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.

This course has 3 main parts:

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

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.

- Maximum Likelihood Estimation - Bernoulli (11:42)
- Click-Through Rates (CTR) (02:08)
- Maximum Likelihood Estimation - Gaussian (pt 1) (10:07)
- Maximum Likelihood Estimation - Gaussian (pt 2) (08:40)
- CDFs and Percentiles (09:38)
- Probability Review in Code (10:24)

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

- Hypothesis Testing - Examples (07:15)
- Statistical Significance (05:26)
- Hypothesis Testing - The API Approach (09:17)
- Hypothesis Testing - Accept Or Reject? (02:23)
- Hypothesis Testing - Further Examples (04:59)
- Z-Test Theory (pt 1) (08:47)
- Z-Test Theory (pt 2) (08:30)
- Z-Test Code (pt 1) (13:02)
- Z-Test Code (pt 2) (05:54)
- T-Test Theory (08:52)
- T-Test Code (06:09)
- Paired Test Theory (10:12)
- Paired Test Code (04:48)
- Sign Test Theory (09:55)
- Sign Test Code (02:16)

- Google Colab Notebooks