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Statistics and hypothesis testing with Python: Essential math for data science

Data

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Take control of your data by honing your fundamental math skills

Michael Cullan

Machine learning requires strong statistical foundations. To be effective at machine learning, you need to know the difference between standard error and standard deviation, what the p-value is and why it’s important when rejecting the null hypothesis, and how to calculate the standard error for hypothesis testing.

Get hands-on experience with rigorous statistical analysis: parameter estimation, hypothesis testing, p-values, z-scores, and other core concepts in statistical inference. You’ll practice on real-world datasets using Python's stats-oriented libraries to ask interesting, relevant questions and draw concrete inferences from population data.

This is the fourth course in a four-part series focused on essential math topics. We recommend taking Linear Algebra with Python, Linear Regression with Python, and Probability with Python first.

What you'll learn-and how you can apply it

By the end of this live online course, you’ll understand:

  • How to use the central limit theorem in statistics
  • What hypothesis testing and parameter estimation are
  • How bootstrapping for parameter estimation works

And you’ll be able to:

  • Perform hypothesis testing to determine if a result is statistically significant
  • Calculate confidence intervals to quantify a measurement uncertainty
  • Apply bootstrapping to determine confidence intervals for any general estimator
  • Implement A/B testing

This training course is for you because...

  • You’re in a technical role, but you’re looking to transition into a data scientist or data analyst position.
  • You want to apply data-driven decision making in your position.
  • You work with data and want to generate insights and analysis.

Prerequisites

  • A basic understanding of Python (variable creation, conditional statements, functions, and loops) and statistical values (mean, median, and mode)

Recommended preparation:

Recommended follow-up:

About your instructor

  • Michael holds a master’s degree in statistics and a bachelor’s degree in mathematics. His academic research areas ranged from computational paleobiology, where he developed software for measuring evidence for disparate evolutionary models based on fossil data, to music and AI, where he assisted in modeling musical data for a jazz improvisation robot.

    In his current work, Michael teaches hands-on courses in data science as well as business-oriented topics in managing data science initiatives at the organizational level. Aside from teaching, he leads internal data science projects for Pragmatic Institute in support of the marketing and operations teams. In his free time, he applies his math and programming skills toward creating code-based visual art and design projects.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Introduction to statistics and statistical inference (10 minutes)

  • Lecture: The Jupyter Notebook environment; statistical inference
  • Group discussion: Do union or nonunion construction workers have higher salaries?

Hypothesis testing of the mean (10 minutes)

  • Lecture: Mean estimation
  • Hands-on exercise: Generate income samples for union workers

Standard error of the mean (10 minutes)

  • Lecture: Standard error of the mean
  • Hands-on exercise: Estimate the standard error
  • Group discussion: Increasing sample size

Confidence intervals (10 minutes)

  • Lecture: Confidence intervals
  • Hands-on exercise: Calculate the confidence interval for the unionized salary mean

Hypothesis testing two means (5 minutes)

  • Lecture: Null hypothesis
  • Group discussion: Rejecting the null hypothesis with unknown population means

Estimating variance (5 minutes)

  • Lecture: Unbiased estimator

Students’ t-distribution (15 minutes)

  • Lecture: Large n assumption
  • Q&A
  • Break (5 minutes)

Standard error of proportion and variance (15 minutes)

  • Lecture: Standard error of proportion; the rule of three; the standard error of variance estimate
  • Hands-on exercise: Change variables to interact with SEP figures

Hypothesis testing for counts (10 minutes)

  • Lecture: The chi-squared hypothesis test
  • Group discussion: Where else would you use a chi-squared test?

Bootstrapping (10 minutes)

  • Lecture: Subsampling data

Determining distributions (5 minutes)

  • Lecture: Data matching distribution
  • Hands-on activity: Apply the Kolmogorov-Smirnov test

Wrap-up and Q&A (10 minutes)