Statistics with R

TRAINING COURSE

Details Audience and Pre-Requisites Course Outline / Curriculum Schedule Dates and Booking Enquiry

Details

Statistics and data fuel business with information and insights and programming has made working with these stats easier and more powerful. R is a programming language developed for statistical computing to make data analysis accessible and effective.

In this course we will use the R programming language to:

  • Work with probability
  • Understand factors in probability 
  • Calculate combinations and permutations
  • Work with continuous variables
  • Work with random variables
  • Apply central limit theorem
  • Apply statistics to real-world scenarios

Delivery Methods

Delivery Method Duration
Classroom
Instructor-led classroom-based training. Classes are scheduled at various conference centres in the Sandton area or your premises. Stationary and printed manuals or online resources are included. Refreshments, including 2 tea breaks and a cooked meal for lunch are provided. Training is between 9 am to 4 pm.
Classroom
4 Days Get a Quote
Live Virtual Training
This course is delivered live via Microsoft Teams. You will be able to see and hear the instructor, view their whiteboard, and ask questions or communicate in the chat. Attend from the comfort of your own home or private office.
Live Virtual Training
4 Days Get a Quote

Discounts Available

Save up to 10% by booking and paying 10 business days before the course.

Brochure:

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Information may change without notice.

Audience

  • Data scientists
  • Programmers
  • Business analysts 
  • Engineers
  • Scientists

Pre-Requisites

Leading Training's Introduction to R or equivalent knowledge

Course Outline / Curriculum

Introduction to statistics with R 

Probability 

  • Discrete probability
    • Relative frequency
    • Notation
    • Probability distributions
  • Monte Carlo simulations for categorical data
    • Setting the random seed
    • With and without replacement
  • Independence
  • Conditional probabilities
  • Addition and multiplication rules
    • Multiplication rule
    • Multiplication rule under independence
    • Addition rule
  • Combinations and permutations
    • Monte Carlo example
  • Examples
    • Monty Hall problem
    • Birthday problem
  • Infinity in practice
  • Exercises
  • Continuous probability
  • Theoretical continuous distributions
    • Theoretical distributions as approximations
    • The probability density
  • Monte Carlo simulations for continuous variables
  • Continuous distributions
  • Exercises

Random variables 

  • Random variables
  • Sampling models
  • The probability distribution of a random variable
  • Distributions versus probability distributions
  • Notation for random variables
  • The expected value and standard error
    • Population SD versus the sample SD
  • Central Limit Theorem
    • How large is large in the Central Limit Theorem?
  • Statistical properties of averages
  • Law of large numbers
    • Misinterpreting law of averages
  • Exercises
  • Case study: The Big Short
    • Interest rates explained with chance model
    • The Big Short
  • Exercises

Schedule Dates and Booking

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