University of Michigan
Understanding and Visualizing Data with Python
University of Michigan

Understanding and Visualizing Data with Python

This course is part of Statistics with Python Specialization

Taught in English

Some content may not be translated

Brenda Gunderson
Brady T. West
Kerby Shedden

Instructors: Brenda Gunderson

132,808 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.7

(2,593 reviews)

|

95%

Beginner level

Recommended experience

19 hours (approximately)
Flexible schedule
Learn at your own pace

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

Details to know

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Assessments

9 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(2,593 reviews)

|

95%

Beginner level

Recommended experience

19 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Statistics with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

In the first week of the course, we will review a course outline and discover the various concepts and objectives to be mastered in the weeks to come. You will get an introduction to the field of statistics and explore a variety of perspectives the field has to offer. We will identify numerous types of data that exist and observe where they can be found in everyday life. You will delve into basic Python functionality, along with an introduction to Jupyter Notebook. All of the course information on grading, prerequisites, and expectations are on the course syllabus and you can find more information on our Course Resources page.

What's included

11 videos7 readings2 quizzes1 discussion prompt5 ungraded labs

In the second week of this course, we will be looking at graphical and numerical interpretations for one variable (univariate data). In particular, we will be creating and analyzing histograms, box plots, and numerical summaries of our data in order to give a basis of analysis for quantitative data and bar charts and pie charts for categorical data. A few key interpretations will be made about our numerical summaries such as mean, IQR, and standard deviation. An assessment is included at the end of the week concerning numerical summaries and interpretations of these summaries.

What's included

6 videos3 readings3 quizzes1 discussion prompt6 ungraded labs

In the third week of this course on looking at data, we’ll introduce key ideas for examining research questions that require looking at more than one variable. In particular, we will consider both numerically and visually how different variables interact, how summaries can appear deceiving if you don’t properly account for interactions, and differences between quantitative and categorical variables. This week’s assignment will consist of a writing assignment along with reviewing those of your peers.

What's included

4 videos2 readings2 quizzes1 peer review1 discussion prompt6 ungraded labs

In this week, you’ll spend more time thinking about where data come from. The highest-quality statistical analyses of data will always incorporate information about the process used to generate the data, or features of the data collection design. You’ll be exposed to important concepts related to sampling from larger populations, including probability and non-probability sampling, and how we can make inferences about larger populations based on well-designed samples. You’ll also learn about the concept of a sampling distribution, and how estimation of the variance of that distribution plays a critical role in making statements about populations. Finally, you’ll learn about the importance of reading the documentation for a given data set; a key step in looking at data is also looking at the available documentation for that data set, which describes how the data were generated.

What's included

12 videos10 readings2 quizzes4 ungraded labs

Instructors

Instructor ratings
4.7 (561 ratings)
Brenda Gunderson
University of Michigan
3 Courses147,089 learners

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