About this Course
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Intermediate Level

High school algebra, successful completion of Course 1 in this specialization or equivalent background

Approx. 18 hours to complete

Suggested: 4 weeks of study, 4-6 hours/week...

English

Subtitles: English, Korean

What you will learn

  • Check

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

  • Check

    Create confidence intervals in Python and interpret the results.

  • Check

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

  • Check

    Run hypothesis tests in Python and interpret the results.

Skills you will gain

Confidence IntervalPython ProgrammingStatistical InferenceStatistical Hypothesis Testing

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

High school algebra, successful completion of Course 1 in this specialization or equivalent background

Approx. 18 hours to complete

Suggested: 4 weeks of study, 4-6 hours/week...

English

Subtitles: English, Korean

Syllabus - What you will learn from this course

Week
1
3 hours to complete

WEEK 1 - OVERVIEW & INFERENCE PROCEDURES

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....
8 videos (Total 62 min), 5 readings, 1 quiz
8 videos
Introduction to Inference Methods: Oh the Things You Will See!3m
Bag A or Bag B?13m
Introduction to Bayesian4m
This or That? Language and Notation13m
The Python Statistics Landscape2m
Intermediate Python Concepts: Lists vs Numpy Arrays10m
Functions and Lambda Functions, Reading Help Files11m
5 readings
Course Syllabus5m
Meet the Course Team!10m
Help Us Learn More About You!10m
About Our Datasets2m
This or That Reference10m
1 practice exercise
Python Basics Assessment15m
Week
2
6 hours to complete

WEEK 2 - CONFIDENCE INTERVALS

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 and a peer assessment will appear throughout the week to test your understanding. In addition, you’ll learn how to create confidence intervals in Python....
13 videos (Total 121 min), 4 readings, 4 quizzes
13 videos
Understanding Confidence Intervals10m
Demo: Seeing Theory5m
Assumptions for a Single Population Proportion Confidence Interval3m
Conservative Approach & Sample Size Consideration8m
Estimating a Difference in Population Proportions with Confidence6m
Interpretations & Assumptions for Two Population Proportion Intervals4m
Estimating a Population Mean with Confidence14m
Estimating a Mean Difference for Paired Data10m
Estimating a Difference in Population Means with Confidence (for Independent Groups)14m
Chocolate & Cycling Assignment2m
Introduction to Confidence Intervals in Python12m
Confidence Intervals for Differences between Population Parameters21m
4 readings
Confidence Intervals: Other Considerations15m
What Affects the Standard Error of an Estimate?10m
Chocolate & Cycling Assignment Instructions5m
Additional Practice: Confidence Intervals1m
3 practice exercises
Practice Quiz: All About Confidence Intervals14m
Sample Size & Assumptions
Confidence Intervals Assessments
Week
3
5 hours to complete

WEEK 3 - HYPOTHESIS TESTING

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....
11 videos (Total 136 min), 3 readings, 2 quizzes
11 videos
Testing a One Population Proportion8m
Setting Up a Test of Difference in Population Proportions7m
Testing a Difference in Population Proportions8m
Interview: P-Values, P-Hacking and More24m
One Mean: Testing about a Population Mean with Confidence17m
Testing a Population Mean Difference13m
Testing for a Difference in Population Means (for Independent Groups)12m
Demo: Name That Scenario2m
Introduction to Hypothesis Testing in Python20m
Walk-Through: Hypothesis Testing with NHANES13m
3 readings
Hypothesis Testing: Oher Considerations10m
The Relationship between Confidence Intervals & Hypothesis Testing5m
Additional Practice: Hypothesis Testing1m
2 practice exercises
Name That Scenario15m
Hypothesis Testing in Python Assessments
Week
4
4 hours to complete

WEEK 4 - LEARNER APPLICATION

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....
6 videos (Total 77 min), 3 readings, 1 quiz
6 videos
Descriptive Inference Examples for Single Variables Using Hypothesis Testing12m
Descriptive Inference Examples for Single Variables Using Confidence Intervals12m
Comparing Means for Two Independent Samples: An Example14m
Comparing Means for Two Paired Samples: An Example12m
Comparing Proportions for Two Independent Samples: An Example13m
3 readings
Assumptions Consistency5m
Revisiting Examples: Accounting for Complex Samples10m
Course Feedback10m
1 practice exercise
Assessment10m
3.9
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Top Reviews

By RRMar 7th 2019

If you are interested in statistics and statistical analysis, this course gets you grounded in the essential aspects of statistics. Excellent instructors.

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....

About the Statistics with Python Specialization

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....
Statistics with Python

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.