit and apply what you have learned in our courses to achieve a robust, practical and/or fun-filled project. We absolutely encourage you to design whatever you can think up! This is your chance to be creative or to explore an idea that you have had. But if you don’t have your own idea, we provide the description of a surveillance system, for you to build. We will participate in the Capstone with you by building a surveillance system that features an off-grid solar powered workstation that will serve as a hub to multiple surveillance sensors. You will be able to demonstrate the knowledge and skills you have gained in this course through delivery of industry-appropriate documents such as System Design documents and Unit Test reports. Additionally, you will be asked to describe and show case your project as a short video presentation – appropriate for demonstrating your knowledge and technical communication skills. Learning Goals: After completing this Capstone, you will be able to: 1. Design systems using mobile platforms. You will gain experience in documenting and presenting designs. 2. Develop systems that interface multiple sensors and actuators to the DragonBoard™ 410c system and develop the necessary software to create a fully functional system. 3. Specify unit tests and system tests, run tests and prepare Test Reports as are commonly done by those working in this industry. 4. Gain experience (and feedback!) in making technical presentations.
Computational Thinking for K-12 Educators: Conditional Loops and If Statements
University of California San Diego (via Coursera)
Want to make a game that ends when you "catch" an object by clicking on it? Or maybe you get points based on how close you came? You'll do that in this class! This class teaches the concepts of conditional loops and if/else statements. For each concept, we'll start by helping you connect real-world experiences you are already familiar with to the programming concept you are about to learn. Next, through a cognitively scaffolded process we'll engage you in developing your fluency with problem solving with repeat until loops, while loops, and if/else statements in a way that keeps frustration at a minimum. Along the way you will learn about the common challenges or "bugs" students have with these concepts as well as ways to help them find and fix those concepts. You'll also be guided in running classroom discussions to help students develop deeper understanding of these concepts. Finally, you'll learn how to support interactive learning experiences among your students with Peer Instruction. Additionally, you will create a resource for your classroom to support an equitable classroom.
Computational Thinking for K-12 Educators Capstone
University of California San Diego (via Coursera)
In this capstone project course, you will learn to support your students in successfully completing the Advanced Placement Principles Create Task -- however this task can be useful for any course as a culminating, student-designed final programming project. You will learn to interpret and practice applying to real sample student work the Create Task rubric and have the option to modify it for your own setting. You'll prepare resources to help students through the challenges that come with doing an open-ended project that still needs to meet certain specifications. Finally, you'll complete your own Create Task assignment including writing about the ways in which you designed algorithms, used abstraction, and struggled with a challenge while completing the task. You'll be prepared to help students do well on the Advanced Placement Computer Science Principles Create Task!
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
University of California San Diego (via Coursera)
Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.
A New Communication Framework for Healthcare
University of California San Diego (via Coursera)
As clinical care providers, we shoulder the stories of many patients. Because we’re usually in a rush, we don’t have the luxury of finding out all the information that we would like to. But no matter the situation, our understanding of the human story will determine a great deal about our communication. Research shows that when we hear a story, our brain begins to sync with the storyteller. We quickly make connections drawn from our own lives and can fill in other aspects in a story like meaning, emotion, narrative trajectory and so on. As the listener, it’s relatively easy to hear a story and imagine, empathize and even come up with possible solutions to alleviate pain. But not so easy when you are the provider—and part of the story itself. Why is that? Why can we do it from a distance, but it’s so difficult to see when we are one of the people in the story? This course discusses these questions. Join palliative care physician Dr. Kathryn Winters, along with the Sanford Institute’s Center for Compassionate Communication at UC San Diego Health, for this fascinating, personal and instructive lesson on how to better understand the stories involved in clinical care—both provider and patient.
Genomic Data Science and Clustering (Bioinformatics V)
University of California San Diego (via Coursera)
How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters. In the first half of the course, we will introduce algorithms for clustering a group of objects into a collection of clusters based on their similarity, a classic problem in data science, and see how these algorithms can be applied to gene expression data. In the second half of the course, we will introduce another classic tool in data science called principal components analysis that can be used to preprocess multidimensional data before clustering in an effort to greatly reduce the number dimensions without losing much of the "signal" in the data. Finally, you will learn how to apply popular bioinformatics software tools to solve a real problem in clustering.
Data Structures and Performance
University of California San Diego (via Coursera)
How do Java programs deal with vast quantities of data? Many of the data structures and algorithms that work with introductory toy examples break when applications process real, large data sets. Efficiency is critical, but how do we achieve it, and how do we even measure it? This is an intermediate Java course. We recommend this course to learners who have previous experience in software development or a background in computer science, and in particular, we recommend that you have taken the first course in this specialization (which also requires some previous experience with Java). In this course, you will use and analyze data structures that are used in industry-level applications, such as linked lists, trees, and hashtables. You will explain how these data structures make programs more efficient and flexible. You will apply asymptotic Big-O analysis to describe the performance of algorithms and evaluate which strategy to use for efficient data retrieval, addition of new data, deletion of elements, and/or memory usage. The program you will build throughout this course allows its user to manage, manipulate and reason about large sets of textual data. This is an intermediate Java course, and we will build on your prior knowledge. This course is designed around the same video series as in our first course in this specialization, including explanations of core content, learner videos, student and engineer testimonials, and support videos -- to better allow you to choose your own path through the course!
Data Structures
University of California San Diego (via Coursera)
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
Capstone Project: Teaching Impacts of Technology
University of California San Diego (via Coursera)
In this project-based course you’ll review the Advanced Placement Computer Science Principles course and exam description guide to prepare for the “Explore Task”, where students must research a recent computing innovation and and analyze its impacts on the world. You’ll also review the description of this task from the student perspective and complete the task yourself. Then you’ll assess sample secondary student work by following the APCSP scoring guidelines as well as provide feedback to a fellow learner on their submitted task and receive the same from fellow learners. This course is part of a larger Specialization, in which the first five courses focus on teaching impacts of computing concepts and the technology and computing concepts that make them possible, preparing you to teach pre-college learners to be both savvy and effective participants in their digital worlds. While this course and project can be completed without taking the other courses, the bulk of new knowledge is taught there. Additionally, throughout the courses you’ll reflect on your learning experience from both the perspective of the student and the educator, helping you become a more reflective teacher and develop an understanding of how instruction and activities can be designed to support learning. Note, if your goal is to receive graduate credit from the University of California, San Diego, you need to to make that decision before you complete this course. Please see the FAQ “Will I earn university credit for completing this course?” for details on how to receive that credit. In terms of CSTA K-12 computer science standards, throughout the Specialization we primarily cover learning objectives within the “impacts of computing” concept, while also including some within the “networks and the Internet” concepts and the “data and analysis” concept. Practices we cover include “fostering and inclusive computing culture”, “recognizing and defining computational problems”, and “communicating...
Hacking COVID-19 — Course 2: Decoding SARS-CoV-2's Secrets
University of California San Diego (via Coursera)
In this course, you will follow in the footsteps of the bioinformaticians investigating the COVID-19 outbreak by annotating the SARS-CoV-2 genome and using the annotation to design a COVID-19 diagnostic test. Whether you’re new to the world of computational biology, or you’re a bioinformatics expert seeking to learn about its applications in the COVID-19 pandemic, or somewhere in between, this course is for you! As you go through this journey, we will introduce and explain genomic concepts and give you many opportunities to practice your skills, and we will provide a series of problems with gradually increasing complexity. This second course will only discuss the annotation of the SARS-CoV-2 genome, but future courses in this series will explore follow-up bioinformatics analyses used in the COVID-19 pandemic.
Learn to Teach Java: Boolean Expressions, If Statements, and Iteration
University of California San Diego (via Coursera)
Learn to program with Boolean Expressions, If Statement, and For and While Loops in Java, and prepare to teach others using the free, online interactive CS Awesome textbook. In this course for teachers we'll guide you both in learning Java concepts and skills but also in how to effectively teach those to your students. This course will support you in teaching the Advanced Placement Computer Science A course or a similar introductory university-level programming course. We'll cover the critical Java concepts of selection (if statements) and iteration (loops), as covered in the APCS A Units 3 and 4. Each topic will begin by relating Java to block-based programming languages and then provide video overviews of CS Awesome content along with additional materials to supplement learning for your students. You'll engage with additional materials to support your teaching including "deep dive" classroom discussion questions, assessment overviews, code tracing and problem solving skills for your students, including preparation for free response coding questions.
Computational Thinking for K-12 Educators: Sequences and Loops
University of California San Diego (via Coursera)
How do we give instructions to a computer? Isn't programming hard? Not really! Whether it's giving someone directions to a nearby store or writing out some dance moves we frequently exhibit aspects of computational thinking in our everyday lives! This class teaches the first key concepts of programming -- sequences of instructions and basic counted repetition of instructions. For each concept, we'll start by helping you connect real-world experiences you are already familiar with to the programming concept you are about to learn. Next, through a cognitively scaffolded process we'll engage you in developing your fluency with problem solving with sequences and repeated instructions in a way that keeps frustration at a minimum. Along the way you will learn about the common challenges or "bugs" students have with these concepts as well as ways to help them find and fix those concepts. You'll also be guided in running classroom discussions to help students develop deeper understanding of these concepts. Finally, you'll learn about a recommended pedagogical practice, Pair Programming, and find out why research recommends teaching block-based programming first.
Genome Sequencing (Bioinformatics II)
University of California San Diego (via Coursera)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
Internet of Things: Communication Technologies
University of California San Diego (via Coursera)
Have you wondered how “Things” talk to each other and the cloud? Do you understand the alternatives for conveying latency-sensitive real time data versus reliable signaling data? Building on the skills from the Sensing and Actuation course, we will explore protocols to exchange information between processors. In this course, you will learn how VoIP systems like Skype work and implement your own app for voice calls and text messages. You will start by using the Session Initiation Protocol (SIP) for session management. Next, you will learn how voice codecs such as Adaptive Multi Rate (AMR) are used in 3G networks and use them for voice traffic in your app. Learning Goals: After completing this course, you will be able to: 1. Implement session initiation, management and termination on your DragonBoard™ 410c using SIP. 2. Discover other users and exchange device capabilities. 3. Compare and contrast narrowband and wideband codecs and experience the voice quality differences between them. 4. Implement and demonstrate VoIP calls using the DragonBoard 410c.
Teaching Impacts of Technology: Relationships
University of California San Diego (via Coursera)
In this course you’ll focus on how “smart” devices have changed how we interact with others in personal ways, impacting how we stay connected in our increasingly mobile society. This will be done through a series of paired teaching sections, exploring a specific “Impact of Computing” in your typical day and the “Technologies and Computing Concepts” that enable that impact, all at a K12-appropriate level. This course is part of a larger Specialization through which you’ll learn impacts of computing concepts you need to know, organized into 5 distinct digital “worlds”, as well as learn pedagogical techniques and evaluate lesson plans and resources to utilize in your classroom. By the end, you’ll be prepared to teach pre-college learners to be both savvy and effective participants in their digital world. In this particular digital world (relationships), you’ll explore the following Impacts & Technology pairs -- Impacts (Keep me connected in a mobile society):, personal relationships, facebook, circle of friends Technology and Computing Concepts: algorithms, software engineering evolution, heuristics, computer runtime, big O notation, P vs NP Impacts (Making geography-based connections): findings friends, maps, geolocation Technology and Computing Concepts: data and binary, image encoding, pixels, how color pickers work, filters, blurs In the pedagogy section for this course, in which best practices for teaching computing concepts are explored, you’ll learn about the current CSTA K-12 CS Standards and practice using them to review and apply to lesson plans, as well as how to apply the ICAP framework to connect your students’ engagement to active learning outcomes, such as through peer instruction. In terms of CSTA K-12 computer science standards, we’ll primarily cover learning objectives within the “impacts of computing” concept, while also including some within the “networks and the Internet” concepts and the “data and analysis” concept. Practices we cover...
Computational Thinking for K-12 Educators: Abstraction, Methods, and Lists
University of California San Diego (via Coursera)
How do gamers cause things to happen when they hit buttons on their controller? How does the computer keep track of gamer's scores? This class teaches the concepts of nested loops, events, and variables. For each concept, we'll start by helping you connect real-world experiences you are already familiar with to the programming concept you are about to learn. Next, through a cognitively scaffolded process we'll engage you in developing your fluency with problem solving with nested loops, events, and variables in a way that keeps frustration at a minimum. Along the way you will learn about the common challenges or "bugs" students have with these concepts as well as ways to help them find and fix those concepts. You'll also be guided in running classroom discussions to help students develop deeper understanding of these concepts. Finally, you'll learn how to develop low-frustration learning experiences for learning programming via Parsons' Problems., Additionally, you will create an email to either a counselor, administrator or parent organization to help them understand the value of all students taking computer science.
Molecular Evolution (Bioinformatics IV)
University of California San Diego (via Coursera)
In the previous course in the Specialization, we learned how to compare genes, proteins, and genomes. One way we can use these methods is in order to construct a "Tree of Life" showing how a large collection of related organisms have evolved over time. In the first half of the course, we will discuss approaches for evolutionary tree construction that have been the subject of some of the most cited scientific papers of all time, and show how they can resolve quandaries from finding the origin of a deadly virus to locating the birthplace of modern humans. In the second half of the course, we will shift gears and examine the old claim that birds evolved from dinosaurs. How can we prove this? In particular, we will examine a result that claimed that peptides harvested from a T. rex fossil closely matched peptides found in chickens. In particular, we will use methods from computational proteomics to ask how we could assess whether this result is valid or due to some form of contamination. Finally, you will learn how to apply popular bioinformatics software tools to reconstruct an evolutionary tree of ebolaviruses and identify the source of the recent Ebola epidemic that caused global headlines.
Delivery Problem
University of California San Diego (via Coursera)
In this online course we’ll implement (in Python) together efficient programs for a problem needed by delivery companies all over the world millions times per day — the travelling salesman problem. The goal in this problem is to visit all the given places as quickly as possible. How to find an optimal solution to this problem quickly? We still don’t have provably efficient algorithms for this difficult computational problem and this is the essence of the P versus NP problem, the most important open question in Computer Science. Still, we’ll implement several solutions for real world instances of the travelling salesman problem. While designing these solutions, we will rely heavily on the material learned in the courses of the specialization: proof techniques, combinatorics, probability, graph theory. We’ll see several examples of using discrete mathematics ideas to get more and more efficient solutions.
Big Data - Capstone Project
University of California San Diego (via Coursera)
Welcome to the Capstone Project for Big Data! In this culminating project, you will build a big data ecosystem using tools and methods form the earlier courses in this specialization. You will analyze a data set simulating big data generated from a large number of users who are playing our imaginary game "Catch the Pink Flamingo". During the five week Capstone Project, you will walk through the typical big data science steps for acquiring, exploring, preparing, analyzing, and reporting. In the first two weeks, we will introduce you to the data set and guide you through some exploratory analysis using tools such as Splunk and Open Office. Then we will move into more challenging big data problems requiring the more advanced tools you have learned including KNIME, Spark's MLLib and Gephi. Finally, during the fifth and final week, we will show you how to bring it all together to create engaging and compelling reports and slide presentations. As a result of our collaboration with Splunk, a software company focus on analyzing machine-generated big data, learners with the top projects will be eligible to present to Splunk and meet Splunk recruiters and engineering leadership.
Design Principles: an Introduction
University of California San Diego (via Coursera)
What makes an interface intuitive? How can I tell whether one design works better than another? This course will teach you fundamental principles of design and how to effectively evaluate your work with users. You'll learn fundamental principles of visual design so that you can effectively organize and present information with your interfaces. You'll learn principles of perception and cognition that inform effective interaction design. And you'll learn how to perform and analyze controlled experiments online. In many cases, we'll use Web design as the anchoring domain. A lot of the examples will come from the Web, and we'll talk just a bit about Web technologies in particular. When we do so, it will be to support the main goal of this course, which is helping you build human-centered design skills, so that you have the principles and methods to create excellent interfaces with any technology.
User Experience: Research & Prototyping
University of California San Diego (via Coursera)
What makes for a great user experience? How can you consistently design experiences that work well, are easy to use and people want to use? This course will teach you the core process of experience design and how to effectively evaluate your work with the people for whom you are designing. You'll learn fundamental methods of design research that will enable you to effectively understand people, the sequences of their actions, and the context in which they work. Through the assignments, you’ll learn practical techniques for making sense of what you see and transform your observations into meaningful actionable insights and unique opportunity areas for design. You’ll also explore how to generate ideas in response to the opportunities identified and learn methods for making your ideas tangible. By answering specific questions and refining your concepts, you’ll move closer to making your ideas real. We’ll use cases from a variety of industries including health, education, transportation, finance, and beyond to illustrate how these methods work across different domains. Good luck and we hope you enjoy the course!
Introduction to Graph Theory
University of California San Diego (via Coursera)
We invite you to a fascinating journey into Graph Theory — an area which connects the elegance of painting and the rigor of mathematics; is simple, but not unsophisticated. Graph Theory gives us, both an easy way to pictorially represent many major mathematical results, and insights into the deep theories behind them. In this online course, among other intriguing applications, we will see how GPS systems find shortest routes, how engineers design integrated circuits, how biologists assemble genomes, why a political map can always be colored using a few colors. We will study Ramsey Theory which proves that in a large system, complete disorder is impossible! By the end of the course, we will implement an algorithm which finds an optimal assignment of students to schools. This algorithm, developed by David Gale and Lloyd S. Shapley, was later recognized by the conferral of Nobel Prize in Economics. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
Introduction to Algae
University of California San Diego (via Coursera)
This course was produced by the Algae Technology Educational Consortium and UC San Diego with funding from the Algae Foundation, the National Renewable Energy Lab, and the U.S. Department of Energy. Algae are an extremely diverse group of organisms that can be found in almost every ecosystem on the planet, and they play an essential role for life on earth. They are little bio-factories that use the process of photosynthesis to create chemical compounds that we can utilize for food, feed, medicine, and even energy. We’ve brought together some of foremost algae experts from industry and academia to teach you the fundamentals of algae. This course will cover what algae are, why they are important, and why we are interested in them for both their environmental benefit, as well as their use for products. You will also explore the vast diversity of algae including the characteristics and applications of some of the main types of algae that are in commercial use today. Later you will learn about algal ecology and how interactions with environment, including pests and predators, affect algal productivity. And finally you will examine the processes of algae bio-manufacturing including production processes, as well as some of the products, benefits, and challenges that impact our ability to make commercially viable products from algae.
Deploying Machine Learning Models
University of California San Diego (via Coursera)
In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets. This course is the final course in the Python Data Products for Predictive Analytics Specialization, building on the previous three courses (Basic Data Processing and Visualization, Design Thinking and Predictive Analytics for Data Products, and Meaningful Predictive Modeling). At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
Bending the Curve: Climate Change Solutions 3
University of California San Diego (via Coursera)
In the third course, we build on the scientific framework and multifaceted solutions approach to establish the importance of an international approach. We explore the challenges to implementation, along with practical techniques to mitigate the obstacles.
Algorithmic Toolbox
University of California San Diego (via Coursera)
This online course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).
Drug Commercialization
University of California San Diego (via Coursera)
The University of California San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences Drug Commercialization course brings you lectures from both faculty and industry experts. With this course, recorded on campus at UCSD, we seek to share our access to top people in the field who bring an unprecedented range of expertise on drug commercialization. This course will cover pharmacoeconomic, marketing strategy, intellectual property strategy, portfolio management, managed markets and strategic alliances. It will also have a lecture case study from startup to success. In addition, the course will discuss post-marketing clinical trials or Phase 4 trials. These are conducted after a new drug has been approved by the regulatory agencies and launched. In these studies, the new drug is prescribed in an everyday healthcare environment using a much larger group of patients. This enables new treatment uses for the new drug to be developed, comparisons with other treatments for the same indication to be made, and determination of the clinical effectiveness of the new drug in a wider variety of patient types, and more rare side effects, if any, may be detected . Pre-marketing strategy should be instigated as early as Phase 1 clinical trials to ensure that the market's needs are incorporated into the new drug's overall development. Later phases when clinical results are presented at international medical conferences the marketing strategy is then refined in order to develop an awareness amongst the medical community who will be prescribing the new drug. In addition to the marketing strategy, pricing strategy and a tactical plan will be developed. Promotional material, and the sales force will be trained so that when the product is approved they can promote the drug to physician, pharmacist and nurses. This course is intended as part 3 of a series: Drug Discovery (https://www.coursera.org/learn/drug-discovery), Drug Development (https://www.coursera.org/le...
Genome Assembly Programming Challenge
University of California San Diego (via Coursera)
In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.
Learn to Teach Java: Writing Classes and Arrays
University of California San Diego (via Coursera)
Learn to program using Class design and 1-D Arrays in Java, and prepare to teach others using the free, online interactive CS Awesome textbook. In this course for teachers we'll guide you both in learning Java concepts and skills but also in how to effectively teach those to your students. This course will support you in teaching the Advanced Placement Computer Science A course or a similar introductory university-level programming course. We'll cover the critical Java concepts of class design and 1-dimensional arrays, as covered in the APCS A Units 5 and 6. Each topic will begin by relating Java to block-based programming languages and then provide video overviews of CS Awesome content along with additional materials to supplement learning for your students. You'll engage with additional materials to support your teaching including "deep dive" classroom discussion questions, assessment overviews, code tracing and problem solving skills for your students, including preparation for free response coding questions.
Advanced Algorithms and Complexity
University of California San Diego (via Coursera)
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
Bending the Curve: Climate Change Solutions 1
University of California San Diego (via Coursera)
This first course begins the learner’s journey of discovery into the climate crisis. We begin with an exploration of the science, including the human contribution to the crisis. We will develop a multi-faceted framework into which all solutions will be organized. Our first solution will focus on societal transformation and the leadership role that each of us can play in solving the climate crisis.
Number Theory and Cryptography
University of California San Diego (via Coursera)
A prominent expert in the number theory Godfrey Hardy described it in the beginning of 20th century as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the world nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this course we will start with the basics of the number theory and get to cryptographic protocols based on it. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
Hadoop Platform and Application Framework
University of California San Diego (via Coursera)
This course is for novice programmers or business people who would like to understand the core tools used to wrangle and analyze big data. With no prior experience, you will have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry. You will be comfortable explaining the specific components and basic processes of the Hadoop architecture, software stack, and execution environment. In the assignments you will be guided in how data scientists apply the important concepts and techniques such as Map-Reduce that are used to solve fundamental problems in big data. You'll feel empowered to have conversations about big data and the data analysis process.
Bioinformatics Capstone: Big Data in Biology
University of California San Diego (via Coursera)
In this course, you will learn how to use the BaseSpace cloud platform developed by Illumina (our industry partner) to apply several standard bioinformatics software approaches to real biological data. In particular, in a series of Application Challenges will see how genome assembly can be used to track the source of a food poisoning outbreak, how RNA-Sequencing can help us analyze gene expression data on the tissue level, and compare the pros and cons of whole genome vs. whole exome sequencing for finding potentially harmful mutations in a human sample. Plus, hacker track students will have the option to build their own genome assembler and apply it to real data!
Graph Analytics for Big Data
University of California San Diego (via Coursera)
Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.
Teaching Impacts of Technology: Data Collection, Use, and Privacy
University of California San Diego (via Coursera)
In this course you’ll focus on how constant data collection and big data analysis have impacted us, exploring the interplay between using your data and protecting it, as well as thinking about what it could do for you in the future. This will be done through a series of paired teaching sections, exploring a specific “Impact of Computing” in your typical day and the “Technologies and Computing Concepts” that enable that impact, all at a K12-appropriate level. This course is part of a larger Specialization through which you’ll learn impacts of computing concepts you need to know, organized into 5 distinct digital “worlds”, as well as learn pedagogical techniques and evaluate lesson plans and resources to utilize in your classroom. By the end, you’ll be prepared to teach pre-college learners to be both savvy and effective participants in their digital world. In this particular digital world (personal data), you’ll explore the following Impacts & Technology pairs -- Impacts (Show me what I want to see!): Internet Privacy, Custom Ads, Personalization of web pages Technologies and Computing Concepts: Cookies, Web vs Internet, https, Web Servers Impacts (Use my data…. But protect it!): Common Cybersecurity knowledge levels, ISP data collection, Internet design, finding out what is known about you online, software terms and services Technology and Computing Concepts: DNS, Cryptography (ciphers, hashing, encryption, SSL), Deep and Dark Web Impacts (What could my data do for me in the future?): What is Big Data, Machine Learning finds new music, Wearable technologies. Technology and Computing Concepts: AI vs ML, Supervised vs Unsupervised learning, Neural Networks, Recommender systems, Speech recognition In the pedagogy section for this course, in which best practices for teaching computing concepts are explored, you’ll learn how to apply Bloom’s taxonomy to create meaningful CS learning objectives, the importance of retrieval-based learning, to build learning activiti...
Algorithms on Strings
University of California San Diego (via Coursera)
World and internet is full of textual information. We search for information using textual queries, we read websites, books, e-mails. All those are strings from the point of view of computer science. To make sense of all that information and make search efficient, search engines use many string algorithms. Moreover, the emerging field of personalized medicine uses many search algorithms to find disease-causing mutations in the human genome. In this online course you will learn key pattern matching concepts: tries, suffix trees, suffix arrays and even the Burrows-Wheeler transform.
Design Thinking and Predictive Analytics for Data Products
University of California San Diego (via Coursera)
This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
Internet of Things: Sensing and Actuation From Devices
University of California San Diego (via Coursera)
Have you wondered how information from physical devices in the real world gets communicated to Smartphone processors? Do you want to make informed design decisions about sampling frequencies and bit-width requirements for various kinds of sensors? Do you want to gain expertise to affect the real world with actuators such as stepper motors, LEDs and generate notifications? In this course, you will learn to interface common sensors and actuators to the DragonBoard™ 410c hardware. You will then develop software to acquire sensory data, process the data and actuate stepper motors, LEDs, etc. for use in mobile-enabled products. Along the way, you’ll learn to apply both analog-to-digital and digital-to-analog conversion concepts. Learning Goals: After completing this course, you will be able to: 1. Estimate sampling frequency and bit-width required for different sensors. 2. Program GPIOs (general purpose input/output pins) to enable communication between the DragonBoard 410c and common sensors. 3. Write data acquisition code for sensors such as passive and active infrared (IR) sensors, microphones, cameras, GPS, accelerometers, ultrasonic sensors, etc. 4. Write applications that process sensor data and take specific actions, such as stepper motors, LED matrices for digital signage and gaming, etc.
Object Oriented Programming in Java
University of California San Diego (via Coursera)
Welcome to our course on Object Oriented Programming in Java using data visualization. People come to this course with many different goals -- and we are really excited to work with all of you! Some of you want to be professional software developers, others want to improve your programming skills to implement that cool personal project that you’ve been thinking about, while others of you might not yet know why you’re here and are trying to figure out what this course is all about. This is an intermediate Java course. We recommend this course to learners who have previous experience in software development or a background in computer science. Our goal is that by the end of this course each and every one of you feels empowered to create a Java program that’s more advanced than any you have created in the past and that is personally interesting to you. In achieving this goal you will also learn the fundamentals of Object Oriented Programming, how to leverage the power of existing libraries, how to build graphical user interfaces, and how to use some core algorithms for searching and sorting data. And this course is project-based, so we’ll dive right into the project immediately! We are excited to be offering a unique course structure, designed to support learners of different backgrounds in succeeding at their own pace. The first module explains how this will work and if this course is right for you. We also recommend taking a few minutes to explore the course site. A good place to start is the navigation bar on the left. Click Course Content to see what material we’ll cover each week, as well preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. Be sure to introduce yourself to everyone in the Meet and Greet forum. This course should take about 6 weeks to complete. You can check out the recommended course schedule below to see a quick over...
Big Data Modeling and Management Systems
University of California San Diego (via Coursera)
Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools. Through guided hands-on tutorials, you will become familiar with techniques using real-time and semi-structured data examples. Systems and tools discussed include: AsterixDB, HP Vertica, Impala, Neo4j, Redis, SparkSQL. This course provides techniques to extract value from existing untapped data sources and discovering new data sources. At the end of this course, you will be able to: Recognize different data elements in your own work and in everyday life problems Explain why your team needs to design a Big Data Infrastructure Plan and Information System Design Identify the frequent data operations required for various types of data Select a data model to suit the characteristics of your data Apply techniques to handle streaming data Differentiate between a traditional Database Management System and a Big Data Management System Appreciate why there are so many data management systems Design a big data information system for an online game company This course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, ...
Biology Meets Programming: Bioinformatics for Beginners
University of California San Diego (via Coursera)
Are you interested in learning how to program (in Python) within a scientific setting? This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. It offers a gently-paced introduction to our Bioinformatics Specialization (https://www.coursera.org/specializations/bioinformatics), preparing learners to take the first course in the Specialization, "Finding Hidden Messages in DNA" (https://www.coursera.org/learn/dna-analysis). Each of the four weeks in the course will consist of two required components. First, an interactive textbook provides Python programming challenges that arise from real biological problems. If you haven't programmed in Python before, not to worry! We provide "Just-in-Time" exercises from the Codecademy Python track (https://www.codecademy.com/learn/python). And each page in our interactive textbook has its own discussion forum, where you can interact with other learners. Second, each week will culminate in a summary quiz. Lecture videos are also provided that accompany the material, but these videos are optional.
Machine Learning With Big Data
University of California San Diego (via Coursera)
Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems. At the end of the course, you will be able to: • Design an approach to leverage data using the steps in the machine learning process. • Apply machine learning techniques to explore and prepare data for modeling. • Identify the type of machine learning problem in order to apply the appropriate set of techniques. • Construct models that learn from data using widely available open source tools. • Analyze big data problems using scalable machine learning algorithms on Spark. Software Requirements: Cloudera VM, KNIME, Spark
Hacking COVID-19 — Course 5: Tracing SARS-CoV-2's Evolution
University of California San Diego (via Coursera)
In this course, you will follow in the footsteps of the bioinformaticians investigating the COVID-19 outbreak by tracing the evolution of SARS-CoV-2. Whether you’re new to the world of computational biology, or you’re a bioinformatics expert seeking to learn about its applications in the COVID-19 pandemic, or somewhere in between, this course is for you! As you go through this journey, we will introduce and explain genomic concepts and give you many opportunities to practice your skills, and we will provide a series of problems with gradually increasing complexity. This fifth course will discuss the "Italy First" hypothesis of COVID-19 origins, and it will cover bioinformatics methods for rooting and dating a phylogenetic tree inferred from SARS-CoV-2 genome sequences.
Hacking COVID-19: Metabolic Pathway Analysis Yields SARS-CoV-2 Drug Targets
University of California San Diego (via Coursera)
Pathway Bioinformatics is a subfield of Bioinformatics that is concerned with computationally deriving functional insights from genomic data through analysis of molecular networks. This course will present principles and techniques from Pathway Bioinformatics, and will apply these methodologies to the search for drug targets for SARS-CoV-2. The course will begin by discussing motivations for Pathway Bioinformatics, and by presenting an overview of metabolism and of metabolic pathways. Next it will discuss machine representation of pathway data, and methods for pathway visualization. The course will describe how the metabolic pathways of an organism can be inferred from genome data, and how pathways can be used to interpret high-throughput data such as transcriptomics data. It will show how to predict the essential genes of an organism via reachability analysis, and then present a metabolic analysis of human metabolism when interacting with SARS-CoV-2 to predict SARS-CoV-2 drug targets.
Advanced Data Structures in Java
University of California San Diego (via Coursera)
How does Google Maps plan the best route for getting around town given current traffic conditions? How does an internet router forward packets of network traffic to minimize delay? How does an aid group allocate resources to its affiliated local partners? To solve such problems, we first represent the key pieces of data in a complex data structure. In this course, you’ll learn about data structures, like graphs, that are fundamental for working with structured real world data. You will develop, implement, and analyze algorithms for working with this data to solve real world problems. In addition, as the programs you develop in this course become more complex, we’ll examine what makes for good code and class hierarchy design so that you can not only write correct code, but also share it with other people and maintain it in the future. The backbone project in this course will be a route planning application. You will apply the concepts from each Module directly to building an application that allows an autonomous agent (or a human driver!) to navigate its environment. And as usual we have our different video series to help tie the content back to its importance in the real world and to provide tiered levels of support to meet your personal needs.
Introduction to Seaweeds
University of California San Diego (via Coursera)
This course was produced by the Algae Technology Educational Consortium and UC San Diego with funding from the Algae Foundation, the National Renewable Energy Lab, and the U.S. Department of Energy.
Finding Mutations in DNA and Proteins (Bioinformatics VI)
University of California San Diego (via Coursera)
In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome. We will see that the combinatorial pattern matching algorithms solving this problem are elegant and extremely efficient, requiring a surprisingly small amount of runtime and memory. In the second half of the course, we will learn how to identify the function of a protein even if it has been bombarded by so many mutations compared to similar proteins with known functions that it has become barely recognizable. This is the case, for example, in HIV studies, since the virus often mutates so quickly that researchers can struggle to study it. The approach we will use is based on a powerful machine learning tool called a hidden Markov model. Finally, you will learn how to apply popular bioinformatics software tools applying hidden Markov models to compare a protein against a related family of proteins.
Learn to Teach Java: Inheritance and Recursion
University of California San Diego (via Coursera)
Learn to program with Inheritance and Recursion in Java, and prepare to teach others using the free, online interactive CS Awesome textbook. In this course for teachers we'll guide you both in learning Java concepts and skills but also in how to effectively teach those to your students. This course will support you in teaching the Advanced Placement Computer Science A course or a similar introductory university-level programming course. We'll cover the Java concepts of inheritance and recursion, as covered in the APCS A Units 9 and 10. Each topic will begin by relating Java to block-based programming languages and then provide video overviews of CS Awesome content along with additional materials to supplement learning for your students. You'll engage with additional materials to support your teaching including "deep dive" classroom discussion questions, assessment overviews, code tracing and problem solving skills for your students, including preparation for free response coding questions.
빅 데이터 모델링 및 관리 시스템
University of California San Diego (via Coursera)
분석해야 할 빅 데이터 문제를 파악했다면, 빅 데이터 솔루션을 사용해 어떻게 데이터를 수집, 저장 및 정리할 수 있을까요? 이 강의에서는 각 문제에 적합한 다양한 데이터 유형과 관리 도구를 소개합니다. 이 강의를 통해 빅 데이터 관리 시스템과 분석 도구의 관점에서 새로운 빅 데이터 플랫폼이 진화하고 있는 이유를 설명할 있을 것입니다. 이 실습 튜토리얼을 통해 실시간 및 반정형 데이터 사례를 사용한 기술에 익숙해질 것입니다. 여기서 다루는 시스템과 도구에는 다음이 포함됩니다. AsterixDB, HP Vertica, Impala, Neo4j, Redis, SparkSQL. 이 강의는 기존의 미개척 데이터 소스에서 가치를 창출하는 기법과 새로운 데이터 소스를 소개합니다. 이 강의를 마치고 나면 다음을 기대할 수 있습니다. 업무 및 실생활 속 문제들에서 다양한 데이터 요소를 식별할 수 있습니다. 팀에서 빅 데이터 인프라 계획과 정보 시스템 디자인을 설계해야 하는 이유를 설명할 수 있습니다. 다양한 데이터 유형에 요구되는 흔한 데이터 연산을 파악할 수 있습니다. 데이터의 성격에 맞는 데이터 모델을 선택할 수 있습니다. 스트리밍 데이터를 처리하기 위한 기술을 적용할 수 있습니다. 전통적 데이터베이스 관리 시스템과 빅 데이터 관리 시스템의 차이를 알 수 있습니다. 데이터 관리 시스템이 왜 이토록 많은지 이해할 수 있습니다. 온라인 게임 업체에 사용되는 빅 데이터 정보 시스템을 설계할 수 있습니다. 이 강의는 데이터 과학 분야의 초심자를 위한 강의입니다. 빅 데이터 개요를 수강할 것을 권장합니다. 프로그래밍에 대한 사전 지식은 필요하지 않지만, 실습 과제를 수행하려면 애플리케이션을 설치하고 가상 머신을 활용할 수 있어야 합니다. 전체 하드웨어 및 소프트웨어 요구 사항은 전문 기술 요구 사항을 참조하세요. 하드웨어 요구 사항: (A) 쿼드코어 프로세서(VT-x, AMD-V 지원 권장), 64비트 (B) 8GB RAM (C) 20GB 여유 디스크 하드웨어 정보를 찾는 방법: (Windows): 시작 버튼을 눌러 System을 여신 후, Computer를 우클릭해 Properties 메뉴를 확인하세요. (Mac): Apple 메뉴를 클릭해 Overview를 열고 “About this Mac”을 클릭하세요. 3년 이내에 구매한 대부분의 컴퓨터는 8GB RAM 최소 사양을 만족할 것입니다. 최대 4GB 용량의 파일을 다운로드해야 하기 때문에 고속 인터넷 연결이 필요합니다. 소프트웨어 요구 사항: 이 강의는 Apache Hadoop을 포함한 몇몇 오픈소스 소프트웨어 도구를 사용합니다. 필요한 모든 소프트웨어는 무료로 다운로드 및 설치할 수 있습니다(인터넷 제공업체에 내는 데이터 사용료는 제외). 소프트웨어 요구 사항은 다음과 같습니다. Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ 또는 CentOS 6+ VirtualBox 5+
Teaching Impacts of Technology: Fundamentals
University of California San Diego (via Coursera)
In this course you’ll focus on the fundamentals of teaching the impacts of technology, starting by exploring how you interact with and benefit from technology in a typical 24 hour period, such as the desire for instant food and entertainment. This will be done through a series of paired teaching sections, exploring a specific “Impact of Computing” in your typical day and the “Technologies and Computing Concepts” that enable that impact, all at a K12-appropriate level. This course is part of a larger Specialization through which you’ll learn impacts of computing concepts you need to know, organized into 5 distinct digital "worlds”, as well as learn pedagogical techniques and evaluate lesson plans and resources to utilize in your classroom. By the end, you’ll be prepared to teach pre-college learners to be both savvy and effective participants in their digital world. In this particular digital world (daily life), you’ll explore the following Impacts & Technology pairs -- Impacts (Food Delivery): Apps that bring you food, drivers, and find and recommend businesses Technologies and Computing Concepts: Geolocation, Push Notifications, Near Field Communications, HMTL5, GPS, Graph representations, Minimal Spanning Trees, Shortest Path Algorithms Impacts (Entertainment): Streaming for entertainment and education, Environmental impact of Internet, YouTube culture Technologies and Computing Concepts: Data Centers, Downloading vs Streaming, Digital vs. Analog image representation, basic compression algorithms, Internet metrics (latency, bandwidth) In the pedagogy section for this course, in which best practices for teaching computing concepts are explored, you’ll learn to employ constructivist activities useful in teaching impacts of computing and to evaluate and contribute to an unplugged lesson plan. In terms of CSTA K-12 computer science standards, we’ll primarily cover learning objectives within the “impacts of computing” concept, while also including some within t...
مقدمة عن البيانات الضخمة
University of California San Diego (via Coursera)
مقدمة عن البيانات الضخمة هل أنت مهتم بزيادة معرفتك بأبرز سمات البيانات الضخمة؟ هذه الدورة التدريبية مخصصة للمستجدين في علوم البيانات والمهتمين بفهم أسباب ظهور عصر البيانات الضخمة. فهي مخصصة لمن يريدون الإلمام بالمصطلحات والمفاهيم الأساسية الخاصة بمشكلات البيانات الضخمة وتطبيقاتها وأنظمتها. إنها لمن يريدون البدء في التفكير بشأن الطريقة التي يمكن أن تفيدهم البيانات الضخمة بها في عملهم أو مسيرتهم المهنية. حيث تتعرض مقدمة عن أحد أكثر أطر العمل الشائعة ألا وهو Hadoop، والذي زاد من سهولة تحليل البيانات الضخمة وإمكانية الوصول إليها، فقد زاد من احتمالية تطوير البيانات الضخمة لعالمنا! وفي نهاية الدورة التدريبية، ستتمكن مما يلي: وصف أبرز سمات البيانات الضخمة بما في ذلك الأمثلة على مشكلات البيانات الضخمة على أرض الواقع التي تتضمن ثلاثة مصادر أساسية للبيانات الضخمة وهي الأفراد والمؤسسات وأدوات الاستشعار. شرح خصائص البيانات الضخمة التي تبدأ بالحرف V مثل (volume (الحجم)، وvelocity (السرعة)، وvariety (التنوع)، وveracity (الصحة)، وvalence (التكافؤ)، وvalue (القيمة)) ولماذا تؤثر كل خاصية من تلك الخصائص في جمع البيانات ومتابعتها وتخزينها وتحليلها والإبلاغ عنها الاستفادة بقيمة البيانات الضخمة عن طريق استخدام عملية مكونة من 5 خطوات لهيكلة تحليلك. تحديد المشكلات التي تندرج تحت البيانات الضخمة والتي لا تندرج تحتها، والقدرة على إعادة تشكيل مشكلات البيانات الضخمة مثل مسائل علوم البيانات. تقديم تفسير للمكونات الهندسية والنماذج البرمجية التي تستخدم في التحليل القابل للتوسيع للبيانات الضخمة. تلخيص ميزات المكونات الأساسية لمكدس Hadoop وقيمتها بما في ذلك مورد YARN ونظام إدارة الوظائف، ونظام ملفات HDFS، ونموذج برمجة MapReduce. * تثبيت البرامج وتشغيلها باستخدام إطار عمل Hadoop! هذه الدورة التدريبية موجهة للمستجدين في علوم البيانات. لا يلزم توافر خبرة برمجية مسبقة، على الرغم من ضرورة توافر القدرة على تثبيت التطبيقات واستخدام الأجهزة الظاهرية لإنجاز الواجبات العملية. متطلبات الأجهزة: (أ) معالج رباعي النواة (يوصى بمعالج يدعم ميزة VT-x أو AMD-V)، 64 بت؛ (ب) ذاكرة وصول عشوائي بحجم 8 جيجابايت؛ (ج) مساحة خالية بحجم 20 جيجابايت. طريقة العثور على معلومات الأجهزة: (نظام Windows)...
Mathematical Thinking in Computer Science
University of California San Diego (via Coursera)
Mathematical thinking is crucial in all areas of computer science: algorithms, bioinformatics, computer graphics, data science, machine learning, etc. In this course, we will learn the most important tools used in discrete mathematics: induction, recursion, logic, invariants, examples, optimality. We will use these tools to answer typical programming questions like: How can we be certain a solution exists? Am I sure my program computes the optimal answer? Do each of these objects meet the given requirements? In the online course, we use a try-this-before-we-explain-everything approach: you will be solving many interactive (and mobile friendly) puzzles that were carefully designed to allow you to invent many of the important ideas and concepts yourself. Prerequisites: 1. We assume only basic math (e.g., we expect you to know what is a square or how to add fractions), common sense and curiosity. 2. Basic programming knowledge is necessary as some quizzes require programming in Python.
Internet of Things: Multimedia Technologies
University of California San Diego (via Coursera)
Content is an eminent example of the features that contributed to the success of wireless Internet. Mobile platforms such as the Snapdragon™ processor have special hardware and software capabilities to make acquisition, processing and rendering of multimedia content efficient and cost-effective. In this course, you will learn the principles of video and audio codecs used for media content in iTunes, Google Play, YouTube, Netflix, etc. You will learn the file formats and codec settings for optimizing quality and media bandwidth and apply them in developing a basic media player application. Learning Goals: After completing this course, you will be able to: 1. Explain the tradeoffs between media quality and bandwidth for content delivery. 2. Extract and display metadata from media files. 3. Implement and demonstrate a simple media player application using DragonBoard™ 410c.
Algae Biotechnology
University of California San Diego (via Coursera)
For decades, researchers have been studying microalgae to understand their biological functions and how we can use technology to harness algae’s power to create a wide range of commercial products. In this course, we will cover how synthetic biology, genetic engineering, and metabolic engineering is used in algae biotechnology, and also examine the current state of algae biotechnology research and tools. We’ll also explore some of the common bio-products we can make from algae, and take a look at some real-world algae companies that are using algae biotechnology to create products consumers can buy today. This course is part of a series of courses produced by the Algae Technology Educational Consortium and UC San Diego with funding from the Algae Foundation, National Renewable Energy Lab, and the U.S. Department of Energy.
Our Energy Future
University of California San Diego (via Coursera)
This course is designed to introduce students to the issues of energy in the 21st century – including food and fuels – which are inseparably linked – and will discuss energy production and utilization from the biology, engineering, economics, climate science, and social science perspectives. This course will cover the current production and utilization of energy, as well as the consequences of this use, examining finite fossil energy reserves, how food and energy are linked, impacts on the environment and climate, and the social and economic impacts of our present energy and food production and use. After the introductory lectures, we will examine the emerging field of sustainable energy, fuel and food production, emphasizing the importance of developing energy efficient and sustainable methods of production, and how these new technologies can contribute to replacing the diminishing supplies of fossil fuels, and reduce the consequences of carbon dioxide release into the environment. This course will also cover the importance of creating a sustainable energy future for all societies including those of the developing world. Lectures will be prepared and delivered by leading UC San Diego and Scripps Institution of Oceanography faculty and industry professionals across these areas of expertise.
Social Computing
University of California San Diego (via Coursera)
People are social creatures and the modern Internet reflects that. Technology has made collaboration at a distance possible in new ways that present their own set of challenges. This course will introduce you to the major challenges and opportunities for creating online communities. What does the future hold? Learn how social computing can create collaboration experiences that go beyond what’s possible face to face.
Algorithms on Graphs
University of California San Diego (via Coursera)
If you have ever used a navigation service to find optimal route and estimate time to destination, you've used algorithms on graphs. Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect a set of computers into a network or efficient algorithm to automatically find communities and opinion leaders in Facebook, you're going to work with graphs and algorithms on graphs. In this online course, you will first learn what a graph is and what are some of the most important properties. Then you'll learn several ways to traverse graphs and how you can do useful things while traversing the graph in some order. We will then talk about shortest paths algorithms — from the basic ones to those which open door for 1000000 times faster algorithms used in Google Maps and other navigational services. You will use these algorithms if you choose to work on our Fast Shortest Routes industrial capstone project. We will finish with minimum spanning trees which are used to plan road, telephone and computer networks and also find applications in clustering and approximate algorithms.
Internet of Things: How did we get here?
University of California San Diego (via Coursera)
It is hard to imagine life without your Smartphone – you have come to rely on it so much – for your work; to stay in touch with family and friends; to capture and share those special moments; to find your way around in a new neighborhood. Did you ever wonder how and when all this happened? Or how and when GPS sensors came to be in your cell phone? In this course, we will explore the convergence of multiple disciplines leading to todays’ Smartphones. You will learn about the birth and evolution of Telephony Networks, Broadcast Networks (TV and Radio) and Consumer Electronics. We will discuss the impact of Internet, (multimedia) content, smartphones and apps on everyday lives. We will then look at how this emerging platform called the Internet of Things – wherein billions and trillions of devices communicating with each other and “the cloud” – could enable unprecedented, innovative products and services. Take this course if you want to understand what great new advances in mobile-enabled products will be coming our way! Learning Goals: This course provides a core grounding in how science and technology have developed to enable the Internet of Things – in a way appropriate for any learner. For those interested in developing further hands-on expertise in designing and developing for the Internet of Things, this course will provide a context to the discoveries and converging technologies that will springboard the next round of innovations. After completing this course, you will be able to: 1. Compare how the telephone system works (that is, peer-to-peer networks) with how media delivery works (that is, broadcast/multicast networks). 2. Explain the tradeoffs between circuit switched networks (that is, dedicated resources) and packet switched networks (that is, shared resources). 3. Tell interesting stories about key innovations that transformed the communications, entertainment and consumer electronics industries. 4. Explain how email, YouTube, SMS, etc. work. 5....
Algorithmic Design and Techniques
University of California, San Diego (via edX)
Algorithmic Design and Techniques
Data Structures Fundamentals
University of California, San Diego (via edX)
Data Structures Fundamentals
Graph Algorithms
University of California, San Diego (via edX)
Graph Algorithms
Statistics and Probability in Data Science using Python
University of California, San Diego (via edX)
Statistics and Probability in Data Science using Python
NP-Complete Problems
University of California, San Diego (via edX)
NP-Complete Problems
String Processing and Pattern Matching Algorithms
University of California, San Diego (via edX)
String Processing and Pattern Matching Algorithms
Dynamic Programming: Applications In Machine Learning and Genomics
University of California, San Diego (via edX)
Dynamic Programming: Applications In Machine Learning and Genomics
Graph Algorithms in Genome Sequencing
University of California, San Diego (via edX)
Graph Algorithms in Genome Sequencing
Algorithms and Data Structures Capstone
University of California, San Diego (via edX)
Algorithms and Data Structures Capstone
Learn to Teach Java: Sequences, Primitive Types and Using Objects
University of California San Diego (via Coursera)
Get started with the basics of Java, and prepare to teach others using the free, online interactive CS Awesome textbook. In this course for teachers we'll guide you both in learning Java concepts and skills but also in how to effectively teach those to your students. This course will support you in teaching the Advanced Placement Computer Science A course or a similar introductory university-level programming course. We'll begin with simple instruction sequences, primitive types, and using objects, as covered in the APCS A Units 1 and 2. Each topic will begin by relating Java to block-based programming languages and then provide video overviews of CS Awesome content along with additional materials to supplement learning for your students. You'll engage with additional materials to support your teaching including "deep dive" classroom discussion questions and assessment overviews and options for your students.
The Cybersecurity Boot Camp at UC San Diego Extended Studies
University of California, San Diego (via edX)
The Cybersecurity Boot Camp at UC San Diego Extended Studies
Computational Thinking for K-12 Educators: Nested If Statements and Compound Conditionals
University of California San Diego (via Coursera)
How could you program a complex "choose your own adventure" game? How can your soccer game determine goals, balls out of bounds, and corner kicks? You'll learn to do both of these in this course! This class teaches the concepts of nested if/else statements and compound Boolean conditional expressions. For each concept, we'll start by helping you connect real-world experiences you are already familiar with to the programming concept you are about to learn. Next, through a cognitively scaffolded process we'll engage you in developing your fluency with problem solving with nested if/else statements and compound conditionals in a way that keeps frustration at a minimum. Along the way you will learn about the common challenges or "bugs" students have with these concepts as well as ways to help them find and fix those concepts. You'll also be guided in running classroom discussions to help students develop deeper understanding of these concepts. Finally, you'll prepare classroom resources to help your students to develop debugging skills. Additionally, you will create resources to help educate your students about the impacts of lack of equity in K-12 CS instruction.
The Data Science and Visualization Boot Camp at UC San Diego Extended Studies
University of California, San Diego (via edX)
The Data Science and Visualization Boot Camp at UC San Diego Extended Studies
The Coding Boot Camp at UC San Diego Extended Studies
University of California, San Diego (via edX)
The Coding Boot Camp at UC San Diego Extended Studies
Analyze Your Genome!
University of California, San Diego (via edX)
Analyze Your Genome!
Delete
University of California, San Diego (via edX)
Delete
So You Want to Be a Biomedical Engineer
University of California, San Diego (via edX)
So You Want to Be a Biomedical Engineer
Designing, Running, and Analyzing Experiments
University of California San Diego (via Coursera)
You may never be sure whether you have an effective user experience until you have tested it with users. In this course, you’ll learn how to design user-centered experiments, how to run such experiments, and how to analyze data from these experiments in order to evaluate and validate user experiences. You will work through real-world examples of experiments from the fields of UX, IxD, and HCI, understanding issues in experiment design and analysis. You will analyze multiple data sets using recipes given to you in the R statistical programming language -- no prior programming experience is assumed or required, but you will be required to read, understand, and modify code snippets provided to you. By the end of the course, you will be able to knowledgeably design, run, and analyze your own experiments that give statistical weight to your designs.
Data Structures: An Active Learning Approach
University of California, San Diego (via edX)
Data Structures: An Active Learning Approach
How Virtual Reality Works
University of California, San Diego (via edX)
How Virtual Reality Works
Input and Interaction
University of California San Diego (via Coursera)
In this course, you will learn relevant fundamentals of human motor performance, perception, and cognition that inform effective interaction design. You will use these models of how people work to design more effective input and interaction techniques. You’ll apply these to both traditional graphic and gestural interfaces.
Computer Graphics
University of California, San Diego (via edX)
Computer Graphics
Capstone: Analyzing (Social) Network Data
University of California San Diego (via Coursera)
In this capstone project we’ll combine all of the skills from all four specialization courses to do something really fun: analyze social networks! The opportunities for learning are practically endless in a social network. Who are the “influential” members of the network? What are the sub-communities in the network? Who is connected to whom, and by how many links? These are just some of the questions you can explore in this project. We will provide you with a real-world data set and some infrastructure for getting started, as well as some warm up tasks and basic project requirements, but then it’ll be up to you where you want to take the project. If you’re running short on ideas, we’ll have several suggested directions that can help get your creativity and imagination going. Finally, to integrate the skills you acquired in course 4 (and to show off your project!) you will be asked to create a video showcase of your final product.
Computer Graphics II: Rendering
University of California, San Diego (via edX)
Computer Graphics II: Rendering teaches physically-based realistic image synthesis or rendering, as used in production and real-time rendering, culminating in a Monte Carlo Path Tracer with Multiple Importance Sampling
Drug Development
University of California San Diego (via Coursera)
The University of California San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences Drug Development course brings you lectures from both faculty and industry experts. With this course, recorded on campus at UCSD, we seek to share our access to top people in the field who bring an unprecedented range of expertise on drug development. In this course you will learn the different stages of clinical development as well as the regulatory including but not limited to, an Investigational New Drug Application (IND), New Drug Application (NDA), and product labeling. Additionally you will learn how to Incorporate study design methods for consideration in the design of clinical protocols to assess safety, tolerability, and efficacy in multiple therapeutic areas. In this course you will learn the different phases of clinical development: Phase 1 or early stage clinical trial are conducted primarily to determine how the new drug works in humans, its safety profile and to predict its dosage range. It typically involves between 30 and 100 healthy volunteers. Phase 2 or Proof of Concept POC studies test for efficacy as well as safety and side effects in a group of between 30 to 200 hundred patients with the disease for which the new drug is being developed. Phase 3 or late stage clinical development involve much larger group of patients, between a few hundred to thousands, depending on the indication, which will help determine if the new drug can be considered both safe and effective. It will involve control groups using placebo and/or current treatment as a comparison. Product registration and approval process after a drug is considered safe and effective from Phase 3 trials, it must be authorized in each individual country before it can be marketed. All data generated about the small molecule or biologic is collected and submitted to the regulatory authorities in the US at the FDA, Food and Drug Administration FDA, in Europe the EMA or E...
Introduction to Genomic Data Science
University of California, San Diego (via edX)
Introduction to Genomic Data Science
Genome Sequencing (Bioinformatics II)
University of California, San Diego (via edX)
Genome Sequencing (Bioinformatics II)
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
University of California, San Diego (via edX)
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
Molecular Evolution (Bioinformatics IV)
University of California, San Diego (via edX)
Molecular Evolution (Bioinformatics IV)
Basic Data Processing and Visualization
University of California San Diego (via Coursera)
This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization. This course will introduce you to the field of data science and prepare you for the next three courses in the Specialization: Design Thinking and Predictive Analytics for Data Products, Meaningful Predictive Modeling, and Deploying Machine Learning Models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
Genomic Data Science and Clustering (Bioinformatics V)
University of California, San Diego (via edX)
Genomic Data Science and Clustering (Bioinformatics V)
Meaningful Predictive Modeling
University of California San Diego (via Coursera)
This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better? By the end of this course you will be familiar with diagnostic techniques that allow you to evaluate and compare classifiers, as well as performance measures that can be used in different regression and classification scenarios. We will also study the training/validation/test pipeline, which can be used to ensure that the models you develop will generalize well to new (or "unseen") data.
Big Data Integration and Processing
University of California San Diego (via Coursera)
At the end of the course, you will be able to: Retrieve data from example database and big data management systems Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications Identify when a big data problem needs data integration Execute simple big data integration and processing on Hadoop and Spark platforms This course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
Finding Mutations in DNA and Proteins (Bioinformatics VI)
University of California, San Diego (via edX)
Finding Mutations in DNA and Proteins (Bioinformatics VI)
Internet of Things V2: DragonBoard™ bring up and community ecosystem
University of California San Diego (via Coursera)
Do you want to develop skills to prototype embedded products using state-of-the-art technologies? In this course you will build a hardware and software development environment to guide your journey through the Internet of Things specialization courses. We will use the DragonBoard™ 410c single board computer (SBC). This is the first in a series of courses where you will learn both the theory and get the hands-on development practice needed to prototype Internet of Things products. This course is suitable for a broad range of learners. This course is for you if: You want to learn how to use learn how to use Linux for embedded purposes. You want to pivot your career towards the design and development of Internet of Things enabled products You are an entrepreneur, innovator or member of a DIY community Learning Goals: After completing this course, you will be able to: 1) Know where you can find resources and help in the 96Boards ecosystem. 2) Describe the DragonBoard™ 410c peripherals, I/O expansion capabilities, Compute (CPU and Graphics) capabilities, and Connectivity capabilities. 3) Understand how to navigate and make use of the Linux terminal. 4) Configure at least one integrated development environment (IDE) for developing software. 5) Make use of Git and GitHub for version control purposes. 6) Create and build projects that interface with sensors and actuators through GPIO and Arduino.
Combinatorics and Probability
University of California San Diego (via Coursera)
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this online course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main ‘consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
Bioinformatics Capstone: Big Data in Biology
University of California, San Diego (via edX)
Bioinformatics Capstone: Big Data in Biology
Creating Virtual Reality (VR) Apps
University of California, San Diego (via edX)
Creating Virtual Reality (VR) Apps
Algorithmic Toolbox
University of California, San Diego (via edX)
Algorithmic Toolbox
Machine Learning Fundamentals
University of California, San Diego (via edX)
Machine Learning Fundamentals
Algal Blooms and Proliferations
University of California San Diego (via Coursera)
This course is intended to provide information to the general public who may be curious after seeing a TV clip or read a news article about harmful algal bloom. It will cover 13 of the known toxins associated with harmful algae, their impacts on human and animal health, and how we detect those toxins and their associated algae. Harmful and nuisance algae are a global phenomenon. They are found in marine and freshwater systems. They can impact human and animal health, the food supply, the water supply, and tourism. Lastly, this course will discuss the different techniques used for mitigating the presence of harmful algae, their impact on water bodies and recreation, as well as how they might be in your food. It is intended to follow up our first three courses: Introduction to algae, Algal biotechnology, and Introduction to seaweeds. This course was produced by the Algae Technology Educational Consortium and UC San Diego with funding from the Algae Foundation, the National Renewable Energy Lab, and the U.S. Department of Energy.
Python for Data Science
University of California, San Diego (via edX)
Python for Data Science
Internet of Things V2: Setting up and Using Cloud Services
University of California San Diego (via Coursera)
Have you wondered what exactly AWS is and why is it important? Do you want to make informed design decisions about which services to use? Do you want to gain expertise to leverage the cloud for your own projects? In this course, you will learn to interface with the AWS cloud. You will then develop software to send data to and receive data from the cloud. Along the way, you’ll learn how to structure your project with a variety of these difference services. Learning Goals: After completing this course, you will be able to: 1) Understand what the cloud is and how it works. 2) Install and configure the AWS CLI and SDK on a Linux system. 3) Use various AWS services such as EC2, IoT, and many more. 4) Build projects that heavily leverage the cloud. 5) Integrate the cloud into embedded systems.
Probability and Statistics in Data Science using Python
University of California, San Diego (via edX)
Probability and Statistics in Data Science using Python
Delete-Probability and Statistics in Data Science using Python
University of California, San Diego (via edX)
Delete-Probability and Statistics in Data Science using Python
Hacking COVID-19 — Course 1: Identifying a Deadly Pathogen
University of California San Diego (via Coursera)
In this course, you will follow in the footsteps of the bioinformaticians investigating the COVID-19 outbreak by assembling the SARS-CoV-2 genome. Whether you’re new to the world of computational biology, or you’re a bioinformatics expert seeking to learn about its applications in the COVID-19 pandemic, or somewhere in between, this course is for you! As you go through this journey, we will introduce and explain genomic concepts and give you many opportunities to practice your skills, and we will provide a series of problems with gradually increasing complexity. This first course will only discuss the assembly of the SARS-CoV-2 genome, but future courses in this series will explore follow-up bioinformatics analyses used in the COVID-19 pandemic.
Droit d’asile et des réfugiés
University of California, San Diego (via edX)
Droit d’asile et des réfugiés
Big Data Analytics using Spark
University of California, San Diego (via edX)
Big Data Analytics using Spark
HLS Encoding Course
University of California, San Diego (via edX)
HLS Encoding Course
Learn to Teach Java: ArrayLists and 2D Arrays
University of California San Diego (via Coursera)
Learn to program with ArrayLists and 2-D Arrays in Java, and prepare to teach others using the free, online interactive CS Awesome textbook. In this course for teachers we'll guide you both in learning Java concepts and skills but also in how to effectively teach those to your students. This course will support you in teaching the Advanced Placement Computer Science A course or a similar introductory university-level programming course. We'll cover the Java concepts of ArrayLists and 2-dimensional arrays, as covered in the APCS A Units 7 and 8. Each topic will begin by relating Java to block-based programming languages and then provide video overviews of CS Awesome content along with additional materials to supplement learning for your students. You'll engage with additional materials to support your teaching including "deep dive" classroom discussion questions, assessment overviews, code tracing and problem solving skills for your students, including preparation for free response coding questions.
Teaching Impacts of Technology: Workplace of the Future
University of California San Diego (via Coursera)
In this course you’ll focus on how the Internet has enabled new careers and changed expectations in traditional work settings, creating a new vision for the workplace of the future. This will be done through a series of paired teaching sections, exploring a specific “Impact of Computing” in your typical day and the “Technologies and Computing Concepts” that enable that impact, all at a K12-appropriate level. This course is part of a larger Specialization through which you’ll learn impacts of computing concepts you need to know, organized into 5 distinct digital “worlds”, as well as learn pedagogical techniques and evaluate lesson plans and resources to utilize in your classroom. By the end, you’ll be prepared to teach pre-college learners to be both savvy and effective participants in their digital world. In this particular digital world (careers and work), you’ll explore the following Impacts & Technology pairs -- Impacts (Getting jobs in new ways): technology based freelancing, Linkedin and how it changed the way we work Technology and Computing Concepts: Data retrieval, data vs metadata, SQL, Boolean logic (AND, OR, NOT) Impacts (Physical ties to work restricts people and businesses): work communication, the cloud, cloud computing, companies affected by ransomware attacks Technology and Computing Concepts: how the cloud works, FTP, cloud storage, clients and servers, scalability basics, fault tolerance, AWS, devops Impacts (Advancing your career in the fast moving technical world): digital technology changing jobs, online classes, machines replacing jobs, data science and artificial intelligence In the pedagogy section for this course, in which best practices for teaching computing concepts are explored, you’ll learn how to effectively explore and critique curricular material you find and practice reviewing lesson plans, with a focus on material aimed at learning HTML. In terms of CSTA K-12 computer science standards, we’ll primarily cover learning obj...
Minecraft, Coding and Teaching
University of California, San Diego (via edX)
Minecraft, Coding and Teaching
Bending the Curve: Climate Change Solutions 2
University of California San Diego (via Coursera)
Climate change solutions must be approached from the local, regional, national and international scope. In the second course, we look at California as a case study, and analyze energy issues, power generation, and transportation. We also expand our view and investigate a variety of international efforts.
The Science of Parenting
University of California, San Diego (via edX)
The Science of Parenting
Code Free Data Science
University of California San Diego (via Coursera)
The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements. Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data. Predicting future trends and behaviors allows for proactive, data-driven decisions. During the class learners will acquire new skills to apply predictive algorithms to real data, evaluate, validate and interpret the results without any pre requisites for any kind of programming. Participants will gain the essential skills to design, build, verify and test predictive models. You Will Learn • How to design Data Science workflows without any programming involved • Essential Data Science skills to design, build, test and evaluate predictive models • Data Manipulation, preparation and Classification and clustering methods • Ways to apply Data Science algorithms to real data and evaluate and interpret the results
Communicating with the Public
University of California San Diego (via Coursera)
Rooted in theater, journalism and humanities practices, this course presents tools and techniques that help you improve your public-facing communication skills, particularly when describing your work to a lay audience. Whether it’s a 30-second elevator pitch or speaking to a large organization, “Communicating with the Public” will boost your confidence in any speech-communication scenario.
Internet of Things: Setting Up Your DragonBoard™ Development Platform
University of California San Diego (via Coursera)
Do you want to develop skills to prototype mobile-enabled products using state-of-the-art technologies? In this course you will build a hardware and software development environment to guide your journey through the Internet of Things specialization courses. We will use the DragonBoard™ 410c single board computer (SBC). This is the first in a series of courses where you will learn both the theory and get the hands-on development practice needed to prototype Internet of Things products. This course is suitable for a broad range of learners. This course is for you if: • You want to develop hands-on experience with mobile technologies and the Internet • You want to pivot your career towards the design and development of Internet of Things enabled products • You are an entrepreneur, innovator or member of a DIY community Learning Goals: After completing this course, you will be able to: 1. Configure at least one integrated development environment (IDE) for developing software. 2. Make use of git, adb and fastboot to flash multiple OS and repair bricked boards. 3. Install Android 5.1 (Lollipop) and Linux based on Ubuntu. 4. Create, compile and run a Hello World program. 5. Describe the DragonBoard™ 410c peripherals, I/O expansion capabilities, Compute (CPU and Graphics) capabilities, and Connectivity capabilities.
Introduction to Big Data
University of California San Diego (via Coursera)
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. Get value out of Big Data by using a 5-step process to structure your analysis. Identify what are and what are not big data problems and be able to recast big data problems as data science questions. Provide an explanation of the architectural components and programming models used for scalable big data analysis. Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start but...
Internet of Things Capstone: Build a Mobile Surveillance System
University of California San Diego (via Coursera)
In the Capstone project for the Internet of Things specialization, you will design and build your own system that uses at least 2 sensors, at least 1 communication protocol and at least 1 actuator. You will have a chance to revisit and apply what you have learned in our courses to achieve a robust, practical and/or fun-filled project. We absolutely encourage you to design whatever you can think up! This is your chance to be creative or to explore an idea that you have had. But if you don’t have your own idea, we provide the description of a surveillance system, for you to build. We will participate in the Capstone with you by building a surveillance system that features an off-grid solar powered workstation that will serve as a hub to multiple surveillance sensors. You will be able to demonstrate the knowledge and skills you have gained in this course through delivery of industry-appropriate documents such as System Design documents and Unit Test reports. Additionally, you will be asked to describe and show case your project as a short video presentation – appropriate for demonstrating your knowledge and technical communication skills. Learning Goals: After completing this Capstone, you will be able to: 1. Design systems using mobile platforms. You will gain experience in documenting and presenting designs. 2. Develop systems that interface multiple sensors and actuators to the DragonBoard™ 410c system and develop the necessary software to create a fully functional system. 3. Specify unit tests and system tests, run tests and prepare Test Reports as are commonly done by those working in this industry. 4. Gain experience (and feedback!) in making technical presentations.
Teaching Impacts of Technology: Global Society
University of California San Diego (via Coursera)
In this course you’ll focus on how technology-enabled communication is changing geopolitics and, more broadly, how technology is connecting our world and changing lives. This will be done through a series of paired teaching sections, exploring a specific “Impact of Computing” in your typical day and the “Technologies and Computing Concepts” that enable that impact, all at a K12-appropriate level. This course is part of a larger Specialization through which you’ll learn impacts of computing concepts you need to know, organized into 5 distinct digital “worlds”, as well as learn pedagogical techniques and evaluate lesson plans and resources to utilize in your classroom. By the end, you’ll be prepared to teach pre-college learners to be both savvy and effective participants in their digital world. In this particular digital world (global society), you’ll explore the following Impacts & Technology pairs -- Impacts (Freedom of Speech): Internet in third world countries, censorship, and social media Technology and Computing Concepts: VPN, how Internet censorship works, metadata, tor Impacts (Life Made Easy): Internet changing the way we live, travel, autonomous vehicles Technology and Computing Concepts: Internet of things, how self-driving cars work Impacts (Keeping Your Information Secure): two-factor authentication, PINs, Patterns, fingerprints, apple ID Technology and Computing Concepts: DDoS attacks and Botnets, man-in-the-middle attacks, dangers of public Wifi, phishing, ransomware, bitcoin In the pedagogy section for this course, in which best practices for teaching computing concepts are explored, you’ll learn about the principles of the computer science advanced placement exam, how it assesses students, and how to prepare your students for this critical exam. In terms of CSTA K-12 computer science standards, we’ll primarily cover learning objectives within the “impacts of computing” concept, while also including some within the “networks and the Interne...
Mastering the Software Engineering Interview
University of California San Diego (via Coursera)
You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
Computational Thinking for K-12 Educators: Variables and Nested Loops
University of California San Diego (via Coursera)
How can students learn about abstraction by creating a movie scene? Or make an interactive map using lists? You'll learn (and do it yourself) in this course! This class teaches the concepts of abstraction (methods and parameters) and lists. For each concept, we'll start by helping you connect real-world experiences you are already familiar with to the programming concept you are about to learn. Next, through a cognitively scaffolded process we'll engage you in developing your fluency with problem solving with abstraction and lists in a way that keeps frustration at a minimum. Along the way you will learn about the common challenges or "bugs" students have with these concepts as well as ways to help them find and fix those concepts. You'll also be guided in running classroom discussions to help students develop deeper understanding of these concepts. Finally, you'll learn about the importance and logistics of assigning creative, student-designed programming projects. Additionally, you will create a personal plan for increasing your skills in supporting a culturally responsive learning environment in your classroom.
Drug Discovery
University of California San Diego (via Coursera)
The University of California San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences Drug Discovery course brings you lectures from both faculty and industry experts. With this course, recorded on campus at UCSD, we seek to share our access to top people in the field who bring an unprecedented range of expertise on drug discovery. In this course you will learn the drug discovery process up to the filing of an Initial New Drug Application or IND. Each week you will learn the steps that a pharmaceutical or biotech company goes through to discover a new therapeutic drug. In this course you will be able to: Understand the pharmaceutical and biotechnology market a changing landscape Learn the major aspects of the drug discovery process, starting with target selection, to compound screening to designing lead candidates. Recognize current modern drug discovery based on the lock-and-key theory, which attempts to use one single compound to hit one target to combat the related disease. Increase understanding of the various drug discovery tools and methods that are used for finding, identifying and designing a new drug. * Define and understand the regulatory responsibilities for drug discovery to file an Investigational New Drug Application (IND). This course is intended as part 1 of a series: Drug Discovery, Drug Development (https://www.coursera.org/learn/drug-development) and Drug Commercialization (https://www.coursera.org/learn/drug-commercialization). We would highly recommend that you take the courses in order since it will give you a better understanding on how a drug is discovered in the lab before being tested in clinical trials and then launched in the market place.
Interaction Design Capstone Project
University of California San Diego (via Coursera)
Apply the skills you learned during the sequence of courses -- from needfinding to visual design -- as you redesign a new interface, service, or product for your Interaction Design Capstone Project. We’re working with some exciting design teams in Silicon Valley across multiple industries to develop real-world design challenges for this final project. Upon completion, you will have a polished capstone project you can share in your design portfolio to highlight your work and document your design process.
Hacking COVID-19 — Course 3: Unraveling COVID-19's Origins
University of California San Diego (via Coursera)
In this course, you will follow in the footsteps of the bioinformaticians investigating the COVID-19 outbreak by investigating the origins of SARS-CoV-2. Whether you’re new to the world of computational biology, or you’re a bioinformatics expert seeking to learn about its applications in the COVID-19 pandemic, or somewhere in between, this course is for you! As you go through this journey, we will introduce and explain genomic concepts and give you many opportunities to practice your skills, and we will provide a series of problems with gradually increasing complexity. This third course will only discuss the multiple sequence alignment and maximum-likelihood phylogenetic inference of SARS-CoV-2 genomes, but future courses in this series will explore follow-up bioinformatics analyses used in the COVID-19 pandemic.