Course curriculum

  • 1

    Welcome to Foundations in Python for Actuaries Presentation

  • 2

    Getting started

    • 0.1 - Objectives of the Course

      FREE PREVIEW

    • 0.2 - Introducing Python

    • 0.3 - Installing Python, External Packages, Jupyter, and Spyder

    • 0.4 - Working with Jupyter Notebooks and Spyder (Optional)

    • 0.5 - Using Jupyter Notebooks in this Platform (Mandatory)

    • 0.6 - Overview of Quizzes, Coding Challenges and Assessments
  • 3

    1 - Problem Specification

    • 1.1 - Overview

    • 1.2 - Basic Concepts

    • 1.3 - Basic data structures and range() function

    • 1.4 - Example: Using Python as a Calculator

    • 1.5 - Conditionals

    • 1.6 - Summary

    • 1.7.1 - Plot Customisation (Optional)

    • 1.7.2 - Different plots (Optional)

    • Problem Specification Quiz

    • Coding Challenge Week 1
  • 4

    2 - Data Collection

    • 2.1 - Overview

    • 2.2 - Random data creation

    • 2.3 - Arrays and series

    • 2.4 - Matrices

    • 2.5.0 - DataFrames: Introduction, subsetting and filtering

    • 2.5.1 - DataFrames: More subsetting and lookup functions

    • 2.5.2 - DataFrames: Missing (or NA) values

    • 2.5.3 - DataFrames: Adding or removing data

    • 2.5.4 - DataFrames: Categorical variables, summary and sorting

    • 2.5.5 - DataFrames: Pivot table and grouping

    • 2.5.6 - DataFrames: Time series

    • 2.6 - Reading and Writing External Data Sets

    • 2.7 - Built-in Datasets

    • 2.8 - Summary

    • Data Collection Quiz

    • Coding Challenge Week 2
  • 5

    3 - Data Management

    • 3.1 - Overview

    • 3.2 - Loops

    • 3.3 - Functions

    • 3.4 - Object-Oriented Programming

    • 3.5 - Example: Natural Disasters

    • 3.6 - Example: Cash Flows

    • 3.7 - Summary

    • Data Management Quiz

    • Coding Challenge Week 3
  • 6

    4 - Model Building

    • 4.1 - Overview

    • 4.2 - Object Functions

    • 4.3 - Mathematical Functions

    • 4.4 - Statistical Functions

    • 4.5 - Statistical Models: Introduction to Linear Regression

    • 4.6 - Summary

    • Model Building Quiz

    • Coding Challenge Week 4
  • 7

    5 - Visualisations and Reporting

    • 5.1 - Overview

    • 5.2 - Line Plots

    • 5.3 - Scatter plots

    • 5.4 - Bar charts

    • 5.5 - Histograms and Density plots

    • 5.6 - Box plots

    • 5.7 - Summary

    • Visualisations Quiz

    • Coding Challenge Week 5
  • 8

    Appendix and Further Resources

    • Blank Python Notebook

    • Introduction and Summary of Libraries Introduced

    • Seaborn - statistical data visualisation library

    • Example: Linear Regression and Plotting

    • User input and data validation functions

    • Python conventions

    • Debugging in Python

    • Keyboard shortcuts and help function