Understanding and Visualizing Data with Python

Enrollment Options

What You'll Learn

Properly identify various data types and understand the different uses for each
Create data visualizations and numerical summaries with Python
Communicate statistical ideas clearly and concisely to a broad audience
Identify appropriate analytic techniques for probability and non-probability samples

4 Modules

20 Hours

5 hrs per module (approx.)

Rating

About Understanding and Visualizing Data with Python

In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling.

At the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment. During these lab-based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera.

What You'll Earn

Certificate of Completion: Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.

Experience Type: 100% Online
Format: Self-Paced
Series: Statistics with Python
Subject: Data Science
Platform: Coursera

Welcome Message

Welcome to Understanding and Visualizing Data with Python, a capstone course part of the Python for Everybody series that brings together the full range of skills developed through learning Python for data work. In this course, you will retrieve, process, analyze, and visualize real-world data using Python 3. You will begin with guided visualizations and progress to designing your own data-driven project, building practical experience in data analysis and visualization workflows.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.

Course Schedule

Module 1: Introduction to Data

Video: Welcome to the Course!
Video: Understanding and Visualizing Data Guidelines
Reading: Syllabus
Reading: Meet the Course Team!
Reading: About Our Datasets
Reading: Help Us Learn More About You!
Video: What is Statistics?
Video: Interview: Perspectives on Statistics in Real Life
Reading: Resource: This is Statistics
Video: (Cool Stuff in) Data
Reading: Let's Play with Data!
Discussion Prompt: Discussion: Three Guiding Questions
Video: Where Do Data Come From?
Video: Variable Types
Graded Assignment: Practice Quiz - Variable Types
Video: Study Design
Reading: Data management and manipulation
Ungraded Lab: Introduction to Jupyter Notebooks
Video: Optional: Introduction to Jupyter Notebooks
Ungraded Lab: Data Types in Python
Video: Optional: Data Types in Python
Ungraded Lab: Introduction to Libraries and Data Management
Video: Optional: Introduction to Libraries and Data Management
Ungraded Lab: Continued Data Basics
Ungraded Lab: Deeper Dive into Data Management & Python Resources
Graded: Assessment: Different Data Types

Module 2: Univariate Data

Video: Categorical Data: Tables, Bar Charts & Pie Charts
Video: Quantitative Data: Histograms
Video: Quantitative Data: Numerical Summaries
Video: Standard Score (Empirical Rule)
Video: Quantitative Data: Boxplots
Video: Demo: Interactive Histogram & Boxplot
Graded Assignment: Practice Quiz: Summarizing Graphs in Words
Discussion Prompt: What is There? What isn't There?
Reading: What's Going on in This Graph?
Reading: Modern Infographics
Ungraded Lab: Python Libraries and an Introduction to Graphing
Ungraded Lab: Tables, Histograms, and Boxplots in Python
Reading: Optional: Link to a Graphics Gallery
Ungraded Lab: Case Study of Univariate Data Analyses using NHANES Data
Ungraded Lab: More Practice: Univariate Analysis Using NHANES
Ungraded Lab: More Practice: Univariate Analysis Using NHANES (Solutions)
Ungraded Lab: Univariate Analysis: Assessment Notebook
Graded: Assessment: Numerical Summaries
Graded: Python Assessment: Univariate Analysis

Module 3: Multivariate Data

Video: Looking at Associations with Multivariate Categorical Data
Video: Looking at Associations with Multivariate Quantitative Data
Graded Assignment: Practice Quiz: Multivariate Data
Video: Demo: Interactive Scatterplot
Reading: Pitfall: Simpson's Paradox
Discussion Prompt: Discussion: Find Your Own Example
Reading: Modern Ways to Visualize Data
Video: Introduction to Pizza Assignment
Ungraded Lab: Multivariate Data Selection
Ungraded Lab: Multivariate Distributions
Ungraded Lab: Unit Testing
Ungraded Lab: Case Study of Multivariate Analyses in NHANES
Ungraded Lab: More Practice: Multivariate Analyses with NHANES
Ungraded Lab: Multivariate Analysis: Assessment Notebook
Graded: Pizza Study Design Assignment
Graded: Python Assessment: Multivariate Analysis

Module 4: Populations and Samples

Reading: Building on Visualization Concepts
Video: Sampling from Well-Defined Populations
Video: Probability Sampling: Part I
Reading: More on SRS Probabilities of Inclusion
Video: Probability Sampling: Part II
Video: Non-Probability Sampling: Part I
Video: Non-Probability Sampling: Part II
Reading: Potential Pitfalls of Non-Probability Sampling: A Case Study
Video: Sampling Variance & Sampling Distributions: Part I
Reading: Cluster Sampling and Design Effects
Video: Sampling Variance & Sampling Distributions: Part II
Video: Demo: Interactive Sampling Distribution
Video: Beyond Means: Sampling Distributions of Other Common Statistics
Video: Making Population Inference Based on Only One Sample
Reading: Resource: Seeing Theory
Reading: Article: Jerzy Neyman on Population Inference
Reading: Preventing Bad/Biased Samples
Video: Inference for Non-Probability Samples
Video: Complex Samples
Reading: Optional: Deeper Dive Reference
Ungraded Lab: Sampling from a Biased Population
Ungraded Lab: Randomness and Reproducibility
Ungraded Lab: The Empirical Rule of Distribution
Ungraded Lab: Illustrating sampling distributions using NHANES
Reading: Course Feedback
Reading: Keep Learning with Michigan Online
Graded: Assessment: Distinguishing Between Probability & Non-Probability Samples
Graded: Generating Random Data and Samples

Grading Policy

This course includes quizzes, written peer-reviewed work, and programming assessments completed in a Jupyter Notebook environment. Some assessments may not be mobile-friendly. All assignments are worth between 10% and 20% of your final grade.

Brenda Gunderson

Lecturer IV Emerita in Statistics, College of Literature, Science, and the Arts

Kerby Shedden

Associate Chair, Department of Statistics and Professor of Statistics, College of Literature, Science, and the Arts

Brady West

Collegiate Research Professor, Faculty Associate, Population Studies Center, Research Professor, Survey Research Center, Institute for Social Research, Adjunct Lecturer in Quantitative Methods and Social Sciences Program, College of Literature, Science, and the Arts and Research Professor, Biostatis

Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.

Beginner Level

High school algebra

Individuals

This experience is available to individual learners on the following platforms:

U-M Community

Free access is only available to current U-M students, alumni, faculty, and staff.

Organizations

Special pricing and tailored programming available for organizational partners.

4.7

2,159 Ratings from Coursera

Most Recent Reviews

Read all reviews

July 29, 2026

<co-content><text>really helpful</text></co-content>

June 29, 2026

<co-content><text>I have completed 98% of the course but I am failing questions 2 and 3 regarding BPXSY2. I have tried calculating the mean and std after dropping NaN values, but it is not accepting my answers. Could you please check if there is an issue with the dataset filtering for my profile?"</text></co-content>

May 06, 2026

<co-content><text>best course</text></co-content>

November 08, 2024

Muy buen curso, que ofrece la oportunidad de aprender a analizar datos con Python.

November 06, 2024

It's excellent course

April 28, 2024

It was irrelevant and contained unnecessary content. Why are we drowning in theoretical statistical topics instead of focusing on Python? Thus far, the course has been more about statistics than actually working with Python! I am here to address my statistical needs using Python, not to become an expert in statistics. Unfortunately, this course seems to be doing just the opposite.

March 04, 2024

Good statistics content, but it is not interactive and the testing is weak. Python learning is extremely unforgivable. There are no step-by-step videos, and no theory explanation either, which makes understanding python syntax and functions (particularly in the context of data science) extremely difficult. As someone with an advanced java background, I expected the python learning to be smooth. Unfortunately, I was thrown into the deep end with no life jacket, as the course went from basic variables to creating scatterplots and manipulating datasets in less than a day. This wouldn't be as bad if there were video instructions, but there are none. The "interactive labs" are not interactive, but rather, are just vague notes that don't truly teach or test you on anything. After completing week 2, I left with nothing other than 5 hours of wasted time.

December 26, 2023

The courses is supposed to help students learn how to use Python to understand and visualize data. However, the course lacks focus on the subject as well as tasks for practicing Python code. Lack of practice. The peer-reviewed tasks are hilarious - you will be asked to describe how you'd visualize metrics in (Python you would think? No!) words. This is so easy to turn this task into something actually useful: create a notebook with preloaded data and ask students to come up with metrics and visualize them. No-one came here to practice English writing skills, and this shows in the tasks of the students. The quizzes are easy, the final quiz has all answers in hints which are not even hidden. That's actually a pretty good representation of the course creators' confidence in the students' knowledge after the course - we know you didn't learn anything, so we will just give you all the answers. Concentration on the course goal. The course is too short for trying to pack all the information in it. The last week was interesting, but if I wanted to learn about study design, I'd take a course on Study design. A lot of topics can be described as 'Understanding and Visualizing Data', and the difference between a well-designed course with thought-through structure and this course is that the good course is focused around the narrow subject (e.g. using Python for understanding the data) and delves as deep as possible instead of throw in different topics that are related to 'understanding data' in such a short course. And one last thing I would like to bring up is the students teaching in the course. I understand that it was probably the project they got credits for, and the professors thought that it's be a great practice for them. This is a great initiative, but the Coursera students actually pay for this course, and, I am sorry, but the students lectures were bad for the most part - the explanations are not coherent, the repetitions, the 'we are not going discuss that' (then please structure the lection the way the you don't use the function you don't want to explain). While it's understandable that students need more practice in teaching (they are students after all), the question arises as to why one should pay to listen to their 'end-of-the-course project'.

March 24, 2022

The course material seemed a bit scattered, possibly because of there being at least five presenters. The material wasn't really that focused on data visualization and veered into esoteric (but interesting) topics like non-probability sampling. The pizza memorandum assignment seemed quite pointless. More work with Python labs would have been my preferrence.

September 30, 2021

The title of the course is a bit misleading. The focus is really on some basic Statistics, with Python notebooks thrown in to demonstrate some of those concepts. However, you won't get much help understanding Python. Even the workbooks involved use some interesting methods/libraries, but not much detail in the course about them, other than the particular use they come up in. It's a 4 week course, but can easily be completed in about a week, possibly less. If you already have a fair foundation in Stats, this course probably won't add much value. I did enjoy the instructors and they were trying to keep things interesting.

Understanding and Visualizing Data with Python

What You'll Learn