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

Welcome to Python for Finance!

Run code vs. submit answer

Python as a calculator

Variables

Creating numeric variables

Finding the average revenue

Data types

Creating different types of variables

Determining types

Combining data types

Welcome to Python

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

Lists

Creating lists in Python

Indexing list items

Slicing multiple list elements

Nested lists

Stock up a nested list

Subset a nested list

List methods and functions

Exploring list methods and functions

Using list methods to add data

Finding stock with maximum price

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

Arrays

Create an array

Elementwise operations on arrays

Subsetting elements from an array

2D arrays and functions

Creating a 2D array

Subsetting 2D arrays

Calculating array stats

Generating a sequence of numbers

Using arrays for analysis

Who's above average?

Who's in health care?

Arrays in Python

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

Visualization in Python

Importing matplotlib and pyplot

Adding axis labels and titles

Multiple lines on the same plot

Scatterplots

Histograms

What are applications of histograms in finance?

Is data normally distributed?

Comparing two histograms

Adding a legend

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

Introducing the dataset

Arrays and NumPy

A closer look at the sectors

Filtering arrays

Summarizing sector data

Plot P/E ratios

Visualizing trends

Histogram of P/E ratios

Identify the outlier

Name the outlier

S&P 100 Case Study

Stocks data (I)

Stocks data (II)

S&P 100 data

Course Glossary

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.
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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.
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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.
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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.
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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.

The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.

The course glossary can be found on the right in the resources section.

To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

<h2>Learn the Fundamentals of Python for Finance</h2>
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.
<br><br>
<h2>Manipulate and Visualize Data with Python Packages</h2>
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.
<br><br>
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.
<br><br>
<h2>Perform Financial Analysis Using Python</h2>
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.
<br><br>
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.

Build Python skills to elevate your finance career. Learn how to work with lists, arrays and data visualizations to master financial analyses.

Python for Finance - Introduction

Introduction to Python for Finance

Finance Fundamentals in Python

Finance Fundamentals with CFI

Is data normally distributed?

Introduction to Python for Finance

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