Statistics & Probability for Data Science & Machine Learning

Course Overview Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no …

Statistics & Probability for Data Science & Machine Learning

Statistics & Probability for Data Science & Machine Learning




1 Year Access


0 Students


11 hours, 26 minutes

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Course Overview

Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no further; you’ve come to the right place!

Our easy-to-follow Statistics & Probability for Data Science & Machine Learning will provide you with all the deep knowledge and insight you need to know about this topic. This comprehensive course has been broken down into several manageable modules, which we believe will assist you to easily grasp each concept – from the fundamental to the more advanced aspects of the course. 

Learn the most in-demand skills from the safety & comfort of your home. Enjoy the freedom to learn at your own comfortable pace and prepare yourself for the market of the future.

Learning Outcomes

Whether you are an aspiring professional or a complete beginner, this course will improve your expertise and boost your CV with key skills and an accredited certification attesting to your knowledge. 

Entry Requirement

    • This course is available to all learners of all academic backgrounds.
    • Learners should be aged 16 or over to undertake the qualification.
    • Some basic understanding of the English language and numeracy.  


After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can, however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £29 or in PDF format at the cost of £19.

    • PDF certificate’s turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days

Why choose us?

      • Affordable, engaging & high-quality e-learning study materials;
      • Tutorial videos/materials from the industry-leading experts;
      • Study in a user-friendly, advanced online learning platform;
      • Efficient exam systems for the assessment and instant result;
      • The UK & internationally recognised accredited qualification;
      • Access to course content on mobile, tablet or desktop from anywhere, anytime;
      • The benefit of career advancement opportunities;
      • 24/7 student support via email.

Career path

Develop Your Career Plan is a useful qualification to possess and would be beneficial for any related profession or industry. 

Course Curriculum

Section 01: Let's Get Started
Welcome! 00:02:00
What will you learn in this course? 00:06:00
How can you get the most out of it? 00:06:00
Section 02: Descriptive Statistics
Intro 00:03:00
Mean 00:06:00
Median 00:05:00
Mode 00:04:00
Mean or Median? 00:07:00
Skewness 00:08:00
Practice: Skewness 00:01:00
Solution: Skewness 00:03:00
Range & IQR 00:10:00
Sample vs. Population 00:05:00
Variance & Standard deviation 00:11:00
Impact of Scaling & Shifting 00:19:00
Statistical moments 00:06:00
Section 03: Distributions
What is a distribution? 00:10:00
Normal distribution 00:09:00
Z-Scores 00:13:00
Practice: Normal distribution 00:04:00
Solution: Normal distribution 00:07:00
Section 04: Probability Theory
Intro 00:01:00
Probability Basics 00:10:00
Calculating simple Probabilities 00:05:00
Practice: Simple Probabilities 00:01:00
Quick solution: Simple Probabilities 00:01:00
Detailed solution: Simple Probabilities 00:06:00
Rule of addition 00:13:00
Practice: Rule of addition 00:02:00
Quick solution: Rule of addition 00:01:00
Detailed solution: Rule of addition 00:07:00
Rule of multiplication 00:11:00
Practice: Rule of multiplication 00:01:00
Solution: Rule of multiplication 00:03:00
Bayes Theorem 00:10:00
Bayes Theorem – Practical example 00:07:00
Expected value 00:11:00
Practice: Expected value 00:01:00
Solution: Expected value 00:03:00
Law of Large Numbers 00:08:00
Central Limit Theorem – Theory 00:10:00
Central Limit Theorem – Intuition 00:08:00
Central Limit Theorem – Challenge 00:11:00
Central Limit Theorem – Exercise 00:02:00
Central Limit Theorem – Solution 00:14:00
Binomial distribution 00:16:00
Poisson distribution 00:17:00
Real life problems 00:15:00
Section 05: Hypothesis Testing
Intro 00:01:00
What is a hypothesis? 00:19:00
Significance level and p-value 00:06:00
Type I and Type II errors 00:05:00
Confidence intervals and margin of error 00:15:00
Excursion: Calculating sample size & power 00:11:00
Performing the hypothesis test 00:20:00
Practice: Hypothesis test 00:01:00
Solution: Hypothesis test 00:06:00
T-test and t-distribution 00:13:00
Proportion testing 00:10:00
Important p-z pairs 00:08:00
Section 06: Regressions
Intro 00:02:00
Linear Regression 00:11:00
Correlation coefficient 00:10:00
Practice: Correlation 00:02:00
Solution: Correlation 00:08:00
Practice: Linear Regression 00:01:00
Solution: Linear Regression 00:07:00
Residual, MSE & MAE 00:08:00
Practice: MSE & MAE 00:01:00
Solution: MSE & MAE 00:03:00
Coefficient of determination 00:12:00
Root Mean Square Error 00:06:00
Practice: RMSE 00:01:00
Solution: RMSE 00:02:00
Section 07: Advanced Regression & Machine Learning Algorithms
Multiple Linear Regression 00:16:00
Overfitting 00:05:00
Polynomial Regression 00:13:00
Logistic Regression 00:09:00
Decision Trees 00:21:00
Regression Trees 00:14:00
Random Forests 00:13:00
Dealing with missing data 00:10:00
Section 08: ANOVA (Analysis Of Variance)
ANOVA – Basics & Assumptions 00:06:00
One-way ANOVA 00:12:00
F-Distribution 00:10:00
Two-way ANOVA – Sum of Squares 00:16:00
Two-way ANOVA – F-ratio & conclusions 00:11:00
Section 09: Wrap Up
Wrap up 00:01:00

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