Take a Look at a UW Data Science Course: Exploratory Data Analysis

“What are the UW Data Science master’s program courses like?”

We often receive this question from prospective students. This post gives you an inside look at DS701: Exploratory Data Analysis, a foundational course in the UW Data Science master’s curriculum, and answers some of the questions that may be on your mind.

What will I learn in the course?

DS 701 introduces exploratory data analysis with R. The course focuses on the early stages of an analysis workflow: understanding a dataset, preparing it for use, exploring patterns, building visualizations, and explaining the results.

Students build R skills throughout the course, but those skills are taught as tools for data exploration rather than as a separate programming course. The course also introduces statistical interpretation and simple modeling so students can better explain the patterns they find, while the central emphasis remains on data cleaning, visualization, exploration, and communication with R.

At the end of this course, you’ll be able to:

• Explain the role of exploratory data analysis in data science and decision-making.
• Use RStudio, R packages, and R Markdown files to complete reproducible analysis work.
• Import, read, and write data in common formats using R.
• Clean, tidy, join, and manage datasets using tidyverse tools such as dplyr.
• Use summary statistics and visual exploration to identify patterns, outliers, relationships, and missing values.
• Create effective visualizations with ggformula and explain what those visuals show.
• Apply introductory statistical ideas, including p-values and confidence intervals, to interpret exploratory findings.
• Use simple linear and logistic regression as introductory tools for modeling and interpretation.
• Communicate an EDA workflow from question to data cleaning, exploration, visualization, and final interpretation.

The lesson sequence is designed to build step by step from basic R use to a complete exploratory analysis. The course begins with RStudio and reproducible work, moves into importing and cleaning data, and then builds toward visualization, pattern identification, and introductory modeling and interpretation.

• Lesson 1: Introduction to Data Science and the Role of Exploratory Data Analysis
• Lesson 2: Getting Started in RStudio, Packages, and R Markdown
• Lesson 3: R Basics and Debugging for Data Analysis
• Lesson 4: Importing, Reading, and Writing Data
• Lesson 5: Cleaning and Tidying Data
• Lesson 6: Exploring Data with dplyr
• Lesson 7: Visualizing Patterns with ggformula
• Lesson 8: Joining, Managing, and Preparing Datasets
• Lesson 9: Supporting Programming Skills: Functions, Control Flow, and Reproducible Code
• Lesson 10: Introductory Statistical Interpretation: p-values, confidence intervals, and simple
  regression
• Lesson 11: Final EDA Project: Cleaning, Exploring, Visualizing, and Interpreting Data

What are the lectures like?

After you enroll in DS701: Exploratory Data Analysis, you can log in to Canvas, the learning management system, to access all course content. The lectures are hosted in the Storybook+ media player and include slides, animations, videos, and instructor narration. You can listen to and replay lectures as many times as you wish.

Some lessons include interviews with real-life data scientists about their work and the current industry landscape. Other videos show the professor demonstrating practical skills students use in the course, such as debugging R code used to clean, inspect, or visualize data.

What types of assignments do I complete?

The course includes homework, quizzes, discussion prompts, and a final project. Assignments are designed to build from smaller R practice tasks into a complete EDA workflow. Early work focuses on RStudio, packages, R Markdown, and data import. Later work asks you to clean and explore data, create visualizations, interpret patterns, and explain your findings.

What else do I do in the course?

Learning materials: The professor selects readings, textbook excerpts, articles, or videos that connect to each lesson’s applied data analysis focus.

Discussion posts: Graded discussion prompts give you opportunities to learn from peers, explain your thinking, and connect EDA concepts to real data questions.

Quizzes: Lessons include quizzes with approximately 25-30 questions covering course content, readings, videos, and applied R concepts.

What technology do I use in this data science course?

You use RStudio and Microsoft Office, including Word and PowerPoint. These applications are available through the Virtual Lab desktop platform, which you have access to as a student in the UW Data Science program. The course uses R as the primary programming language, with tidyverse tools such as dplyr for data exploration and management and ggformula for visualization.

Who developed the course?

The course was developed by instructors Dr. Lauren Mauel, Praneet Tiwari, and Dr. Abra Brisbin. Instructors regularly work with an instructional design team, a media team, and an Advisory Board of industry experts to keep the course aligned with workplace needs and current data science practices.

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Do students in the course interact?

Yes. Students interact and share ideas through graded discussions in Canvas. Students can also collaborate and ask questions through Piazza, a web-based forum used for general, non-assessed discussions.

How much do I do in one week?

Generally, you complete one lesson per week. You have seven days to complete the readings, lectures, assignments, and discussion posts. If you take the course during the summer term, the timeline is accelerated.

This master’s curriculum is intensive. Depending on prior experience, some students spend up to 20 hours per week on a single course. The flexible, online format allows you to study whenever works best for your schedule, making it a strong option for students who work full time.

Have questions about DS701: Exploratory Data Analysis, the rest of the curriculum, how to apply, home campuses, and more? Our enrollment advisers can help. Call 608-800-6762 or email learn@uwex.wisconsin.edu.