Python Training for Actuaries and Certification

This online course contains 5 lessons, each with a 1-hour video and supporting files. A certificate is issued upon competition.

Course Level: Beginner

Course Requirements: None

Lesson 1: Basics of Python language:

• History of Python and basic details on Python
• Installation of Anaconda Environment and familiarity with Spyder and Jupyter
• Basics of variables, string handling, flow controls, functions and loops
• Lists, Tuples and Dictionaries



Lesson 2: Fundamentals of Python

• Input validation
• Conditionals and recursion
• Reading and writing files
• Debugging in python
• Introduction to Pandas
• Viewing and selection of data and working with excel spreadsheets
• Pivot tables and reshaping of the data
• Time series and plotting using matplot library
• Introduction to web scrapping



Lesson 3: Introduction to Numpy

• Basics of Numpy
• Vectors, matrices and multidimensional arrays
• Optimisation problems and a distribution fitting case study



Lesson 4: Objected Oriented Programming in Python

• Introduction to classes and objects
• Inheritance
• Classes and methods, Classes and functions
• Creating basic power point slides using python



Lesson 5: Introduction to Scikit Learn

• Basics of Scikit Learn
• Case study using Principal Component Analysis (PCA)
• Case study using ridge regression

Actuaries.

• Actuaries and aspiring actuaries.

Certification

• On successful completion of the course, you will receive a Course Completion Certificate from Merit Global

Obtaining the Python Training for Actuaries certification gives actuarial professionals and aspiring actuaries a practical, career‑relevant edge by teaching them how to apply Python - one of the most widely‑used programming languages in analytics - to real actuarial tasks such as data cleaning, modeling, automation and visualization. The course covers foundational Python concepts (like variables, flow controls, file handling and object‑oriented programming) as well as powerful libraries such as Pandas, NumPy, and Scikit‑Learn that are essential for efficient data analysis and predictive modeling. These skills help actuaries move beyond manual spreadsheets to scalable workflows, enabling them to automate repetitive calculations, integrate data from multiple sources and build robust analytical models that support pricing, risk evaluation and forecasting. This prepares learners not just to understand data but to harness it - a key capability in modern actuarial practice where data complexity is growing rapidly.

In the U.S. job market, combining actuarial expertise with Python programming can meaningfully enhance career opportunities and compensation. Actuaries overall command strong salaries - with average total compensation often ranging from about $85,000 to over $190,000 annually depending on experience and seniority - and professionals with data analytics and programming skills tend to outperform peers without them. According to salary estimates, actuarial roles that require modeling and technical skills like Python proficiency can see a +7–10% salary uplift compared with traditional roles, reflecting the premium employers place on analytics capability. As actuarial work continues to merge with data science and predictive analytics, being certified in Python helps you stand out for roles that value both domain knowledge and technological fluency, boosting your competitiveness for higher‑impact, higher‑paying positions in insurance, consulting, finance and risk management.

Qualified actuary since 2013 and has worked in a freelance capacity for few of the large UK insurers. His experience is in the areas of risk calibrations, Solvency II Matching Adjustment & Volatility Adjustment and system design and implementation.

Highlights:

• Actuary-focused Python training with real-world insurance and financial data examples.
• Hands-on, instructor-led sessions covering both fundamentals and advanced applications.
• Practical use of industry-standard Python libraries such as Pandas, NumPy, Matplotlib and Scikit-Learn.
• Step-by-step guidance using tools like Jupyter Notebook, Anaconda and Spyder.
• Applied case studies aligned to actuarial workflows, modelling and analytics.
• Introduction to machine learning concepts relevant to actuarial analysis.
• Focus on automation, data handling and efficiency improvements for actuarial tasks.
• Access to recorded sessions and learning materials for flexible revision.
• Certificate of completion from Merit Global Training.