Basic Python for Finance & Quant Analytics at The Risk Insider is a hands-on, beginner-friendly program designed for students and professionals who want to build a strong programming foundation for finance, risk, and quantitative roles. This course takes you step-by-step from setting up Python and development tools (Anaconda, Jupyter Notebook and VS Code) to mastering core programming concepts such as variables, data types, control flow, loops, functions and object-oriented programming.
You will gain a solid command of industry-standard numerical and data-analysis libraries including NumPy and Pandas, covering array computing, vectorised operations, linear algebra, efficient memory usage, data cleaning, exploratory data analysis, filtering, merging datasets, and rolling computations. The course also introduces professional data visualisation using Matplotlib, Seaborn and Plotly to analyse prices, returns and volatility in a clear and practical way.
We’re excited to have you on board!
This course has been carefully designed to give you end-to-end foundational knowledge of Python for Finance and Quant Analytics. It combines core programming concepts, practical coding workflows, real financial datasets, numerical computing, data analysis, visualisation, and finance-oriented use cases to help you build a strong and industry-ready Python foundation. Since this is a self-paced course, you can pause, revisit, and practise each topic as many times as you need. Consistency, however, will significantly multiply your outcomes.
This learning journey is designed for students and professionals in finance, risk and quantitative roles who want to build strong Python fundamentals and accelerate their transition into data-driven finance and analytics roles.
The course assumes a basic quantitative or mathematical background, but essential ideas are explained wherever required.
While the course aims to cover all the practical Python skills needed for finance and quant workflows, tools, libraries and industry practices continue to evolve. Learners are encouraged to request new topics or enhancements directly from the instructor so the course remains relevant.
This is a dynamic course. New examples, datasets, coding techniques and finance-oriented use cases may be added over time. Don’t worry if you notice new content appearing later 😄 — this is part of keeping your learning current and industry-aligned.
Demonstrations are provided using realistic financial datasets and simplified examples. Actual institutions may use different systems, proprietary data sources or internal platforms.
This course is educational in nature and does not constitute financial, investment or trading advice.
This course is intended to supplement learning and professional development, and does not replace formal workplace training, internal model governance or compliance requirements.
Updates related to live sessions, doubt-clearing, resume guidance and one-to-one discussions will be shared through the WhatsApp Discussion Forum Group.
👉 By the end of this course, you will not only have a strong command of Python fundamentals for finance and quant analytics, but also the confidence to apply your skills in interviews, internships, entry-level quant and risk roles, and real-world data-driven financial projects.
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