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 34m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

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.

Testimonials and Success Stories

I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

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You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...


I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

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You are an excellent teacher, and a rare breed.

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I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...


By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.



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 48min
  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 (07:11)
  12. Paired Test Theory (10:12)
  13. Paired Test Code (04:48)
  14. Sign Test Theory (09:55)
  15. Sign Test Code (02:16)


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