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Course Overview

Learn how to make a genuine difference in your life by taking our popular Statistics & Probability for Data Science & Machine Learning. Our commitment to online learning and our technical experience has been put to excellent use within the content of these educational modules. By enrolling today, you can take your knowledge of Statistics & Probability for Data Science & Machine Learning to a whole new level and quickly reap the rewards of your study in the field you have chosen.

We are confident that you will find the skills and information that you will need to succeed in this area and excel in the eyes of others. Do not rely on substandard training or half-hearted education. Commit to the best, and we will help you reach your full potential whenever and wherever you need us.

Please note that Statistics & Probability for Data Science & Machine Learning provides valuable and significant theoretical training for all. However, it does not offer official qualifications for professional practice. Always check details with the appropriate authorities or management.

Learning Outcomes

  • Instant access to verified and reliable information
  • Participation in inventive and interactive training exercises
  • Quick assessment and guidance for all subjects
  • CPD accreditation for proof of acquired skills and knowledge
  • Freedom to study in any location and at a pace that suits you
  • Expert support from dedicated tutors committed to online learning

Your Path to Success

By completing the training in Statistics & Probability for Data Science & Machine Learning, you will be able to significantly demonstrate your acquired abilities and knowledge of Statistics & Probability for Data Science & Machine Learning. This can give you an advantage in career progression, job applications, and personal mastery in this area.

Is This Course Right for You?

This course is designed to provide an introduction to Statistics & Probability for Data Science & Machine Learning and offers an excellent way to gain the vital skills and confidence to start a successful career. It also provides access to proven educational knowledge about the subject and will support those wanting to attain personal goals in this area. Full-time and part-time learners are equally supported, and the study periods are entirely customisable to your needs.

Assessment Process

Once you have completed all the modules in the Statistics & Probability for Data Science & Machine Learning course, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.

Show off Your New Skills with a Certification of Completion

The learners have to successfully complete the assessment of this Statistics & Probability for Data Science & Machine Learning course to achieve the CPD & IAO accredited certificate. Digital certificates can be ordered for only £10. Learners can purchase printed hard copies inside the UK for £29, and international students can purchase printed hard copies for £39.

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 – Statistics & Probability for Data Science & Machine Learning 3 weeks, 3 days