Probability And Statistics For Data Science Training - Canada

Prerequisites

There are no formal prerequisites for attending this Probability and Statistics for Data Science​ Training course.

Audience

This training is suitable for anyone who wants to learn how to apply probability and statistics in Data Science.

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Probability and Statistics for Data Science​ Training Course Overview

Probability and Statistics is the mathematical field that includes the gathering, examination, interpretation, and presentation of numerical data, which are concerned with the rules regulating random events. Statistical methods primarily depend on probability theory and make estimates for further analysis. This training assists organisations in applying ideas of randomness, prediction, expected value, and estimation with a more logical and mathematical approach. This training aims to teach probability and statistical best practices for analysing data and deriving meaningful insights from raw and unstructured data. Pursuing this Probability and Statistics for Data Science Training course will allow individuals to handle an investigation of data and advance their careers in Data Science.

In this 2-day Probability and Statistics for Data Science​ Training course, delegates will gain a thorough understanding of applying probability theory and statistics in Data Science. During this training, they will learn about conditional probability to measure in the probability space, which contains the intersection of the sets. They will also learn about the joint distributions of discrete and continuous random variables that define the probability of two events. Our highly skilled tutor with years of teaching experience will conduct this course and help delegates comprehend probability theory.

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

After completing this course, delegates will be able to apply probability theory in data science using random variables. They will also be able to define a valid probability measure on the power set of R and integrate density to obtain the probability of the random variables.

• Delegate pack consisting of course notes and exercises
• Manual
• Experienced Instructor