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Intermediate Python for Finance

Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.

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4 Hours15 Videos52 Exercises15,042 Learners4150 XPFinance Fundamentals in Python Track

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Course Description

Are you a financial or business analyst, or simply looking for an easier way to manage your stock portfolio? If so learning Python can automate financial tasks such as calculating risk, mapping market health, and visualizing a stock's price trends, saving you time and money. In this course, you’ll learn how to use Python data structures, execution control statements, and DataFrames to manipulate financial data. You will then work with pandas, using data from the Federal Reserve Bank, to explore national economic trends—an essential part of understanding investment strategies. You will also calculate risk based on stock price data, and display this data in easy to read plots. By the end of this course, you’ll be the new Python of Wall Street.
  1. 1

    Datetimes and Dictionaries

    Free

    In this chapter, you’ll learn how to create and manipulate Python datetime objects to help you identify key financial events, such as Black Friday. You’ll also learn how to store and efficiently look up items using Python dictionaries.

    Play Chapter Now
    Representing time with datetimes
    50 xp
    Creating datetimes for dates
    100 xp
    Datetimes from strings
    100 xp
    Converting format with datetimes
    100 xp
    Working with datetimes
    50 xp
    Accessing datetime attributes
    100 xp
    Comparing datetimes
    100 xp
    Making relative datetimes
    100 xp
    Dictionaries
    50 xp
    Creating and accessing dictionaries
    100 xp
    Accessing safely and deleting
    100 xp
  2. 2

    Control Flow and Logic

    Through hands-on activities, you’ll discover how to use Boolean logic to determine truth and use comparison and equality operators to control execution in if-statements and loops.

    Play Chapter Now
    Comparison operators
    50 xp
    Equality across types
    50 xp
    Assignment and equality
    100 xp
    Comparing dividends
    100 xp
    Boolean operators
    50 xp
    Decisions with Boolean operations
    100 xp
    Assigning variables with Boolean operators
    100 xp
    Negating with Boolean operators
    100 xp
    If statements
    50 xp
    Control statements
    50 xp
    Comparing sales and purchases
    100 xp
    Branching with elif and else
    100 xp
    For and while loops
    50 xp
    Breaking out of a for loop
    100 xp
    Controlling loop execution
    100 xp
  3. 3

    Pandas Dataframe

    Discover how to create and access DataFrames with pandas using financial data from other data structures, including Dicts, lists, and CSV files. You’ll then uncover additional insights, as you aggregate data across rows or columns, calculate averages, and extend your data using functions.

    Play Chapter Now
    Creating a DataFrame
    50 xp
    Creating DataFrames
    100 xp
    Reading market history
    100 xp
    Accessing Data
    50 xp
    Accessing using names
    100 xp
    Accessing using indexes
    100 xp
    Aggregating and summarizing
    50 xp
    Mean prices
    50 xp
    Median prices
    100 xp
    Extending and manipulating data
    50 xp
    Creating new columns
    100 xp
    Dropping columns from DataFrame
    100 xp
    Manipulating data with Pandas
    100 xp
  4. 4

    Working with NASDAQ Stock Data

    In this final chapter, you’ll try your hand at working with real-world NASDAQ stock data as you learn how to interpret new data, create masks to filter data, and visualize your findings with plots.

    Play Chapter Now
    Peeking at data with head, tail, and describe
    50 xp
    Why use describe
    50 xp
    Peek at top and bottom
    100 xp
    Describing data
    100 xp
    Filtering data
    50 xp
    Why filter data
    50 xp
    Filtering stock data.
    100 xp
    Selecting data from data range
    100 xp
    Plotting data
    50 xp
    Identifying plot type
    50 xp
    Making a line plot
    100 xp
    Choose kind of plot
    100 xp
    Wrapping up
    50 xp

In the following tracks

Finance Fundamentals in Python

Datasets

Alphabet stock

Collaborators

Adel Nehme

Prerequisites

Introduction to Python for Finance
Kennedy Behrman Headshot

Kennedy Behrman

Data Engineer, Author, Founder

Kennedy Behrman is a Senior Data Engineer at Harvest Savings and Wealth Technologies. He began using Python managing data pipelines in the film industry. Since then he has been involved in startups oriented around machine learning, cloud computing, social network prediction, and financial services.
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