• Learn at your own speed with affordable, high-quality E-learning content. 
• When you finish the course, you will be given a certificate of completion.
• Enhance your resume from a globally recognised Accredited Qualification.
• You’ll discover how effective salespeople use a researched and proven technique to boost their careers.
• You’ll be able to successfully implement several practical sales strategies and have a deeper understanding of your consumers.

Statistics & Probability for Data Science & Machine Learning course is designed to be accessible to all. However, the following learners can take the best out of it:

• Those who analyse data and want to improve their statistical and probability knowledge.
• The machine learning engineer whose goal is to improve the fundamental knowledge of statistics needed for modelling.
• Data scientists hope to improve predictive modelling through the use of sophisticated ML and regression algorithms.
• Business analysts hope to use statistical hypothesis testing to enhance decision-making.
• Inferential statistics and ANOVA are widely used by researchers in data analysis.
• Professionals or students making the shift to a data-centric career path.

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

Learners must submit a comprehensive summarised assignment on all the units that will be assessed by our expert tutor if all the learning outcomes are met as per the standard set by QLS. After the quality check, learner will be provided with the “Statistics & Probability for Data Science & Machine Learning” certificate.

An endorsed certificate will be issued to learners at the end of the course as recognition of course completion. Provided that learner completes all the assessment of a course can claim for certificate. 

QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions, including your progression routes into further higher education. It is a non-accredited course.

There are no formal requirements needed for this “Statistics & Probability for Data Science & Machine Learning” course. So learners do not require any prior qualifications to enrol in this course. 

Statistics & Probability for Data Science & Machine Learning skill is required in many careers in the UK, including:

• Data Analyst: £25,000 – £50,000 per year
• Data Scientist: £35,000 – £80,000 per year
• Machine Learning Engineer: £40,000 – £90,000 per year
• Business Analyst (with a focus on data): £30,000 – £60,000 per year
• Statistician: £30,000 – £60,000 per year
• Research Analyst (with statistical skills): £25,000 – £50,000 per year