About this Course
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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

High school algebra

Approx. 22 hours to complete

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

English

Subtitles: English, Korean
User
Learners taking this Course are
  • Data Scientists
  • Data Analysts
  • Biostatisticians
  • Process Analysts
  • Data Engineers

What you will learn

  • Check

    Properly identify various data types and understand the different uses for each

  • Check

    Create data visualizations and numerical summaries with Python

  • Check

    Communicate statistical ideas clearly and concisely to a broad audience

  • Check

    Identify appropriate analytic techniques for probability and non-probability samples

Skills you will gain

StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)
User
Learners taking this Course are
  • Data Scientists
  • Data Analysts
  • Biostatisticians
  • Process Analysts
  • Data Engineers

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

High school algebra

Approx. 22 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
4 hours to complete

WEEK 1 - INTRODUCTION TO DATA

10 videos (Total 110 min), 8 readings, 2 quizzes
10 videos
What is Statistics?9m
Interview: Perspectives on Statistics in Real Life28m
(Cool Stuff in) Data8m
Where Do Data Come From?12m
Variable Types5m
Study Design6m
Introduction to Jupyter Notebooks9m
Data Types in Python12m
Introduction to Libraries and Data Management13m
8 readings
Course Syllabus5m
Meet the Course Team!10m
About Our Datasets2m
Help Us Learn More About You!10m
Resource: This is Statistics10m
Course Syllabus5m
Let's Play with Data!10m
Data management and manipulation10m
2 practice exercises
Practice Quiz - Variable Types10m
Assessment: Different Data Types10m
Week
2
5 hours to complete

WEEK 2 - UNIVARIATE DATA

8 videos (Total 92 min), 2 readings, 3 quizzes
8 videos
Quantitative Data: Histograms12m
Quantitative Data: Numerical Summaries9m
Standard Score (Empirical Rule)7m
Quantitative Data: Boxplots6m
Demo: Interactive Histogram & Boxplot4m
Important Python Libraries21m
Tables, Histograms, Boxplots in Python25m
2 readings
What's Going on in This Graph?10m
Modern Infographics10m
3 practice exercises
Practice Quiz: Summarizing Graphs in Words15m
Assessment: Numerical Summaries10m
Python Assessment: Univariate Analysis10m
Week
3
5 hours to complete

WEEK 3 - MULTIVARIATE DATA

7 videos (Total 56 min), 3 readings, 3 quizzes
7 videos
Looking at Associations with Multivariate Quantitative Data7m
Demo: Interactive Scatterplot2m
Introduction to Pizza Assignment2m
Multivariate Data Selection19m
Multivariate Distributions8m
Unit Testing5m
3 readings
Pitfall: Simpson's Paradox10m
Modern Ways to Visualize Data10m
Pizza Study Design Assignment Instructions10m
2 practice exercises
Practice Quiz: Multivariate Data10m
Python Assessment: Multivariate Analysis15m
Week
4
6 hours to complete

WEEK 4 - POPULATIONS AND SAMPLES

15 videos (Total 223 min), 6 readings, 2 quizzes
15 videos
Probability Sampling: Part I10m
Probability Sampling: Part II15m
Non-Probability Sampling: Part I10m
Non-Probability Sampling: Part II9m
Sampling Variance & Sampling Distributions: Part I15m
Sampling Variance & Sampling Distributions: Part II7m
Demo: Interactive Sampling Distribution21m
Beyond Means: Sampling Distributions of Other Common Statistics10m
Making Population Inference Based on Only One Sample14m
Inference for Non-Probability Samples17m
Complex Samples23m
Sampling from a Biased Population15m
Randomness and Reproducibility14m
The Empirical Rule of Distribution18m
6 readings
Building on Visualization Concepts5m
Potential Pitfalls of Non-Probability Sampling: A Case Study10m
Resource: Seeing Theory10m
Article: Jerzy Neyman on Population Inference10m
Preventing Bad/Biased Samples10m
Course Feedback10m
2 practice exercises
Assessment: Distinguishing Between Probability & Non-Probability Samples10m
Generating Random Data and Samples20m
4.7
56 ReviewsChevron Right

50%

got a tangible career benefit from this course

Top reviews from Understanding and Visualizing Data with Python

By FGApr 4th 2019

Excellent introductory course to statistics. Great use of NHANES dataset to demonstrate techniques on real dataset. I would appreciate a more demanding project at the course end.

By JSJan 24th 2019

I strongly recommend this course to those who want to begin python programming applied to statistics. It launches a very sound foundation for statistical inference theory.

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.