Probability And Statistics For Data Science Training - United Kingdom

Probability and Statistics for Data Science​ Training Course Outline

Day 1: Probability and Random Variables 
Module 1: Basic Probability Theory 

• Probability Spaces
• Conditional Probability 
• Independence 

Module 2: Random Variables 

• Random Variables Intro 
• Discrete Random Variables 
• Continuous Random Variables 
• Conditioning on Events 
• Functions of Random Variables 
• Generating Random Variables 

Module 3: Multivariate Random Variables 

• Introduction 
• Discrete & Continuous Multivariate Variables 
• Joint Distributions 
• Independence 
• Functions of Several Variables 
• Generating Multivariate Variables & Rejection Sampling 

Module 4: Expectation 

• Expectation Operator, Mean and Variance 
• Covariance, Conditional Expectation 

Module 5: Random Processes 

• Intro, Mean, and Autocovariance Functions 
• IID
• Gaussian Process
• Poisson Process
• Random Walk 

Day 2: Statistics and Applications (8 hours total)
Module 6: Convergence of Random Processes 

• Types of Convergence 
• Law of Large Numbers 
• Central Limit Theorem
• Monte Carlo Simulation 

Module 7: Markov Chains 

• Markov Property and Basic Concepts 
• Recurrence
• Periodicity
• Convergence 
• Introduction to Markov-Chain Monte Carlo (MCMC) 

Module 8: Descriptive Statistics 

• What are Descriptive Statistics? 
• Examples and Types of Descriptive Statistics 

Module 9: Frequentist Statistics 

• Mean Square Error
• Consistency 
• Confidence Intervals 
• Parametric vs Nonparametric Model Estimation 
• Maximum Likelihood Estimation (MLE) 

Module 10: Bayesian Statistics 

• Bayesian Parametric Models 
• Conjugate Priors 
• Bayesian Estimators 

Module 11: Hypothesis Testing 

• Hypothesis-Testing Framework 
• Parametric Testing 
• Nonparametric Testing: The Permutation Test 
• Multiple Testing Correction 

Module 12: Linear Regression 

• Introduction to Linear Regression 
• Linear Models & Applications 
• Least-Squares Estimation 

Who should attend this Probability and Statistics for Data Science Training?

This Probability and Statistics for Data Science Course is designed to provide foundational and practical knowledge in Probability and Statistics, which are crucial for Data Science, Machine Learning, and Data Analysis. The following are some professionals who will benefit from attending this course:

• Data Scientists
• Machine Learning Engineers
• Data Analysts
• Business Analysts
• Product Managers
• Quantitative Analysts
• Statisticians

Prerequisites of the Probability and Statistics for Data Science Training

There are no formal prerequisites for this course.

Probability and Statistics for Data Science Training Course Overview

Probability and statistics form the foundational pillars of Data Science, providing the necessary tools for understanding uncertainty, variability, and making informed decisions based on data. This training course delves into the fundamental concepts of probability and statistics, emphasising their crucial role in the field of Data Science. Delegates will explore how these concepts contribute to the extraction of meaningful insights and patterns from data.

Understanding Probability and Statistics is essential for professionals in the Data Science domain. Data Scientists, Analysts, and decision-makers rely on these principles to draw accurate conclusions and predictions from data. Mastery of probability allows for the quantification of uncertainty, while statistics enables the analysis of data patterns and trends.

This 2-day training offered by The Knowledge Academy will empower the delegates with the skills to apply probability and statistics in practical data science scenarios. They will learn key concepts such as probability distributions, hypothesis testing, and regression analysis. The course provides a comprehensive understanding of statistical methods, enabling professionals to make informed decisions and predictions based on data

Course Objectives

• To represent and analyse uncertain phenomena using a framework
• To quantify the outcome of the experiment as belonging to a specific event
• To assign probabilities to each occurrence of interest and an experiment
• To become accustomed to Markov chains and different statistical types
• To generate samples from the appropriate conditional distribution
• To evaluate the occurrence of a particular event that influences another event

Upon completion of this Data Science Training, delegates will possess a strong foundation in Probability and Statistics for Data Science. They will be equipped with the tools and techniques needed to analyse data effectively, make informed decisions, and contribute meaningfully to data-driven projects within their organisations.

What’s included in this Probability and Statistics for Data Science Training?

• World-Class Training Sessions from Experienced Instructors 
• Probability and Statistics for Data Science Certificate 
• Digital Delegate Pack