The Knowledge Academy

Probability And Statistics For Data Science Training - Hong Kong

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Course Information

  • 11 Nov 2021 (Thu) - 12 Nov 2021 (Fri) 9:00 AM - 5:00 PM
  • 9 Dec 2021 (Thu) - 10 Dec 2021 (Fri) 9:00 AM - 5:00 PM
Registration period
10 Sep 2021 (Fri) - 8 Dec 2021 (Wed)
HKD 18,495
Course Level
Study Mode
2 Day(s)

Course Overview


There are no formal prerequisites for attending this course.


Anyone interested in learning how to apply probability and statistics to data science can attend this course.

Probability and Statistics for Data Science​ Training Course Overview

Probability is the most fundamental skill required to be successful in the business world. This Probability and Statistics for Data Science training course is designed to acquaint delegates with the most fundamental concepts in the field of probability. The course will equip delegates with the knowledge about probability and statistics to tackle the problems related to business and data science.

The Knowledge Academy’s Probability and Statistics for Data Science training is crafted to equip delegates with a comprehensive understanding of complicated probabilistic concepts. This course will take your career to the next level, which is of probability, Bayesian probability, conditional probability, and probability distributions.

During this 2-day course, delegates will learn about discrete and continuous random variables. The course will teach delegates how to generate multivariate random variables. In addition, delegates will gain knowledge gaussian and poisson process. Post completion of this training, delegates will become familiarised with parametric and nonparametric testing.

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




What You’ll Learn

Probability and Statistics for Data Science​ Training Course Outline

The following modules will be covered during this Probability and Statistics for Data Science Course:

Basic Probability Theory

  • Probability Spaces
  • Conditional Probability
  • Independence

Random Variables

  • Definition
  • Discrete Random Variables 
  • Continuous Random Variables
  • Conditioning on an Event
  • Functions of Random Variables
  • Generating Random Variables
  • Proofs

Multivariate Random Variables

  • Discrete Random Variables
  • Continuous Random Variables
  • Joint distributions of Discrete and Continuous Variables
  • Independence
  • Functions of Several Random Variables
  • Creating Multivariate Random Variables
  • Rejection Sampling


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

Random Processes

  • Definition
  • Mean and Autocovariance Functions
  • Independent Identically-Distributed Sequences Gaussian Process
  • Poisson Process
  • Random Walk

The convergence of Random Processes

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

Markov Chains

  • Time-Homogeneous Discrete-Time Markov Chains
  • Recurrence
  • Periodicity
  • Convergence
  • Markov-Chain Monte Carlo

Descriptive Statistics

  • Histogram
  • Sample Mean and Variance
  • Order Statistics
  • Sample Covariance
  • Sample Covariance Matrix

Frequentist Statistics

  • Independent Identically-Distributed Sampling
  • Mean Square Error
  • Consistency
  • Confidence Intervals
  • Nonparametric Model Estimation
  • Parametric Model Estimation
  • Proofs

Bayesian Statistics

  • Bayesian Parametric Models
  • Conjugate Prior
  • Bayesian Estimators

Hypothesis Testing

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

Linear Regression

  • Linear Models
  • Least-Squares Estimation
  • Overfitting

Skills covered in this course

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