Python Statistical Modelling

TRAINING COURSE

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

Details

This course will be useful for individuals interested in leveraging statistical modelling to analyze data and derive insights for decision-making. It is designed for data scientists, statisticians, researchers, analysts, and professionals across various industries who work with data and seek to understand patterns, relationships, and trends in their datasets. Additionally, students and academics studying statistics, data science, or related fields will benefit from mastering statistical modelling techniques using Python.

In this course, you will learn to apply a wide range of statistical models to analyze data, make predictions, and draw meaningful insights for decision-making using Python.  You will focus on 2 key areas:

  • Derive statistical inferences
  • Create statistical models

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
2 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
2 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 Python programming knowledge.

Mathematical ability at matric core maths level

Basic statistics knowledge

 

 

Course Outline / Curriculum

  • Introduction to Statistical Modelling

    • Overview of statistical modelling concepts and methodologies. 

    • Explanation of the role of statistical models in data analysis and decision-making. 

  •  

  • Statistical inference  

    •  Polls 

    •  Populations, samples, parameters, and estimates 

    •  Central Limit Theorem in practice 

    •  Confidence intervals 

    •  Power 

    • p-values 

    • Association tests 

    •  

  • Statistical models  

    • Poll aggregators 

    •  Data-driven models 

    •  Bayesian statistics 

    •  Bayes theorem simulation 

    •  Hierarchical models 

    • Case study: election forecasting  

    • The t-distribution 

Schedule Dates and Booking

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