University of Michigan
Statistics with Python Specialization
University of Michigan

Statistics with Python Specialization

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

Taught in English

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Brenda Gunderson
Brady T. West
Kerby Shedden

Instructors: Brenda Gunderson

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Specialization - 3 course series

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4.6

(2,776 reviews)

Beginner level

Recommended experience

1 month at 10 hours a week
Flexible schedule
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Prepare for a degree

What you'll 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.

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Specialization - 3 course series

Get in-depth knowledge of a subject

4.6

(2,776 reviews)

Beginner level

Recommended experience

1 month at 10 hours a week
Flexible schedule
Learn at your own pace
Prepare for a degree

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Specialization - 3 course series

Understanding and Visualizing Data with Python

Course 119 hours4.7 (2,586 ratings)

What you'll learn

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

  • Create data visualizations and numerical summaries with Python

  • Communicate statistical ideas clearly and concisely to a broad audience

  • Identify appropriate analytic techniques for probability and non-probability samples

Skills you'll gain

Category: Statistics
Category: Data Analysis
Category: Python Programming
Category: Data Visualization

Inferential Statistical Analysis with Python

Course 221 hours4.6 (880 ratings)

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.

Skills you'll gain

Category: Confidence Interval
Category: Python Programming
Category: Statistical Inference
Category: Statistical Hypothesis Testing

Fitting Statistical Models to Data with Python

Course 314 hours4.4 (673 ratings)

What you'll learn

  • Deepen your understanding of statistical inference techniques by mastering the art of fitting statistical models to data.

  • Connect research questions with data analysis methods, emphasizing objectives, relationships between variables, and making predictions.

  • Explore various statistical modeling techniques like linear regression, logistic regression, and Bayesian inference using real data sets.

  • Work through hands-on case studies in Python with libraries like Statsmodels, Pandas, and Seaborn in the Jupyter Notebook environment.

Skills you'll gain

Category: Bayesian Statistics
Category: Python Programming
Category: statistical regression
Category: Statistical Model

Instructors

Brenda Gunderson
University of Michigan
3 Courses145,961 learners

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