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Overview

When you take this course on Statistics & Probability for Data Science & Machine Learning, you will learn how to become a true data scientist, prove hypotheses and build AI algorithms with the most advanced techniques. This intuitive training will empower you to manipulate information and understand the most complex processes in this fascinating field.          

This complete Data Science tutorial provides an excellent way to absorb the methodology and principles needed to excel in this sector. You will be given expert tuition in using descriptive statistics, calculating probabilities, performing hypothesis testing, working out the coefficient of determination, accomplishing multiple linear regression, and mastering Analysis of Variance (ANOVA).  Gain an acute understanding of these concepts and the capability to excel in the IT commercial industry and secure a promising career path.       

When you complete this training, you will be uniquely able to work in such areas as automobile design, banking service, media forecasting, and much more.

Course design

The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

You are taught through a combination of

  • Video lessons
  • Online study materials

Will I receive a certificate of completion?

Upon successful completion, you will qualify for the UK and internationally-recognised CPD accredited certification. You can choose to make your achievement formal by obtaining your PDF Certificate at the cost of £9 and Hard Copy Certificate for £15.

Why study this course

It doesn’t matter if you are an aspiring professional or absolute beginner; this course will enhance your expertise and boost your CV with critical skills and an accredited certification attesting to your knowledge.

The Statistics & Probability for Data Science & Machine Learning is fully available to anyone, and no previous qualifications are needed to enrol. All One Education needs to know is that you are eager to learn and are over 16.

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? 00:08:00
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
Assignment
Assignment – Statistics & Probability for Data Science & Machine Learning 00:00:00

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