Classical Statistical Inference and A/B Testing in Python

The Most-Used and Practical Data Science Techniques in the Real-World

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Course Data

Lectures: 28
Length: 3h 33m
Skill Level: All Levels
Languages: English
Includes: Lifetime access

Course Description

The premise of this course is quite simple.

I asked myself: What is the most practical data science technique?

What do I use in the real-world most often?

Methods like neural networks and recommender systems are pretty application-specific, but statistical inference can be used for nearly everything.

  • Website owners: which web page design leads to the highest engagement or revenue?
  • Copywriters: which sales copy leads to the highest conversion rate?
  • Online advertising platforms: which ads lead to the highest click-through rate?
  • Clinical researchers and drug testers: which treatments, drugs, or therapies work best?
  • Machine learning engineers and data scientists: which models have the best performance?


This course has 3 main parts:

  • Maximum likelihood estimation review
  • Confidence intervals
  • Hypothesis testing


Each section prepares you for the next section, to yield maximum understanding and intuition.

Lectures

Review

6 Lectures · 52min
  1. Maximum Likelihood Estimation - Bernoulli (11:42)
  2. Click-Through Rates (CTR) (02:08)
  3. Maximum Likelihood Estimation - Gaussian (pt 1) (10:07)
  4. Maximum Likelihood Estimation - Gaussian (pt 2) (08:40)
  5. CDFs and Percentiles (09:38)
  6. Probability Review in Code (10:24)

Confidence Intervals

6 Lectures · 48min
  1. Confidence Intervals (pt 1) - Intuition (05:09)
  2. Confidence Intervals (pt 2) - Beginner Level (04:45)
  3. Confidence Intervals (pt 3) - Intermediate Level (10:25)
  4. Confidence Intervals (pt 4) - Intermediate Level (11:42)
  5. Confidence Intervals (pt 5) - Intermediate Level (10:08)
  6. Confidence Intervals Code (06:32)

Hypothesis Testing

15 Lectures · 01hr 47min
  1. Hypothesis Testing - Examples (07:15)
  2. Statistical Significance (05:26)
  3. Hypothesis Testing - The API Approach (09:17)
  4. Hypothesis Testing - Accept Or Reject? (02:23)
  5. Hypothesis Testing - Further Examples (04:59)
  6. Z-Test Theory (pt 1) (08:47)
  7. Z-Test Theory (pt 2) (08:30)
  8. Z-Test Code (pt 1) (13:02)
  9. Z-Test Code (pt 2) (05:54)
  10. T-Test Theory (08:52)
  11. T-Test Code (06:09)
  12. Paired Test Theory (10:12)
  13. Paired Test Code (04:48)
  14. Sign Test Theory (09:55)
  15. Sign Test Code (02:16)

Extras

  • Google Colab Notebooks
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