About this Specialization

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Beginner Level

High school-level algebra

Approx. 2 months to complete

Suggested 9 hours/week

English

Subtitles: English...

What you will learn

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    Create and interpret data visualizations using the Python programming language and associated packages & libraries

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    Apply and interpret inferential procedures when analyzing real data

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    Apply statistical modeling techniques to data (ie. linear and logistic regression, linear models, multilevel models, Bayesian inference techniques)

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    Understand importance of connecting research questions to data analysis methods.

Skills you will gain

Python ProgrammingData Visualization (DataViz)Statistical ModelStatistical inference methods

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Beginner Level

High school-level algebra

Approx. 2 months to complete

Suggested 9 hours/week

English

Subtitles: English...

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

how it works

There are 3 Courses in this Specialization

Course1

Understanding and Visualizing Data with Python

4.5
69 ratings
21 reviews
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....
Course2

Inferential Statistical Analysis with Python

3.5
24 ratings
10 reviews
In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately. At the end of each week, learners will apply what they’ve learned using Python within the course environment. During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera....
Course3

Fitting Statistical Models to Data with Python

4.1
12 ratings
6 reviews
In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations. This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python). During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera....

Instructors

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Brenda Gunderson

Lecturer IV and Research Fellow
Department of Statistics
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Brady T. West

Research Associate Professor
Institute for Social Research
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Kerby Shedden

Professor
Department of Statistics

About University of Michigan

The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future....

Frequently Asked Questions

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • This specialization is made up of three courses, each with four weeks/modules. Each week in a course requires a commitment of roughly 3-6 hours, which will vary by learner.

  • High school-level algebra is the only background knowledge mandatory for the first course in the series. A basic Python and/or coding background is recommended.

  • It is definitely recommended to take this specialization in order.

  • You will not earn University credit for completing this specialization.

  • Upon completion of all courses in this specialization, you will have a solid grasp of statistical analysis and will be able to conduct analyses using the Python programming language. You'll be able to create data visualizations in Python, as well as interpret and explain them. You will be able to utilize data for estimation and assessing theories, interpretation of inferential results, and you will be able to apply more advanced statistical modeling procedures.

More questions? Visit the Learner Help Center.