Intermediate Python for Finance
Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
- 4 hours
- 15 Videos
- 52 Exercises
- 1,570 Participants
- 4,150 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.
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1
Datetimes and Dictionaries
FreeIn 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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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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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.
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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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1
Datetimes and Dictionaries
FreeIn 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.
-
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.
-
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.
-
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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Lloyd's Banking Group
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Harvard Business School
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Decision Science Analytics @ USAA
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.
See MoreCollaborators
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Adel Nehme