Hands-On Experience | 25+ Hours & 3+ Projects | No Prerequisite
A perfect foundation stone to start a career in Python programming by giving you the hands-on training and practical experience you need to succeed.
You'll learn the ins and outs of Python programming and become proficient in using libraries like Pandas, NumPy, and Matplotlib. With our comprehensive modules, you'll have the tools and confidence you need to excel in Python. Whether you're a complete beginner or have some programming experience, our course will give you the skills and knowledge you need to succeed.
Ideal for: Financial Analysts, Quant Risk Analysts, Post-Grad Students, and Professionals preparing for roles in: Banks, Investment Firms, Asset Management Companies, Consulting Firms, Mutual Funds and Hedge Funds, and Other Financial Institutions
Python Data Types and Variables: Learn the foundational building blocks of Python, including the four core data types – Strings, Integers, Floats, and Booleans. Understand how to declare, assign, and manipulate variables. Practice using built-in functions like print() and type() and master casting between data types. Develop an understanding of Python's indentation rules and comment usage for clean, readable code.
Python Operations and Data Structures: Master Python's operators – Arithmetic, Assignment, Comparison, and Logical – to manipulate data effectively. Dive deep into essential data structures, including Lists, Tuples, Sets, and Dictionaries. Learn how to create, index, slice, and update these structures. Explore core methods like append(), remove(), sort(), add(), union(), and dictionary methods like keys() and values().
Python Statements and Control Flow: Develop logic-building skills with conditional and looping statements. Learn how to structure programs using if, if-else, and if-elif-else statements. Gain proficiency in iterative control flows using for and while loops, nested conditionals, loop control statements like break, and comprehension techniques for lists and sets. Handle unexpected events with Python’s try-except error-handling mechanisms.
Functions and Object-Oriented Programming (OOP): Understand how to structure code modularly with functions. Learn to define custom functions, pass arguments, and use return statements. Transition into Object-Oriented Programming by mastering classes, objects, constructors, and methods. Explore key OOP principles: Inheritance, Encapsulation, Polymorphism, and Abstraction for scalable and reusable code design.
Project – Bank Account Management System: Apply object-oriented programming concepts in a real-world mini-project. Build a fully functional banking system that supports account creation, deposits, withdrawals, fund transfers, and balance tracking. This project consolidates class-based programming and user-defined methods to simulate a practical financial application.
Python for Data Analysis – Pandas and NumPy: Leverage Python’s most powerful libraries for data analysis. Use Pandas to handle structured data, manipulate DataFrames, clean missing values, and perform group-by and pivot operations. Utilize NumPy for efficient numerical computations, array manipulations, and mathematical operations critical for performance optimization in large datasets.
Data Visualization with Matplotlib: Bring data to life with visual representations. Learn to use Matplotlib to plot line charts, histograms, scatter plots, pie charts, and multi-panel subplots. Customize titles, axis labels, legends, and data annotations to enhance clarity and interpretability of insights.
Basic Financial Applications using Python: Introduce finance-specific tools and workflows in Python. Extract and analyze historical stock data, calculate returns and volatility, and visualize market movements. Understand the structure of the Treasury Yield Curve and use Python to visualize interest rate environments. Gain a conceptual understanding of call and put options with Python-based examples.
Project – Stock Screening and Ranking Tool: Build a real-world Python application to evaluate stock performance. Create a stock screener that computes key metrics such as Mean Return, Beta, Sharpe Ratio, and Standard Deviation. Learn to filter and rank stocks using quantitative measures and object-oriented design for modularity and reusability.
