Ball State University
Public University • US
Showing 5 courses from Ball State University
Introduction to Programming
Ball State University (via Coursera)
Designed for the not-yet-experienced programmer, this course will provide you with a structured foundation for developing complex programs in the fields of computer science or data science. If you are a self-taught programmer with scattered bits of understanding, or a complete novice, this is the course for you. Here, you will gain a thorough understanding of how to write programs to solve problems, through structured, scaffolded, hands-on exercises with many examples and opportunities to practice. You will learn the foundational concepts of computer science by developing programs in the python programming language (one of the most commonly used languages). We will also use many of the most common python packages -- why reinvent the wheel when you can use well-tested, flexible, pre-built solutions? While these packages can save significant time, it is also important to understand how they do their magic, and if your particular problem is the right fit to be solved by these potential tools. You will encounter the following python packages: numpy, scipy, matplotlib, pandas, seaborn, re (for regular expressions), textblob, nltk, and others. In the process of learning how to program, we will explore different topics at the introductory level, including natural language processing and data analytics. By the end of this course, you will be confident in your ability to solve a problem using the python programming language -- and how to verify that your solution is accurate.
Introduction to Data Science
Ball State University (via Coursera)
We reside in a world experiencing an explosion of information, with a rapid and exponential growth of data. This surge in data captures increasing interest across various fields. Data science involves the gathering of extensive data and the fusion of domain expertise, programming skills, mathematics, and statistical knowledge to derive meaningful insights. Given the breadth and depth of data science, this course aims to furnish you with a comprehensive theoretical foundation and framework to initiate your journey in this field. "Data" permeates every aspect of data science. The course is divided into five parts, each centered around core topics related to "data". The initial part introduces data ethics, outlining the ethical issues surrounding data collection, usage, and reporting. The second part delves into data collection, acquisition sources, and data structures. The third part focuses on cutting-edge research in Data Science, immersing you in the realm of data science. The fourth part acquaints you with basic data processing using programming, specifically in R, the prevailing data analytics tool. Here, you will gain familiarity with R fundamentals, execute basic data wrangling tasks, develop an understanding of data storage and management, and gain experience in data visualization. The fifth part of the course imparts fundamental knowledge of probability and statistics, preparing you to move to the next stage of exploration.
Cybersecurity and Secure Software Design
Ball State University (via Coursera)
Introduction of basic principles and applications of cybersecurity. Topics include symmetric and asymmetric encryption and decryption algorithms, hashing techniques, digital certificates, digital signatures, message authentication codes, authentication, malware, and security of systems such as networks, operating systems, software, and databases. Upon successfully completing this course, the student should be able to: describe symmetric and asymmetric cryptographic algorithms, demonstrate security application development skills, compare key distribution algorithms, compare authentication techniques, explain transport layer security, and demonstrate proficiency in the security of systems and applications such as email, networking, database, and operating systems.
Data Visualization
Ball State University (via Coursera)
In the era of big data, acquiring the ability to analyze and visually represent “Big Data” in a compelling manner is crucial. Therefore, it is essential for data scientists to develop the skills in producing and critically interpreting digital maps, charts, and graphs. Data visualization is an increasingly important topic in our globalized and digital society. It involves graphically representing data or information, enabling decision-makers across various industries to comprehend complex concepts and processes that may otherwise be challenging to grasp. DSCI 605 Data visualization serves as the foundation for understanding principles, concepts, techniques, and tools used to visualize information in large, intricate data sets. It also provides hands-on experience in visualizing big data using the open-source software R. Through the course, students will learn to evaluate the effectiveness of visualization designs and think critically about decisions, such as color choice and visual encoding. Additionally, students will create their own data visualizations and become proficient in using R. The course comprises four sections. The first section caters to learners with minimal or no experience in R, establishing the groundwork for data visualization with R. The second section introduces preliminary data visualization techniques, allowing students to gain hands-on experience with common visualization practices for Exploratory Data Analysis (EDA) using ggplot2. This section emphasizes data exploration before delving into advanced data mining. The third section builds upon existing data visualization skills by delving into advanced data visualization topics, including interactive data visualization, time series plotting, and spatial mapping. The primary objective of the first three sections is to equip students with a well-developed set of skills, enabling them to create a wide range of visualizations in R. The final section focuses on completing a final project, whe...
Statistical Methods for Data Science
Ball State University (via Coursera)
Welcome to the Ball State University course “Statistical Methods for Data Science.” As the title suggests, this course provides fundamental concepts and methods for data-generating mechanisms such as probability models and inferential methods such as estimation and hypothesis testing. scientists. You will need the right tools and analytics methods to make good sense of data and to make data-driven decisions. We are going to take a systematic approach to build a strong foundation on probability and probability models, large sample theory as a bridge between probability theory and inference, and basic inferential processes. Please note that as data scientists, it is important for us to be able to connect data and learn how the world around us works. To accomplish this challenging task, we will learn how we can connect data through probability theory and statistical models and take actionable decisions, confirm a hypothesis, or make predictions. After completing the course, you will be able to: 1) Apply probability and distribution theory to address real-world problems related to the data science field. 2) Classify the type of random variables and their probability distributions used to model various types of data in practice. 3) Outline the properties of discrete and continuous random variables. 4) Explain the sampling distributions of sample statistics such as the sample mean and the sample proportion. 5) Explain the Laws for Large numbers for the sample mean and the sample proportion. 6) Choose and use appropriate inference strategies, such as the right estimation method or the hypothesis test, to make inferences on unknown population parameters. 7) Illustrate the estimation process and hypothesis testing as a mode of statistical inference. 8) Outline multivariate discrete and continuous distributions to understand the joint behavior of several correlated discrete and continuous variables, respectively. 9) Relate multivariate analysis techniques to dimension re...