Probability with Python: Essential Math for Data Science
Understand the fundamentals of random events
Topic: Data
Many applied and sciences require strong foundation in probability. Join expert Thomas Nield to solidify your grounding in probability concepts such as important distributions, Bayes's theorem, and conditional expectation. Along the way, you’ll learn how the different probability distributions are related and connected to each other and how to use Python’s stats and probabilityoriented 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:
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What you'll learnand how you can apply it
By the end of this live online course, you’ll understand:
 How probability works and what it means to measure randomness
 How multiple events can affect the probability of another event
 How to use discrete and normal distributions
 When to add and multiply probabilities of different events
And you’ll be able to:
 Recognize situations where Bayes’s theorem applies
 Leverage Python to create continuous and distributions
This training course is for you because...
 You’re a budding data science professional who wants to build foundational knowledge in probability before diving into statistics and machine learning.
 You’re a programmer using data science and machine learning libraries and want to understand the math and probability principles behind them.
 You want to see what Bayes’s theorem is all about.
Prerequisites
 A computer with Python 3 and NumPy installed
 A working knowledge of Python (e.g., variables, functions, loops, generators, and classes)
 A basic understanding of mathematical functions, logarithms, and exponents
Recommended preparation:
 Take Linear Algebra with Python and Linear Regression with Python (live online training courses with Thomas Nield)
Recommended followup:
 Take Statistics and Hypothesis Testing with Python (live online training course with Thomas Nield)
About your instructor

Thomas Nield is an operations research consultant as well as a writer, conference speaker, and trainer. He enjoys making technical content relatable and relevant to those unfamiliar or intimidated by it. Thomas regularly teaches classes on analytics, machine learning, and mathematical optimization. He’s authored two books, including Getting Started with SQL (O'Reilly) and Learning RxJava (Packt), and has written several popular articles, including “How It Feels to Learn Data Science in 2019” and “Is Deep Learning Already Hitting Its Limitations?”
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Getting started (10 minutes)
 Group discussion: The Monty Hall problem
 Presentation: What is probability?
Understanding probability (10 minutes)
 Presentation: Frequentist and Bayesian probability; odds ratios
 Handson exercise: Want to make a bet?
Adding and multiplying probabilities (15 minutes)
 Presentation: Joint probability (AND); union probability (OR)
 Handson exercises: Rain and joint probability; rain and union probability
Conditional probability (10 minutes)
 Presentation: Conditional probability and colorblindness
 Handson exercise: Rain and conditional probability
Break (10 minutes)
Bayes’s theorem (20 minutes)
 Presentation: Violence and video games
 Handson exercise: Medical testing accuracy
Binomial distribution (10 minutes)
 Presentation: Binomial distribution
 Handson exercise: Airline empty seats
Normal distribution (15 minutes)
 Presentation: Normal distribution; quantile functions
 Handson exercise: Predicting the life expectancy of a phone
Beta distribution (15 minutes)
 Presentation: Beta distribution and probability of probabilities
 Handson exercise: Unfair coin flip
Wrapup and Q&A (5 minutes)
 Presentation: Other distributions (Poisson, exponential)