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Inferential Statistical Analysis with Python

Approx. 19 hours to complete

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Course Summary

Learn how to perform inferential statistical analysis using Python. This course is designed for individuals who are interested in analyzing data using statistical methods.

Key Learning Points

Gain a deep understanding of statistical inference and hypothesis testing
Learn how to use Python to perform statistical analysis
Apply statistical methods to real-world data sets

Learning Outcomes

Ability to perform inferential statistical analysis using Python
Understanding of statistical inference and hypothesis testing
Ability to apply statistical methods to real-world data

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Familiarity with statistical concepts

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

Applied Data Science with Python
Advanced Data Science with Python
Data Science Methodology

Related Education Paths

Notable People in This Field

Nate Silver
Hilary Mason
Andrew Ng

Related Books

Description

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.

Knowledge

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.

Outline

WEEK 1 - OVERVIEW & INFERENCE PROCEDURES
Welcome to the Course!
Inferential Statistical Analysis with Python Guidelines
Introduction to Inference Methods: Oh the Things You Will See!
Bag A or Bag B?
Introduction to Bayesian
This or That? Language and Notation
The Python Statistics Landscape
Intermediate Python Concepts: Lists vs Numpy Arrays
Functions and Lambda Functions, Reading Help Files
Course Syllabus
Meet the Course Team!
About Our Datasets
Help Us Learn More About You!
This or That Reference
Python Basics Assessment

WEEK 2 - CONFIDENCE INTERVALS
Estimating a Population Proportion with Confidence
Understanding Confidence Intervals
Demo: Seeing Theory
Assumptions for a Single Population Proportion Confidence Interval
Conservative Approach & Sample Size Consideration
Estimating a Difference in Population Proportions with Confidence
Interpretations & Assumptions for Two Population Proportion Intervals
Estimating a Population Mean with Confidence
Estimating a Mean Difference for Paired Data
Estimating a Difference in Population Means with Confidence (for Independent Groups)
Introduction to Confidence Intervals in Python
Confidence Intervals for Differences between Population Parameters
Confidence Intervals: Other Considerations
What Affects the Standard Error of an Estimate?
Additional Practice: An Introductory Guide to PDFs and CDFs
Additional Practice: Confidence Intervals
Napping and Non-Napping Toddlers Article for Python Assessment
Practice Quiz: All About Confidence Intervals
Sample Size & Assumptions
Confidence Intervals Assessment

WEEK 3 - HYPOTHESIS TESTING
Setting Up a Test for a Population Proportion
Testing a One Population Proportion
Setting Up a Test of Difference in Population Proportions
Testing a Difference in Population Proportions
Interview: P-Values, P-Hacking and More
One Mean: Testing about a Population Mean with Confidence
Testing a Population Mean Difference
Testing for a Difference in Population Means (for Independent Groups)
Demo: Name That Scenario
Chocolate & Cycling Assignment
Introduction to Hypothesis Testing in Python
Walk-Through: Hypothesis Testing with NHANES
Hypothesis Testing: Other Considerations
The Relationship between Confidence Intervals & Hypothesis Testing
Chocolate & Cycling Assignment Instructions
Additional Practice: Hypothesis Testing
Name That Scenario
Hypothesis Testing in Python Assessment

WEEK 4 - LEARNER APPLICATION
The Importance of Good Research Questions for Sound Inference
Descriptive Inference Examples for Single Variables Using Confidence Intervals
Descriptive Inference Examples for Single Variables Using Hypothesis Testing
Comparing Means for Two Independent Samples: An Example
Comparing Means for Two Paired Samples: An Example
Comparing Proportions for Two Independent Samples: An Example
Assumptions Consistency
Revisiting Examples: Accounting for Complex Samples
Course Feedback
Keep Learning with Michigan Online
Assessment

Summary of User Reviews

Discover the power of inferential statistical analysis with Python in this comprehensive online course. Students rave about the engaging lectures and practical assignments, making it one of the top-rated courses on Coursera.

Key Aspect Users Liked About This Course

Many users found the practical assignments to be a standout feature of the course.

Pros from User Reviews

Engaging lectures that explain complex concepts in an easy-to-understand manner.
Practical assignments that help students apply the concepts they've learned.
Comprehensive coverage of inferential statistical analysis with Python.
Excellent for beginners who want to learn statistical analysis from scratch.
Great support from the course instructors and community.

Cons from User Reviews

Some users found the course to be too basic and lacking in advanced topics.
There were some technical issues reported with the course platform.
The course may be too time-consuming for those with busy schedules.
Some users felt that the assessments were too easy and did not adequately test their knowledge.
The course may not be suitable for those looking for a more theoretical approach to statistical analysis.

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