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Probability and Statistics for Everyone

Published by Pearson

Beginner to intermediate content levelBeginner to intermediate

Learn Bayesian statistics and A/B testing from the ground up

  • Materials will be presented in an intuitive way, using illustrative examples
  • The focus is on introducing just enough theory to understand practical real-world applications
  • Theoretical concepts are introduced with illustrative practical examples to help clarify them

Probability and Statistics are the bedrock on which the most recent advances in machine learning and Artificial Intelligence have been built. A deep and intuitive understanding of Probability Theory and Statistics and how to apply them to each specific situation is a fundamental requirement for any successful Data Science and Machine Learning project.

In this live training, we will cover in a hands-on and incremental fashion the fundamentals of descriptive statistics, the foundations of probability theory, Bayesian Analysis, and practical applications such as Model Fitting and Hypothesis Testing that are commonly used in practical applications in both industry and academia.

What you’ll learn and how you can apply it

  • Conditional probability and use probability distributions
  • Bayes Theorem
  • Hypothesis testing

And you’ll be able to:

  • Apply Bayesian Statistics to practical problems
  • Perform model fitting
  • Use A/B testing

This live event is for you because...

The typical audience member will be a data scientist interested in mastering the concepts and ideas behind statistics and probability in machine learning and AI contexts. The primary audience will be someone familiar with Python programming but with limited experience in probabilistic models. A secondary target audience is someone with previous contact with this class of approaches who wishes to get a greater understanding of what’s going on “under the hood.” The course will emphasize practical examples and practical applications as well as highlight the difficulties in applying this class of methods.

Prerequisites

  • Basic Python
  • Numpy
  • Matplotlib
  • Jupyter

Course Set-up:

You will need access to the following if you want to follow along with the demos:

Recommended Preparation:

Recommended Follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1 – Descriptive Statistics (40 min)

  • Big Data and the need for statistics
  • Counting
  • Mean and Standard deviation
  • Quantiles
  • Box and whiskers plots
  • Measures of correlation
  • Q&A: 5 minutes

Segment 2 – Fundamentals of Probability (40 min)

  • Understand what probability is
  • Random variables
  • Calculate the probability of different outcomes
  • Sequences of events
  • Sampling
  • Likelihood
  • Model fitting
  • Break: 10 minutes
  • Q&A: 5 minutes

Segment 3 – Probability Distributions (30 min)

  • Uniform distribution
  • Binomial distribution
  • Gaussian distribution
  • Poisson distribution
  • Central Limit Theorem
  • Power-law distribution
  • Relationships between distributions
  • Q&A: 5 minutes

Segment 4- Bayesian Statistics (50 min)

  • Monty-Hall Problem
  • Understand conditional probabilities
  • Derive Bayes Theorem
  • Sequential updates
  • Parameter estimation
  • Break: 5 minutes
  • Q&A: 5 minutes

Segment 5 - A/B Testing (40 min)

  • Understand Hypothesis Testing
  • A/B Testing
  • Measure p-values
  • Compare the likelihood of two outcomes.
  • Statistical Power
  • Q&A: 5 minutes

Your Instructor

Bruno Gonçalves

Bruno Gonçalves is an author, public speaker, corporate trainer, and consultant specializing in Generative AI, Blockchain Analytics, and Machine Learning. He has a diverse background that spans academia and industry, having previously served as a Data Science fellow at NYU's Center for Data Science while on leave from his tenured faculty position at Aix-Marseille Université. Bruno earned his PhD in the Physics of Complex Systems in 2008. He later focused his research on applying Data Science and Machine Learning to the large-scale analysis of online human behavior.

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Skill covered

Statistics