Designed for Finance Professionals
The Python for Finance Professionals (Basics) Course is designed to build a strong foundation in Python programming by providing hands-on training and practical experience that is needed to succeed across functions. You'll learn the ins and outs of Python, from working with different data types and variables to performing complex operations, automation, data manipulations, and becoming proficient in using libraries such as Pandas, NumPy, and Matplotlib.
This course is ideal for both beginners and professionals looking to learn or advance in programming and bite-sized automation scripting.
What You'll Learn
Explore the core data types and variables in Python. Learn how to work with strings, integers, floats, and booleans, and understand how to store and manipulate values using variables. Learn Python's essential operators, including assignment, arithmetic, comparison, and logical operators.
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MCQ-Based Assessment: Python Data Types and Variables (12 MCQs), Python Operators (15 MCQs), Core Data Structures (30 MCQs).
Interest rates influence financial markets, corporate financing, and investment decisions. This module covers yield curves, interest rate shocks, and the impact of changing interest rates on market risk.
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Hands-On Application: Build automated market reports that monitor yield spread and SnP 500 equity market performance, using Python to extract, process, visualize, and generate reports.
Efficient market risk management relies on high-quality market data across multiple asset classes. This module introduces financial data extraction, automation, and real-time monitoring techniques.
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Hands-On Application: Implement Python-based automation to streamline market data workflows, visualize price trends, and create dynamic dashboards for risk monitoring.
A strong foundation in statistical methods and probability distributions is critical for market risk modeling. This module introduces statistical measures, correlation analysis, and probability distributions that underpin risk quantification in financial markets.​
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Compute statistical measures using real market data, analyze historical returns, and assess correlations between different asset classes.
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Simulate asset returns and prices using normal and log-normal distributions, respectively, evaluate risk measures, and apply these concepts to real-world financial scenarios.
Understanding the term structure of interest rates is crucial for pricing fixed-income securities, managing interest rate risk, and constructing yield curves for scenario creation. This module introduces interpolation techniques used to construct smooth and continuous yield curves, regression models for yield curve estimation (including linear and polynomial regressions), and advanced factor-based models such as the Nelson-Seigel and Nelson-Seigel-Svensson models needed for accurate interest rate modeling and forecasting.
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Hands-On Application: Implement yield curve modeling techniques, calibrate model parameters using real-world market data, and validate their predictive accuracy.
Subscription
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Primer Plan
3,000 INR
100% Refund (No Questions Asked) within 2 hours of subscription.