Applied Data Science For Finance

Learn Python for finance, build real projects with Pyfolio, Riskfolio, YFinance and preprocess financial data

What you'll learn

Use Python to preprocess financial data and prepare it for analysis.

Build hands-on projects using libraries like Pyfolio and Riskfolio-lib.

Apply data science workflows to finance problems such as risk and return.

Understand core financial concepts and connect them with working code.

Requirements

No prior finance or programming experience is required. You’ll learn everything step by step. Just bring a computer and a steady internet connection.

Description

This course is designed for people who want to understand how finance and data science come together in practice — without getting lost in theory or endless formulas. You’ll start with Python, covering everything from basic syntax to functions, data structures, and file handling. Then you’ll move into data preprocessing — how to clean financial data, handle missing values, remove outliers, and prepare data for analysis.After that, the course focuses on applied tools used in finance: Pyfolio, MPLFinance, Riskfolio-lib, and others. You’ll use real financial data to build models for portfolio analysis, risk management, and return calculation. No abstract toy datasets — we work with real stock data, fund performance, and economic indicators.You don’t need a background in finance or computer science. The course starts from the beginning and explains every step in a clear and structured way. And if you already know Python, you can skip ahead to the finance and project sections.Later updates will include R, MATLAB, and Julia implementations for some of the key projects. This makes the course useful not just for learners, but also for professionals looking to compare tools.By the end of the course, you’ll have a working understanding of how to use code in financial workflows — and a set of notebooks you can actually use.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Introduction to Python

Lecture 2 What is Python?

Lecture 3 Anaconda & Jupyter & VSCode

Lecture 4 Google Colab

Lecture 5 Environment Setup

Lecture 6 Python Syntax & Basic Operations

Lecture 7 Data Structures: Lists, Tuples, Sets

Lecture 8 Control Structures & Looping

Lecture 9 Functions & Basic Functional Programming

Lecture 10 Intermediate Functions

Lecture 11 Dictionaries and Advanced Data Structures

Lecture 12 Modules, Packages & Importing Libraries

Lecture 13 File Handling

Lecture 14 Exception Handling & Robust Code

Lecture 15 Basic Object-Oriented Programming (OOP) Concepts

Lecture 16 Advanced List Operations & Comprehensions

Section 3: Data Preprocessing

Lecture 17 Data Quality

Lecture 18 Data Cleaning Techniques

Lecture 19 Handling Missing Value

Lecture 20 Dealing With Outliers

Lecture 21 Feature Scaling and Normalization

Lecture 22 Standardization

Lecture 23 Encoding Categorical Variables

Lecture 24 Feature Engineering

Lecture 25 Dimensionality Reduction

Lecture 26 Data Visualization Basics

Section 4: Python Projects

Lecture 27 Pyfolio

Lecture 28 MPL Finance

Lecture 29 Riskfolio-lib

Lecture 30 Altair & YFinance Project

Lecture 31 Optimization and Risk Management with Sci-Py

Lecture 32 Economic Modelling with Python

Lecture 33 finTA Library with Apple

Section 5: Finance Basics

Lecture 34 Basic Finance Concepts

Lecture 35 Corporate Finance

Lecture 36 Financial Markets

Lecture 37 Financial Ratios

Lecture 38 Financial Statement

Lecture 39 Basics of Macroeconomics

Lecture 40 Bonds and Fixed Income

Lecture 41 Time Value of Money

Lecture 42 Technical Analysis

Lecture 43 Risk and Return

Lecture 44 Portfolio Management

Lecture 45 Financial Instruments

Lecture 46 Forex Markets

Lecture 47 Fundamental Analysis

This course is for finance students, developers, analysts, or anyone curious about using code in financial workflows. It’s beginner-friendly and project-based.