Python Regression

TRAINING COURSE

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

Details

Performing regressions is a powerful method to help you understand the relationships between your variables, which makes it a must for anyone working with data. Thankfully, we can use the Python language to make this important task much easier and more efficient.

In this course you will learn to: 

  • Perform regressions using Python
  • Analyse the strength of correlations
  • Set conditional expectations
  • Analyse the regression line
  • Employ linear models in real-life scenarios 
  • Understand counfounding
  • Perform Lease Squares Estimates
  • Understand association vs causation

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 Python Course or equivalent knowledge

Course Outline / Curriculum

Regression 

  • Case study: is height hereditary?
  • The correlation coefficient
    • Sample correlation is a random variable
    • Correlation is not always a useful summary
  • Conditional expectations
  • The regression line
    • Regression improves precision
    • Bivariate normal distribution (advanced)
    • Variance explained
    • Warning: there are two regression lines
  • Exercises

Linear models 

  • Case study: Moneyball
    • Sabermetics
    • Baseball basics
    • No awards for BB
    • Base on balls or stolen bases?
    • Regression applied to baseball statistics
  • Confounding
    • Understanding confounding through stratification
    • Multivariate regression
  • Least squares estimates
    • Interpreting linear models
    • Least Squares Estimates (LSE)
    • The lm function
    • LSE are random variables
    • Predicted values are random variables
  • Exercises
  • Linear regression in the tidyverse
    • The broom package
  • Exercises
  • Case study: Moneyball (continued)
    • Adding salary and position information
    • Picking nine players
  • The regression fallacy
  • Measurement error models
  • Exercises

Association is not causation 

  • Spurious correlation
  • Outliers
  • Reversing cause and effect
  • Confounders
    • Example: UC Berkeley admissions
    • Confounding explained graphically
    • Average after stratifying
  •  Simpson’s paradox
  • Exercises

Schedule Dates and Booking

There are currently no scheduled dates.

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