In this module, we will delve into the fundamentals of Python programming and the Jupyter Notebook environment. You’ll learn how to set up the necessary tools, explore Python’s features, and become familiar with the Jupyter interface, ensuring a solid foundation for the course.

In this module, we will introduce the core data types in Python, including variables, numbers, Booleans, and strings. You will learn how to store and manipulate different types of data, forming the basis for more advanced programming tasks.

In this module, we will cover Python’s essential syntax elements, including operators, commenting, and the importance of indentation. You’ll learn techniques to enhance code readability and functionality, preparing you for more complex coding challenges.

In this module, we will dive deeper into Python operators, focusing on comparison, logical, and identity operators. You will enhance your ability to create expressions that drive decision-making in your code.

In this module, we will explore conditional statements, such as IF, ELSE, and ELIF. You’ll learn how to build logic-driven code that can handle different scenarios and outcomes based on conditions.

In this module, we will focus on Python functions—how to define them, use parameters, and combine them with other tools. You’ll also explore some of Python’s built-in functions to streamline your programming.

In this module, we will cover Python’s sequence types, including lists, tuples, and dictionaries. You’ll learn how to store, slice, and manage data effectively within these structures.

In this module, we will introduce Python’s looping mechanisms, focusing on for-loops, while-loops, and the range() function. You’ll also learn to integrate loops with conditional logic and functions for powerful automation.

In this module, we will introduce advanced Python tools, including OOP concepts, modules, and data manipulation techniques. You’ll learn how to work with external packages and handle complex data operations in finance.

In this module, we will explore the foundational concepts of calculating and comparing rates of return. You’ll learn how to apply these concepts in Python to compute the returns of individual securities, portfolios, and stock indices, providing key insights into risk and performance.

In this module, we will dive into risk measurement in finance. You’ll learn how to quantify the risk of securities and portfolios, calculate covariance and correlation, and use Python tools to analyze the risks associated with investment decisions.

In this module, we will cover regression analysis and its application in finance. You will learn how to run regressions in Python, interpret the results, and use key indicators such as Alpha and Beta to assess financial performance.

In this module, we will introduce Markowitz Portfolio Optimization, focusing on building efficient portfolios. You’ll learn to calculate the efficient frontier in Python and optimize asset allocation to achieve the best balance between risk and return.

In this module, we will examine the Capital Asset Pricing Model (CAPM), its calculation, and its significance in finance. You’ll use Python to calculate Beta, expected returns, and performance metrics like the Sharpe Ratio and Alpha to evaluate investments.

In this module, we will focus on multivariate regression analysis, applying it in the context of finance. You’ll learn to run multivariate regressions in Python and analyze the relationships between multiple variables affecting asset performance.

In this module, we will delve into Monte Carlo simulations and their powerful applications in finance. You’ll use Python to simulate future profits, forecast stock prices, and apply the Black Scholes formula, enhancing your ability to make informed investment decisions.