Probability with Python: Essential Math for Data Science
Take control of your data by honing your fundamental math skills
Topic: Data
Machine learning requires strong foundation in probability. In this course, we solidify that groundwork by reviewing probability concepts such as important distributions, Bayes' Rule, and conditional expectation. We will also discuss how the different probability distributions are related and connected to each other. In addition, we will be learning how to use Python’s stats/probability oriented libraries.
This is the third course in a fourpart series focused on essential math topics. These courses are grouped in pairs with this natural progression:
and
What you'll learnand how you can apply it
By the end of this live, handson, online course, you’ll understand:
 The difference between discrete and continuous random variables
 The difference between common used probability distributions and how they are related
 The Central Limit Theorem
 Bayes’ theorem
And you’ll be able to:
 Choose an appropriate probability distribution for a process you are modeling
 Apply Bayes’ theorem
 Use Python libraries to generate probability distribution to sample from
This training course is for you because...
 You are someone in a technical role but are looking for foundational knowledge to transition into a data scientist position
 You are someone who is looking to apply data driven decision making in your position
 You work with data and want to generate insights and analysis with that data
 You want to become a data analyst or data scientist
Prerequisites
 Prerequisites What prior knowledge or experience is necessary?
 Basic statistics
 Basic Python: variable creation, conditional statements, functions, loops
Recommended preparation:
 Take Linear Algebra with Python (live online training course)
 Take Linear Regression with Python (live online training course)
Recommended followup:
 Take Statistics and Hypothesis Testing with Python (live online training course)
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 handson courses in data science as well as businessoriented 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 codebased visual art and design projects.
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Introduction and Getting Started (5 minutes)
 Introduction to Jupyter Notebook environment
Introduction to Probability and Random Variables (5 minutes)
 Lecture: What is a random variable?
Statistics of Random Variables I (10 minutes)
 Lecture: Discrete Random Variables
Statistics of Random Variables II (10 minutes)
 Lecture: Continuous Random Variables
Statistics of Random Variables III (10 minutes)
 Lecture: Quantile Functions
Modeling observations with random variables (5 minutes)
 Lecture: How to choose your distribution
 Q&A and Discussion (10 minutes)
 Break (5 minutes)
Bernoulli Trials and Binomial Distribution (10 minutes)
 Lecture: Independence and conditional Expectations
 Lecture: Binomial Random Variables
 Exercise: Adjust distribution parameters to see their effect
Geometric Distribution (10 minutes)
 Lecture: Memoryless Property
 Exercise: Adjust distribution parameters to see their effect
Poisson Distribution (5 minutes)
 Exercise: Adjust distribution parameters to see their effect
Exponential Distribution (5 minutes)
 Exercise: Adjust distribution parameters to see their effect
Normal Distribution (10 minutes)
 Lecture: Central Limit Distribution
 Exercise: Adjust distribution parameters to see their effect
Beta Distribution and Bayes Theorem (15 minutes)
 Lecture: The Beta Distribution
 Exercise: Adjust distribution parameters to see their effect
 Lecture: Bayes Theorem
 Exercise: Adjusting distribution based on new data
Q&A and Discussion (5 minutes)