Data analysis, pandas, Excel, exploratory data analysis
113 courses available
Showing 50 courses
Udemy
Clean, Transform and Summarize Microsoft Excel Data Quickly with Excel's Power Query. Beginner-friendly Business & Finance course on Udemy with 2 hours of content. Rated 4.8/5 by 4 learners. Price: $19.99.
Udemy
Supercharge your spreadsheet & data management skills! Learn essential Excel functions, data clean-up tricks and more. Beginner-friendly Business & Finance course on Udemy with 1 hour of content. Rated 4.8/5 by 23 learners. Price: $59.99.
Udemy
A step towards freedom in IC design!!. Beginner-friendly UI/UX & Design course on Udemy with 4 hours of content. Rated 4.8/5 by 768 learners. Available for free.
Udemy
Get a kick start on exploratory data analysis using plotly library. Beginner-friendly Data Science & Analytics course on Udemy with 2 hours of content. Rated 4.7/5 by 18 learners. Price: $19.99.
Udemy
Excel Data Analysis w/Pivot Tables. Learn my valuable 4-step system to IMPORT, CLEAN, ENHANCE, & ANALYZE data in Excel. Beginner-friendly Business & Finance course on Udemy with 2 hours of content. Rated 4.7/5 by 344 learners. Price: $109.99.
Udemy
Design 10-bit DAC from scratch using eSim - open source EDA tool for circuit design by FOSSEE IIT Bombay and Sky130 PDKs. Beginner-friendly UI/UX & Design course on Udemy with 2 hours of content. Rated 4.7/5 by 101 learners. Price: $199.99.
Udemy
Clean, Transform and Summarize Microsoft Excel Data Quickly with Excel's Power Query and PivotTable Tools. Beginner-friendly Business & Finance course on Udemy with 6 hours of content. Rated 4.7/5 by 755 learners. Price: $149.99.
Udemy
Learn how to clean, optimize and model data tables to develop effective, insightful and actionable Power BI reports. Advanced-level Data Science & Analytics course on Udemy with 4 hours of content. Rated 4.4/5 by 361 learners. Price: $99.99.
Udemy
JupyterNotebookで学習する機械学習の初歩からKaggleの初歩まで. Beginner-friendly AI & Machine Learning course on Udemy with 12 hours of content. Rated 4.2/5 by 89 learners. Price: $24. Taught in Japanese.
Udemy
Master The Analysis and Transformation techniques done before the ML Project | Ensure Maximum Value for your data. Beginner-friendly Data Science & Analytics course on Udemy with 2 hours of content. Rated 4.2/5 by 135 learners. Price: $39.99.
Udemy
Harness the skills to analyze your data effectively with EDA and R. Beginner-friendly Data Science & Analytics course on Udemy with 5 hours of content. Rated 4.2/5 by 19 learners. Price: $124.99.
Udemy
Learn how to use R to quickly understand and analyze new data and start your data analysis projects with ease. Beginner-friendly Data Science & Analytics course on Udemy with 7 hours of content. Rated 4.1/5 by 15 learners. Price: $49.99.
Udemy
12 short lessons that review the basics of Excel's newest data clean-up tool: Power Query. Beginner-friendly Business & Finance course on Udemy with 1 hour of content. Rated 4.0/5 by 216 learners. Price: $99.99.
Udemy
Listen from CEO/architect himself on Machine learning. Beginner-friendly Data Science & Analytics course on Udemy with 4 hours of content. Rated 3.8/5 by 202 learners. Price: $189.99.
Udemy
I call this 'freedom of EDA tools'. Beginner-friendly UI/UX & Design course on Udemy with 4 hours of content. Rated 3.2/5 by 78 learners. Price: $94.99.
Johns Hopkins University (via Coursera)
This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to…. 1. Describe the basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations Commitment: 1 week of study, 4-6 hours Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD
Coursera
Modern electronic products—from consumer devices to industrial systems—depend on precise schematics, accurate simulations, and manufacturable PCB layouts. As complexity grows, engineers must master a complete EDA workflow to ensure designs function correctly on the first build and meet industry standards. This course delivers practical, production-ready training in OrCAD for schematic capture, PSpice simulation, and PCB layout. Through hands-on labs and guided demonstrations, you’ll create professional schematics, validate circuit behavior with simulation, and design PCB layouts that follow electrical rules, fabrication constraints, and reliability needs. Real engineering scenarios show how early design mistakes lead to costly failures—and how OrCAD’s workflow prevents them. This course is for electronics engineers, PCB designers, students, and hardware developers who want practical experience using OrCAD for schematic design, simulation, and PCB layout. A basic understanding of circuit theory and schematic reading is required. Prior experience with EDA tools is helpful but not necessary. By course completion, you will confidently execute the electronic design process: selecting components, applying ERC checks, analyzing circuits with PSpice, routing layouts, resolving DRC issues, and generating manufacturing outputs. Whether preparing designs for production or building a professional workflow, you’ll gain the expertise needed to move from concept to manufacturable hardware.
Google Cloud
In this beginner-level course, you will learn about the Data Analytics workflow on Google Cloud and the tools you can use to explore, analyze, and visualize data and share your findings with stakeholders. Using a case study along with hands-on labs, lectures, and quizzes/demos, the course will demonstrate how to go from raw datasets to clean data to impactful visualizations and dashboards. Whether you already work with data and want to learn how to be successful on Google Cloud, or you’re looking to progress in your career, this course will help you get started. Almost anyone who performs or uses data analysis in their work can benefit from this course.
Coursera
Picture this: You’re a data scientist working for a non-profit organization responding to a natural disaster. You’ve been tasked with analyzing data from multiple sources—satellite imagery, social media posts, and relief agency reports—to identify the most affected areas and allocate resources efficiently. The problem? The data is massive, unstructured, and needs to be processed in real-time. So, with the help of Generative AI, you automate the analysis, summarize critical insights, and create actionable visualizations in hours—saving precious time and ensuring aid reaches those in need faster. This short course was created to help you tackle challenges like these. You’ll learn how to use Generative AI to streamline exploratory data analysis (EDA), automate repetitive processes, and extract meaningful insights efficiently. Whether you’re managing data during a crisis or optimizing daily workflows, this course equips you with practical tools to work smarter, not harder. By completing this course, you’ll gain the skills to immediately apply Generative AI to your data workflows. Automate time-intensive tasks, critically evaluate AI-generated outputs for accuracy, and implement strategies from real-world case studies to make impactful decisions. By the end of this 3-hour course, you will be able to: Recognize the key capabilities of Generative AI in improving and automating exploratory data analysis (EDA) workflows. Apply Generative AI tools to automate repetitive tasks in EDA, such as summarizing datasets or generating descriptive statistics. Analyze outputs generated by Generative AI for accuracy and relevance to ensure ethical and unbiased use in EDA. Evaluate case studies of Generative AI applications to identify strategies for integrating AI into real-world exploratory data analysis tasks EDA. This course is unique because it integrates practical examples from diverse fields, from disaster response to e-commerce, to illustrate the power of Generative AI...
Johns Hopkins University (via Coursera)
This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to…. 1. Describe the basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations Commitment: 1 week of study, 4-6 hours Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD
Microsoft
Transform raw data into valuable insights using R's powerful tidyverse tools. This beginner-friendly course introduces you to essential data cleaning and manipulation techniques, making complex data tasks approachable and practical. Learn how to clean messy data, handle missing values, and prepare datasets for analysis using Microsoft's development environment and AI assistance. Through hands-on practice, you'll master fundamental data cleaning skills while building confidence in: Organizing and structuring data effectively Handling common data issues Working with different data formats Using AI tools to enhance your workflow Creating reproducible data cleaning processes Each concept is taught step-by-step with extensive examples and guided practice, ensuring you build a strong foundation in data manipulation skills.
Johns Hopkins University (via Coursera)
In this course, you’ll learn to collect and care for the data gathered during your trial and how to prevent mistakes and errors through quality assurance practices. Clinical trials generate an enormous amount of data, so you and your team must plan carefully by choosing the right collection instruments, systems, and measures to protect the integrity of your trial data. You’ll learn how to assemble, clean, and de-identify your datasets. Finally, you’ll learn to find and correct deficiencies through performance monitoring, manage treatment interventions, and implement quality assurance protocols.
Coursera
Do you want to figure out how to make the most of Copilot in Microsoft Excel? Then join us in this 1.5 hour guided project in which you will learn the fundamentals of analyzing sales data using Excel’s AI-powered Microsoft Copilot. You will work on a practical project with a fictional technology company generating and formatting sample sales data, organizing it into clean, actionable tables using Copilot. You will learn how to clean up sales data, identify key sales trends and insights and create compelling data visualizations, leveraging the power of AI and conversational editing. By the end of the course, you'll have a comprehensive understanding of how to use Microsoft Copilot in Excel to streamline data analysis, generate insights, chart data and forecast sales growth. There are no special requirements for this project, a basic knowledge of Microsoft Excel is preferable.
University of Illinois Urbana-Champaign (via Coursera)
This course introduces several tools for processing business data to obtain actionable insight. The most important tool is the mind of the data analyst. Accordingly, in this course, you will explore what it means to have an analytic mindset. You will also practice identifying business problems that can be answered using data analytics. You will then be introduced to various software platforms to extract, transform, and load (ETL) data into tools for conducting exploratory data analytics (EDA). Specifically, you will practice using Python to conduct the ETL and EDA processes. The learning outcomes for this course include: 1. Development of an analytic mindset for approaching business problems. 2. The ability to appraise the value of datasets for addressing business problems using summary statistics and data visualizations. 3. The ability to competently operate business analytic software applications for exploratory data analysis.
University of Colorado Boulder (via Coursera)
Welcome to the second course in the Data Analytics for Business specialization! This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business. You’ll also learn how to summarize and visualize datasets using plots so that you can present your results in a compelling and meaningful way. We will use a practical predictive modeling software, XLMiner, which is a popular Excel plug-in. This course is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed are applied in all functional areas within business organizations including accounting, finance, human resource management, marketing, operations, and strategic planning. The expected prerequisites for this course include a prior working knowledge of Excel, introductory level algebra, and basic statistics.
IBM (via edX)
Exploratory Data Analysis for Machine Learning
Simplilearn
This comprehensive Generative AI in Data Analytics course equips you with the skills to optimize data workflows, automate analysis, and generate actionable insights using AI. Begin by mastering the four types of analytics, descriptive, diagnostic, predictive, and prescriptive, and explore how GenAI enhances each stage. Learn to automate ETL processes, generate synthetic data with tools like ChatGPT-4 and MOSTLY AI, and perform EDA using Julius AI and Tableau Pulse. Progress to building predictive models, forecasting trends, and conducting risk analysis through real-world simulations. Understand performance metrics, address integration challenges, and apply GenAI in practical business scenarios. You should have a basic understanding of data analysis, statistics, and familiarity with tools like Excel, SQL, or BI platforms. By the end of this course, you will be able to: Automate Data: Streamline ETL and generate synthetic data using GenAI Analyze Insights: Perform EDA and visualize data with AI-powered tools Predict Outcomes: Build models and simulate risk for better decisions Apply GenAI: Use GenAI across real-world analytics with measurable impact Ideal for analysts, data professionals, and business leaders advancing data strategy with AI.
Coursera
By the end of this project, you will be fluent in identifying and creating Empathy Maps based on persona for new products and services and in deriving opportunities from your analysis, thus generating positive results for your business venture. This project is designed to engage and harness your visionary and exploratory abilities. You will use proven models in strategy and product development with the Miro platform to explore and analyze customer data combined with the development of Empathy Maps. This is an important step for individuals or companies wanting to explore new products or services. We will practice critically examining the developed persona. You will engage in evaluating, through examples and hands-on practice, making decisions on product orientation.
Udemy
Four graphical techniques you can use to quickly explore your data. Beginner-friendly Data Science & Analytics course on Udemy with 5 hours of content. Price: $34.99.
EDUCBA
This hands-on course guides learners through the complete lifecycle of predictive modeling, using a real-world banking use case to forecast term deposit subscriptions. Learners will begin by defining a business problem, analyzing and interpreting raw data through Exploratory Data Analysis (EDA), and applying data preparation techniques such as imputation and variable selection. The course then progresses to constructing robust models using industry-standard statistical practices, including Information Value (IV) analysis and multicollinearity checks. Learners will evaluate model performance using ranking techniques, decile analysis, KS statistics, AUC, and Lift. They will also enhance model effectiveness through optimization strategies such as monotonic binning and tree-based methods. Finally, the course concludes by validating the models on unseen datasets and deploying them to a simulated production environment. By the end, learners will have gained the skills necessary to confidently design, develop, and deliver predictive models that solve real-world business challenges.
Udemy
Ingest ,clean, transform and model data | Design and create reports for data analysis | Create paginated reports. Beginner-friendly Business & Finance course on Udemy with 10 hours of content. Price: $169.99.
Coursera
Welcome to Building a Machine Learning Solution, where you'll journey through the complete lifecycle of a machine learning project. This capstone course covers critical steps from problem definition to deployment and maintenance. You'll learn to define clear problem statements, collect and preprocess data, perform exploratory data analysis (EDA), and engineer features to enhance model performance. The course guides you in selecting and implementing appropriate models, comparing classical machine learning, deep learning, and generative AI approaches. Emphasizing real-world considerations, you'll address scalability, interpretability, and ethical implications. You'll gain hands-on experience with tools like scikit-learn, TensorFlow, PyTorch, and more, ensuring you can deploy and monitor models effectively. By the end of this course, you'll be equipped to build end-to-end ML solutions that transform data into actionable insights, making informed decisions at each stage of development.
Coursera
Creating clear maps quickly is a critical skill when decisions need to be made fast. In this short, hands-on course, learners build confidence using QGIS to create clear, professional zoning maps for meetings, reports, and presentations. Through guided videos, focused readings, and hands-on labs, learners practice navigating the QGIS interface, loading vector and raster data, applying graduated color styling, and designing clean map layouts. The course emphasizes visual clarity for non-technical audiences, helping learners make thoughtful styling and layout decisions that improve understanding. Designed for beginners with no prior GIS experience, Map Fast in QGIS equips learners to produce council-ready PDF maps that communicate zoning information clearly and support real-world decision-making.
National Taiwan University (via Coursera)
本課程為三模組系列課程之第一模組。 此系列課程專門教授各種線性迴歸形式 (regression-type) 的統計分析模型與技術。此系列課程以大量多樣之商管實務案例進行解說，並以 R 統計語言進行建模分析，提供完整的命令稿 (script) 與資料集讓同學們實地操作演練，培養統計分析思維與實戰技能。 本第一模組是築基課程，講述統計分析與迴歸模型的基本觀念，包括統計思維 (statistical thinking)、探索型資料分析 (EDA, Exploratory Data Analysis)、迴歸模型的原理與前提條件、參數估計與假設檢定、模型評估與詮釋、預測與應用、變數轉換與選擇、離群值與建模陷阱等。
Coursera
In this project, you’ll help a leading healthcare organization build a model to predict the likelihood of a patient suffering a stroke. The model could help improve a patient’s outcomes. Working with a real-world dataset, you’ll use R to load, clean, process, and analyze the data and then train multiple classification models to determine the best one for making accurate predictions. Upon completion, you’ll produce a well-validated prediction model that showcases your ability to perform a complete data analysis project involving feature engineering, handling missing data, model evaluation, model selection, and model deployment. There isn’t just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers. ROLE: Data Analyst SKILLS: R, Data Analysis, Predictive Modeling PREREQUISITES: Load, clean, explore, manipulate, and visualize data in R, Use R to build a prediction model Use R documentations and vignettes to write new codes
Udemy
This Spotle advanced bootcamp by industry and academic leaders is for people who want to build careers in data science. Advanced-level Data Science & Analytics course on Udemy with 3 hours of content. Price: $2.
University of California, Davis (via Coursera)
In this course, you’ll learn how to create Tableau dashboards that connect data to decision-making. Starting with stakeholder planning and data requirements, you’ll define goals, success metrics, and key questions. Then, you’ll clean and prepare a real-world tech salary dataset using Tableau’s filtering, aliasing, and data type tools. You’ll turn these insights into impactful dashboards using calculated fields, grouping, and interactivity. Finally, you’ll explore how to interpret and present dashboards effectively through Socratic dialogue—critiquing design choices, analyzing KPIs, and refining your data story. By the end, you’ll be able to deliver dashboards that are insightful, persuasive, and tailored to audience needs.
Coursera
Analyze, Engineer, and Boost AI ROI is an intermediate course designed to help learners turn exploratory analysis and model performance results into decisions that increase business impact. You’ll begin by learning how to interpret Exploratory Data Analysis (EDA) patterns, compare demographic segments, and identify opportunities for feature engineering using statistical tests like chi-square. Then, you’ll explore how to evaluate model outcomes through A/B testing, connecting performance shifts to real ROI. Through hands-on practice, reflective coaching, and a guided Coursera Lab, you’ll learn to diagnose patterns, engineer meaningful features, analyze experiment results, and summarize model impact in clear business terms. By the end of the course, you’ll be prepared to influence product and data science decisions with analytical rigor and stakeholder-ready insights.
Meta
This course introduces you to how to use spreadsheets and SQL queries to analyze and extract data. You will learn how to practically apply the OSEMN data analysis framework and spreadsheet functions to clean data, calculate summary statistics, evaluate correlations, and more. You’ll also dive into common data visualization techniques and learn how to use dashboards to tell a story with your data. By the end of this course you will be able to: • Clean data with spreadsheets • Use common spreadsheet formulas to calculate summary statistics • Identify data trends and patterns • Write foundational SQL statements and queries to extract data in spreadsheets • Create charts in Google Sheets and use Tableau to visualize data • Use dashboards to create data visualizations You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally you have already completed course 1: Marketing Analytics Foundation and course 2: Introduction to Data Analytics in this program.
Duke University (via Coursera)
Build confidence working with messy, real-world data. In this course, you’ll learn how to import, clean, and organize data in R so that it’s ready for analysis, visualization, or modeling. Using dplyr, tidyr, and other Tidyverse tools, you’ll practice joining datasets, reshaping data, and creating efficient data pipelines that support reproducible work. You’ll also explore how to responsibly collect and scrape data from online sources, including ethical and legal considerations. By the end of this course, you’ll know how to transform raw datasets into structured, tidy formats and you’ll understand how responsible data handling and documentation are essential to high-quality, ethical data science.
Microsoft
Discover the power of data visualization and analysis in this beginner-friendly course. Learn how to turn raw data into meaningful insights using R's visualization tools and Microsoft's development environment. Through hands-on practice, you'll learn to create engaging visualizations and understand basic statistical patterns, all while using AI assistance to enhance your learning.
Coursera
in 2006, the British mathematician Clive Humby coined the phrase "Data is the new Oil". This analogy has been proven correct as data powers entire industries nowadays but if left unrefined, is effectively worthless. This 2.5 hours-long guided project is designed for business analysts & data engineers eager to learn how to Clean Messy Data in Snowflake Data Platform. By the end of the project, you will -Be able to identify common data quality issues then use SQL String functions to remove unwanted characters and split rows into multiple columns. -Extract dates from Text fields then use SQL date functions for comparisons and calculations. -Identify and correct missing and duplicated data then answer business questions using SQL statements. To achieve these objectives, we will work on a real example from the field, you will play the role of a Data Analyst in the marketing department, who has been tasked with answering a business question, but the customer data they have received presents several data quality challenges. Note: To be successful in this project you need to have Snowflake beginner knowledge such as Creating a trial account, Databases, Tables, and Virtual Warehouses. If you are not familiar with Snowflake and want to learn the basics, start with my previous Guided Project: Snowflake for Beginners: Make your First Snowsight Dashboard which will give you basic knowledge about Snowflake and will teach you how to create your trial account.
Tableau Learning Partner (via Coursera)
The Data Analysis with Tableau Course will teach you how to manipulate and prepare data for analysis and reporting. You will also learn how to use the analytics features in Tableau to more effectively calculate analytics versus manual calculations. In this course, you will perform exploratory data analysis as well as create reports using descriptive statistics and visualizations. This course is for anyone who is curious about entry-level roles that demand fundamental Tableau skills, such as business intelligence analyst or data reporting analyst roles. It is recommended (but not required) that you have some experience with Tableau Public, but even if you're new to Tableau Public, you can still be successful in this program. By the end of the course, you will be able to: -Apply Tableau Public techniques to manipulate and prepare data for analysis. -Perform exploratory data analysis using Tableau and report insights using descriptive statistics and visualizations. -Identify the benefits of the analytics feature in Tableau by utilizing this tool versus manually calculating the analytics.
Coursera
Transform your spreadsheet skills into a competitive advantage in today's data-driven accounting landscape. This course empowers accounting assistants to master the advanced functions that turn chaotic data exports into clean, actionable insights. By completing this course, you'll confidently handle complex data transformations, create sophisticated financial analyses, and deliver professional-quality reports that inform critical business decisions. These skills directly translate to improved efficiency, accuracy, and value-add capabilities you can apply immediately in your accounting role. By the end of this course, you will be able to:- Apply advanced spreadsheet functions to transform raw data into analysis-ready formats- Analyze transactional datasets to identify spending patterns and budget variances This course is unique because it bridges the gap between basic spreadsheet use and professional financial analysis, focusing specifically on real-world accounting scenarios and enterprise-level data challenges. To be successful in this course, you should have basic spreadsheet experience and familiarity with financial data concepts.
Microsoft
This course forms part of the Microsoft Power BI Analyst Professional Certificate. This Professional Certificate consists of a series of courses that offers a good starting point for a career in data analysis using Microsoft Power BI. In this course, you will learn the process of Extract, Transform and Load or ETL. You will identify how to collect data from and configure multiple sources in Power BI and prepare and clean data using Power Query. You’ll also have the opportunity to inspect and analyze ingested data to ensure data integrity. After completing this course, you’ll be able to: • Identify, explain and configure multiple data sources in Power BI • Clean and transform data using Power Query • Inspect and analyze ingested data to ensure data integrity This is also a great way to prepare for the Microsoft PL-300 exam. By passing the PL-300 exam, you’ll earn the Microsoft Power BI Data Analyst certification.
EDUCBA
By the end of this course, learners will be able to analyze HR attrition data, evaluate key workforce factors, apply statistical techniques, select significant features, and build a predictive attrition model using R. This course provides a practical, end-to-end approach to HR analytics with a strong focus on employee attrition. Learners begin by preparing and validating real-world HR data, followed by in-depth exploratory data analysis to understand workforce demographics, job-related factors, and attrition patterns. The course then progresses to statistical analysis using correlation and Chi-Square tests, helping learners identify meaningful relationships between employee attributes and attrition outcomes. What makes this course unique is its structured, project-driven methodology that mirrors real HR analytics workflows. Learners apply Information Value (IV) techniques for feature selection, create a final modeling dataset, and build an attrition prediction model in R, concluding with performance evaluation on unseen data. By completing this course, learners gain hands-on experience in HR data analysis, develop job-ready analytical thinking, and build confidence in using R for data-driven HR decision-making, making it ideal for aspiring data analysts, HR professionals, and analytics learners seeking practical industry skills.
University of California, Davis (via Coursera)
In this course, you will analyze and apply essential design principles to your Tableau visualizations. This course assumes you understand the tools within Tableau and have some knowledge of the fundamental concepts of data visualization. You will define and examine the similarities and differences of exploratory and explanatory analysis as well as begin to ask the right questions about what’s needed in a visualization. You will assess how data and design work together, including how to choose the appropriate visual representation for your data, and the difference between effective and ineffective visuals. You will apply effective best practice design principles to your data visualizations and be able to illustrate examples of strategic use of contrast to highlight important elements. You will evaluate pre-attentive attributes and why they are important in visualizations. You will exam the importance of using the "right" amount of color and in the right place and be able to apply design principles to de-clutter your data visualization.
University of Colorado Boulder (via Coursera)
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
Coursera
Rarely do analysts begin working with a dataset without cleansing it first. Having clean data will allow for the highest quality of information for strategic decision-making. Data cleaning is also a vital part of the data analytics process. Data Cleaning in Excel: Techniques to Clean Messy Data, is for a beginner audience with basic computing skills, typing, and using Excel web. In this 90-minute Guided Project, you will explore the principles of tidy data, apply built-in Excel features to clean data, and use Excel functions to perform text manipulation. To achieve this, we will clean up untidy data set of student data containing names, registration numbers, addresses, marks for three courses, averages, total, and grades. This project is unique because you will learn by doing through step-by-step instruction using a real-world scenario to equip you with foundational data analysis skills that are useful for reporting data. In order to be successful in this project, prerequisites include basic computing skills, familiarity with Windows, files and folders, and basic typing.
Google Cloud
In this beginner-level course, you will learn about the Data Analytics workflow on Google Cloud and the tools you can use to explore, analyze, and visualize data and share your findings with stakeholders. Using a case study along with hands-on labs, lectures, and quizzes/demos, the course will demonstrate how to go from raw datasets to clean data to impactful visualizations and dashboards. Whether you already work with data and want to learn how to be successful on Google Cloud, or you’re looking to progress in your career, this course will help you get started. Almost anyone who performs or uses data analysis in their work can benefit from this course.