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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 Exercises9,931 Learners
4150 XP

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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


    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.

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    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
    50 xp
    Creating and accessing dictionaries
    100 xp
    Accessing safely and deleting
    100 xp
  2. 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.

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  3. 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.

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In the following tracks

Finance Fundamentals


Adel Nehme
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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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA