Practical and Modern Statistical Thinking For All. Use Python for statistical visualization, inference, and modeling

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Brenda Gunderson +2 more instructors

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Associated with the Master of Applied Data Science degree
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Statistics with Python SpecializationUniversity of Michigan

What you will learn

Create and interpret data visualizations using the Python programming language and associated packages & libraries
Apply and interpret inferential procedures when analyzing real data
Apply statistical modeling techniques to data (ie. linear and logistic regression, linear models, multilevel models, Bayesian inference techniques)
Understand importance of connecting research questions to data analysis methods.

Skills you will gain

About this Specialization

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This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.

Applied Learning Project

The courses in this specialization feature a variety of assignments that will test the learner’s knowledge and ability to apply content through concept checks, written analyses, and Python programming assessments. These assignments are conducted through quizzes, submission of written assignments, and the Jupyter Notebook environment.

Shareable Certificate

Earn a Certificate upon completion

100% online courses

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Flexible Schedule

Set and maintain flexible deadlines.

Beginner Level

Approximately 3 months to complete

Suggested pace of 4 hours/week

English

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Shareable Certificate

Earn a Certificate upon completion

100% online courses

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Flexible Schedule

Set and maintain flexible deadlines.

Beginner Level

Approximately 3 months to complete

Suggested pace of 4 hours/week

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

See how employees at top companies are mastering in-demand skills

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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.

There are 3 Courses in this Specialization

Course1

Course 1

Understanding and Visualizing Data with Python

4.7

stars

2,464 ratings

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

Course 2

Inferential Statistical Analysis with Python

4.6

stars

845 ratings

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

Course 3

Fitting Statistical Models to Data with Python

4.4

stars

641 ratings

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

Offered by

University of Michigan

Start working towards your Master's degree

This specialization is related to the 100% online Master of Applied Data Science from University of Michigan. It will provide you with a preview of the topics, materials and instructors so you can decide if the full online degree program is right for you.

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Frequently Asked Questions

What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Can I just enroll in a single course?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Can I take the course for free?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
Is this course really 100% online? Do I need to attend any classes in person?
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.
How long does it take to complete the Specialization?
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.
What background knowledge is necessary?
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.
Do I need to take the courses in a specific order?
It is definitely recommended to take this specialization in order.
Will I earn university credit for completing the Specialization?
You will not earn University credit for completing this specialization.
What will I be able to do upon completing the 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.

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Statistics with Python Specialization

What you will learn

Skills you will gain

About this Specialization

Applied Learning Project