TFA Curriculum for Python for Finance Professionals (Basics) Program
- Pankaj Maheshwari
- Jan 1, 2024
- 5 min read
Updated: Aug 26
Welcome to the Python for Finance Professionals (Basics) program!
This program is designed to provide structured learning and hands-on experience with a strong focus on its applications in the finance industry. It blends fundamental programming concepts with practical financial use cases, ensuring that you not only learn the basics of Python but also understand how to apply these skills to real-world financial tasks.
Each module has been designed to help you build a solid understanding of Python, from working with different data types and variables to performing complex operations, automation, and data manipulations. The goal is to equip you with the skills to automate financial processes, analyze data, and ultimately enhance your efficiency as a finance professional.
We encourage you to fully immerse yourself, participate actively in discussions, ask questions, and experiment or approach each concept with the intent to apply it in solving real-world problems. Your participation will deepen your understanding and prepare you to apply Python effectively in your financial work. The path weāve laid out will guide you through the essentials, giving you the foundation to tackle more advanced programming and data analysis tasks as you progress.
Getting Started!
Your journey begins with setting up your development environmentāa critical step in ensuring a seamless and productive experience throughout the program. Youāll learn to set up essential tools, explore user-friendly platforms, and prepare to integrate Python with Excel for powerful analytics.
Youāll Learn:
How to use Anaconda NavigatorĀ as your central hub for performing automated tasks and managing environments.
Introduction to Jupyter Notebook, an interactive platform for coding, data visualization, and presenting Python-based projects.
Seamless integration of Python with Microsoft ExcelĀ for enhanced data handling, automation, and visualization capabilities.
To begin, download and install Anaconda Navigator, a platform for managing Python libraries, packages, and virtual environments.
Introduction to Anaconda Navigator
In this session, youāll explore everything you need to get started with Anaconda Navigator:
Step-by-Step Installation Process: from download to setup on your machine.
Understanding the differences between an Integrated Development Environment (IDE), a Code Editor, and a Compiler.
Learn how to Manage Python LibrariesĀ and PackagesĀ for Efficient Workflows.
Recommended tools and configurations for this program.
Introduction to Jupyter Notebook
In this session, youāll explore the Jupyter Notebook, a powerful and versatile platform for interactive computing.
Launching Jupyter Notebook directly from Anaconda Navigator.
Navigating the interface: default directories, creating new notebooks, and managing files.
Understanding the menu options, toolbar, and commonly used keyboard shortcuts.
Writing and executing Python Code CellsĀ in an intuitive environment.
If you face any issues during the setup, feel free to drop your questions in the comment section below or reach out for support.
Module 1: Python Fundamentals and Data Structures
Python Data Types and Variables
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. This session provides a strong foundation for understanding Python's data structures, essential for all future programming tasks.
Understanding Python Strings: Operations, Methods, and Use Cases: Understanding strings is crucial for any Python programmer, as they are extensively used in various applications ranging from simple text processing to complex data manipulation.
Understanding Python Integers: This section introduces how to define integers, perform common arithmetic operations, and work with integer-specific behaviors such as integer division, modulus, and type casting. Youāll also explore how Python handles very large integers and how integers are used in control flow and iteration.
Understanding Variables in Python: A variable is a symbolic name that refers to a value stored in the computerās memory. This section explains how to declare variables, follow naming conventions, assign values dynamically, and understand the role of variables in Pythonās memory model.
Performing Operations in Python
Learn Python's essential operators, including assignment, arithmetic, comparison, and logical operators. This session covers how to perform calculations, compare values, and use logical conditions to control the flow of your program. A must-learn for building functional Python programs that perform real-world tasks.
Python Operators: A Beginnerās Reference Guide: Operators in Python are special symbols used to perform computations and logical operations on variables and values. This session introduces the fundamental types of operators: arithmetic, comparison, assignment, and logical- equipping you with the tools to build expressions, perform calculations, and control the behavior of your code.
Basics and Applications of Python Lists, Tuples, and Sets
Learn how to store and manage collections of values using Python lists. This session covers how to create lists, access elements by index, modify list content, and apply commonly used list methods. Lists are foundational in many programming tasks, from handling sequences of data to building loops and implementing algorithms.
List Creation and Basic Operations: Understanding how to initialize lists, add and remove elements, and perform basic operations like concatenation and repetition.
List Indexing and Slicing: Master accessing individual elements and extracting sublists using Python's powerful indexing and slicing syntax.
Essential List Methods: Explore critical methods like append(), extend(), insert(), remove(), pop(), sort(), and reverse() for effective list manipulation.
Learn how tuples provide immutable, ordered collections perfect for storing structured financial data that shouldn't change, such as coordinate pairs, database records, or fixed configuration parameters. This session covers how to create tuples, access elements by index, modify tuple content, and apply commonly used tuple methods.
Tuple Creation and Characteristics: Learn multiple ways to create tuples, understand their immutable nature, and explore when immutability provides advantages in financial programming.
Tuple Indexing and Unpacking: Master accessing tuple elements by index and the powerful tuple unpacking feature for elegant variable assignment.
Tuples vs Lists: Performance and Use Cases: Understand when to choose tuples over lists, including memory efficiency, hashability, and data integrity considerations.
Learn sets as unordered collections of unique elements, essential for data deduplication, membership testing, and performing mathematical set operations on financial datasets. This session covers how to create sets, remove duplicates, modify set content, and apply commonly used set methods.
Set Creation and Basic Properties: Learn various methods to create sets, understand uniqueness constraints, and explore the unordered nature of sets.
Set Operations: Union, Intersection, and Difference: Master mathematical set operations to analyze relationships between different financial datasets, such as finding common holdings between portfolios or unique market sectors.
Set Methods and Performance: Explore essential methods like add(), remove(), and discard(), and understand the O(1) average time complexity for membership testing.
Basics and Applications of Python Dictionaries
Learn dictionaries as Python's most versatile data structure, providing key-value mapping essential for organizing complex financial information, creating lookup tables, and building sophisticated data models. This session covers how to create dictionaries and nested dictionaries, understand key-value pairs, indexing dictionaries, modify dictionary content, and apply commonly used dictionary methods.
Dictionary Creation and Access Patterns: Learn multiple ways to create dictionaries, access values by keys, and handle missing keys safely using get() and setdefault() methods.
Dictionary Methods and Operations: Explore essential methods including keys(), values(), items(), update(), pop(), and popitem() for comprehensive dictionary manipulation.
Dictionary Performance and Best Practices: Understand the O(1) average time complexity for dictionary operations and learn best practices for choosing appropriate key types.
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