Python for Data Analytics Online Course

Python for Data Analytics Online Course

This course introduces programming through Python, starting with Jupyter Notebooks, Python 3 basics, variables, data types, loops, and functions before moving into finance-focused applications. You’ll learn to calculate returns, analyze risks, measure diversification, and apply regression models for metrics like alpha and beta. Using finance libraries and real datasets, you’ll explore advanced tools such as Markowitz Portfolio Optimization, CAPM, and Monte Carlo simulations to build efficient frontiers, compute Sharpe ratios, and model asset prices. By combining coding fundamentals with practical finance techniques, the course equips you with the skills to bridge data science and financial analysis for real-world applications.

Who should take the course?

This course is ideal for students, beginners, and professionals who want to develop data analytics skills using Python. It’s well-suited for those with little to no prior experience in data analysis who are eager to learn how to work with data, generate insights, and apply Python libraries for real-world problems. Whether you’re an aspiring data analyst, a researcher, or a professional aiming to make data-driven decisions, this course will equip you with practical knowledge to analyze and interpret data effectively with Python.

What you will learn

• Write Python code for data analysis and financial modeling
• Clean, structure, and visualize financial data
• Calculate and interpret returns, volatility, and portfolio risk
• Perform and evaluate regression analyses on investment data
• Simulate asset prices using Monte Carlo methods
• Optimize portfolios using Markowitz theory and CAPM principles

Course Outline

Welcome! Course Introduction

• What Does the Course Cover?

Introduction to Programming with Python

• Programming Explained in 5 Minutes
• Why Python?
• Why Jupyter?
• Installing Python and Jupyter
• Jupyter's Interface – the Dashboard
• Jupyter's Interface – Prerequisites for Coding
• Python 2 vs Python 3: What's the Difference?

Python Variables and Data Types

• Variables
• Numbers and Boolean Values
• Strings
• Introduction to Anaconda AI
• Using the Anaconda Assistant: Strings

Basic Python Syntax

• Arithmetic Operators
• The Double Equality Sign
• Reassign Values
• Add Comments
• Line Continuation
• Indexing Elements
• Structure Your Code with Indentation

Python Operators Continued

• Comparison Operators
• Logical and Identity Operators

Conditional Statements

• Introduction to the IF statement
• Add an ELSE statement
• Else if, for Brief – ELIF
• A Note on Boolean Values

Python Functions

• Defining a Function in Python
• Creating a Function with a Parameter
• Another Way to Define a Function
• Using a Function in another Function
• Combining Conditional Statements and Functions
• Creating Functions Containing a Few Arguments
• Notable Built-in Functions in Python

Python Sequences

• Lists
• Using Methods
• List Slicing
• Tuples
• Dictionaries

Using Iterations in Python

• For Loops
• While Loops and Incrementing
• Create Lists with the range() Function
• Use Conditional Statements and Loops Together
• All In – Conditional Statements, Functions, and Loops
• Using the Anaconda Assistant: Several Python Tools
• Iterating over Dictionaries
• Using the Anaconda Assistant: Dictionaries

Advanced Python Tools

• Object Oriented Programming
• Modules, Packages, and the Standard Library
• Importing Modules
• Must-have packages for Finance and Data Science
• Working with arrays
• Generating Random Numbers
• A Note on Using Financial Data in Python
• Sources of Financial Data
• Accessing the Notebook Files
• Importing and Organizing Data in Python – Part I
• Importing and Organizing Data in Python – Part II.A
• Importing and Organizing Data in Python – Part II.B
• Importing and Organizing Data in Python – Part III
• Changing the Index of Your Time-Series Data
• Restarting the Jupyter Kernel

PART II FINANCE – Calculating and Comparing Rates of Return in Python

• Considering both risk and return
• What are we going to see next?
• Calculating a security's rate of return
• Calculating a Security's Rate of Return in Python – Simple Returns – Part I
• Calculating a Security's Rate of Return in Python – Simple Returns – Part II
• Calculating a Security's Return in Python – Logarithmic Returns
• What is a portfolio of securities and how to calculate its rate of return
• Calculating a Portfolio of Securities' Rate of Return
• Popular stock indices that can help us understand financial markets
• Calculating the Indices' Rate of Return

PART II FINANCE – Measuring Investment Risk

• How do we measure a security's risk?
• Calculating a Security's Risk in Python
• The benefits of portfolio diversification
• Calculating the covariance between securities
• Measuring the correlation between stocks
• Calculating Covariance and Correlation
• Considering the risk of multiple securities in a portfolio
• Calculating Portfolio Risk
• Understanding Systematic vs. Idiosyncratic risk
• Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio

PART II FINANCE – Using Regressions for Financial Analysis

• The fundamentals of simple regression analysis
• Running a Regression in Python
• Are all regressions created equal? Learning how to distinguish good regressions
• Computing Alpha, Beta, and R Squared in Python

PART II FINANCE – Markowitz Portfolio Optimization

• Markowitz Portfolio Theory - One of the main pillars of modern Finance
• Obtaining the Efficient Frontier in Python – Part I
• Obtaining the Efficient Frontier in Python – Part II
• Obtaining the Efficient Frontier in Python – Part III

PART II FINANCE – The Capital Asset Pricing Model

• The intuition behind the Capital Asset Pricing Model (CAPM)
• Understanding and calculating a security's Beta
• Calculating the Beta of a Stock
• The CAPM formula
• Calculating the Expected Return of a Stock (CAPM)
• Introducing the Sharpe ratio and how to put it into practice
• Obtaining the Sharpe ratio in Python
• Measuring alpha and verifying how good (or bad) a portfolio manager is doing

PART II FINANCE – Multivariate Regression Analysis

• Multivariate regression analysis - a valuable tool for finance practitioners
• Running a multivariate regression in Python

PART II FINANCE – Monte Carlo Simulations as a Decision-Making Tool

• The essence of Monte Carlo simulations
• Monte Carlo applied in a Corporate Finance context
• Monte Carlo: Predicting Gross Profit – Part I
• Monte Carlo: Predicting Gross Profit – Part II
• Forecasting Stock Prices with a Monte Carlo Simulation
• Monte Carlo: Forecasting Stock Prices - Part I
• Monte Carlo: Forecasting Stock Prices - Part II
• Monte Carlo: Forecasting Stock Prices - Part III
• An Introduction to Derivative Contracts
• The Black Scholes Formula for Option Pricing
• Monte Carlo: Black-Scholes-Merton
• Monte Carlo: Euler Discretization - Part I
• Monte Carlo: Euler Discretization - Part II