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Introduction to Python for Finance

Learn to use Python for financial analysis using basic skills, including lists, data visualization, and arrays.

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Course Description

Learn the Fundamentals of Python for Finance

The financial industry uses Python extensively for quantitative analysis, ranging from understanding trading dynamics to risk management systems. This course will show you how to analyze your financial data by building your Python skills.

Manipulate and Visualize Data with Python Packages

The first chapter explains how Python and finance go hand in hand. You will then learn Python basics such as printing output, performing calculations, understanding data types, and creating variables.

Next, you’ll cover lists and arrays in Python, exploring how you can use them to work with data. You’ll use the NumPy and Matplotlib packages to manipulate and visualize data.

Perform Financial Analysis Using Python

Finally, you will finish the course by conducting a Python financial analysis on an S&P 100 dataset. Here, you will apply your Python skills to filter lists, summarize sector data, plot P/E ratios in histograms, visualize financial trends, and identify outliers.

By the end of the course, you will be confident in your basic Python skills and practical financial analysis skills. These skills are highly rewarded in the finance industry to solve quantitative finance problems. This course is part of our Finance Fundamentals in Python track which is perfect for those who wish to delve deeper into Python for finance.
  1. 1

    Welcome to Python

    Free

    This chapter is an introduction to basics in Python, including how to name variables and various data types in Python.

    Play Chapter Now
    Welcome to Python for Finance!
    50 xp
    Why might you use Python in finance?
    50 xp
    Run code vs. submit answer
    100 xp
    Comments and variables
    50 xp
    Printing output
    100 xp
    Finding the average revenue
    100 xp
    Data types
    50 xp
    Creating variables
    100 xp
    Determining types
    100 xp
    Booleans in Python
    100 xp
    Combining data types
    100 xp
  2. 2

    Lists

    This chapter introduces lists in Python and how they can be used to work with data.

    Play Chapter Now
    Lists
    50 xp
    Creating lists in Python
    100 xp
    Indexing list items
    100 xp
    Slicing multiple list elements
    100 xp
    Nested lists
    50 xp
    Stock up a nested list
    100 xp
    Subset a nested list
    100 xp
    List methods and functions
    50 xp
    Exploring list methods and functions
    100 xp
    Using list methods to add data
    100 xp
    Finding stock with maximum price
    100 xp
  3. 3

    Arrays in Python

    This chapter introduces packages in Python, specifically the NumPy package and how it can be efficiently used to manipulate arrays.

    Play Chapter Now
    Arrays
    50 xp
    Create an array
    100 xp
    Elementwise operations on arrays
    100 xp
    Subsetting elements from an array
    100 xp
    2D arrays and functions
    50 xp
    Creating a 2D array
    100 xp
    Subsetting 2D arrays
    100 xp
    Calculating array stats
    100 xp
    Generating a sequence of numbers
    100 xp
    Using arrays for analysis
    50 xp
    Who's above average?
    100 xp
    Who's in health care?
    100 xp
  4. 4

    Visualization in Python

    In this chapter, you will be introduced to the Matplotlib package for creating line plots, scatter plots, and histograms.

    Play Chapter Now
    Visualization in Python
    50 xp
    Importing matplotlib and pyplot
    100 xp
    Adding axis labels and titles
    100 xp
    Multiple lines on the same plot
    100 xp
    Scatterplots
    100 xp
    Histograms
    50 xp
    What are applications of histograms in finance?
    50 xp
    Is data normally distributed?
    100 xp
    Comparing two histograms
    100 xp
    Adding a legend
    100 xp
  5. 5

    S&P 100 Case Study

    In this chapter, you will get a chance to apply all the techniques you learned in the course on the S&P 100 data.

    Play Chapter Now
    Introducing the dataset
    50 xp
    Lists
    100 xp
    Arrays and NumPy
    100 xp
    A closer look at the sectors
    50 xp
    Filtering arrays
    100 xp
    Summarizing sector data
    100 xp
    Plot P/E ratios
    100 xp
    Visualizing trends
    50 xp
    Histogram of P/E ratios
    100 xp
    Identify the outlier
    50 xp
    Name the outlier
    100 xp

In the following tracks

Finance Fundamentals in Python

Datasets

Stocks data (I)Stocks data (II)S&P 100 data

Collaborators

Lore Dirick
Eunkyung Park
Sumedh Panchadhar
Adina Howe HeadshotAdina Howe

Assistant Professor and Data Scientist

Adina is an Assistant Professor in the College of Engineering at Iowa State University (ISU) in Ames, Iowa. She teaches numerical methods, computational biology, and data science courses in the undergraduate and graduate programs. Her research focuses on using computational tools to help address global grand challenges, such as how to best manage our land and water resources. She loves dogs, board games, and using programming to solve problems that help people.
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