University of Michigan
Inferential Statistical Analysis with Python
University of Michigan

Inferential Statistical Analysis with Python

Brenda Gunderson
Brady T. West
Kerby Shedden

Instructors: Brenda Gunderson

45,141 already enrolled

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
4.6

(904 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 21 hours
Learn at your own pace
94%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.6

(904 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 21 hours
Learn at your own pace
94%
Most learners liked this course

What you'll learn

  • Determine assumptions needed to calculate confidence intervals for their respective population parameters.

  • Create confidence intervals in Python and interpret the results.

  • Review how inferential procedures are applied and interpreted step by step when analyzing real data.

  • Run hypothesis tests in Python and interpret the results.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments

Taught in English

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

Placeholder

Build your subject-matter expertise

This course is part of the Statistics with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 4 modules in this course

In this first week, we’ll review the course syllabus and discover the various concepts and objectives to be mastered in weeks to come. You’ll be introduced to inference methods and some of the research questions we’ll discuss in the course, as well as an overall framework for making decisions using data, considerations for how you make those decisions, and evaluating errors that you may have made. On the Python side, we’ll review some high level concepts from the first course in this series, Python’s statistics landscape, and walk through intermediate level Python concepts. 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

6 videos7 readings1 assignment1 discussion prompt3 ungraded labs

In this second week, we will learn about estimating population parameters via confidence intervals. You will be introduced to five different types of population parameters, assumptions needed to calculate a confidence interval for each of these five parameters, and how to calculate confidence intervals. Quizzes will appear throughout the week to test your understanding. In addition, you’ll learn how to create confidence intervals in Python.

What's included

10 videos5 readings3 assignments6 ungraded labs

In week three, we’ll learn how to test various hypotheses - using the five different analysis methods covered in the previous week. We’ll discuss the importance of various factors and assumptions with hypothesis testing and learn to interpret our results. We will also review how to distinguish which procedure is appropriate for the research question at hand. Quizzes and a peer assessment will appear throughout the week to test your understanding.

What's included

10 videos2 readings2 assignments1 peer review1 discussion prompt6 ungraded labs

In the final week of this course, we will walk through several examples and case studies that illustrate applications of the inferential procedures discussed in prior weeks. Learners will see examples of well-formulated research questions related to the study designs and data sets that we have discussed thus far, and via both confidence interval estimation and formal hypothesis testing, we will formulate inferential responses to those questions.

What's included

6 videos5 readings1 assignment

Instructors

Instructor ratings
4.7 (142 ratings)
Brenda Gunderson
University of Michigan
3 Courses154,148 learners

Offered by

Recommended if you're interested in Data Analysis

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

Showing 3 of 904

4.6

904 reviews

  • 5 stars

    73.56%

  • 4 stars

    17.80%

  • 3 stars

    5.42%

  • 2 stars

    1.54%

  • 1 star

    1.65%

YP
4

Reviewed on Jul 7, 2019

FJ
5

Reviewed on Jun 21, 2019

AA
5

Reviewed on May 27, 2020

New to Data Analysis? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions