FeaturedUdemy
Importing and Managing Financial Data in Python
4.8(62)45k studentsINTERMEDIATEEnglish
Korshub's take
Overview
This intermediate Python course teaches you how to retrieve, clean, and analyze financial time series data. You learn to exchange data between Excel and pandas, import stock prices and exchange rates from APIs (Google, Yahoo! Finance, OANDA, Federal Reserve), compute returns over custom time frames, examine IPO performance by sector, and evaluate correlations.
Who It Is For
Financial analysts, data scientists, and quantitative researchers wanting to build automated financial data pipelines in Python.
Key Takeaways
- Transfer financial datasets seamlessly between Excel spreadsheets and pandas DataFrames.
- Fetch market prices, exchange rates, and economic indicators from online APIs and financial data feeds.
- Calculate asset returns across multiple time horizons and compute statistical correlations across market sectors.
About this course
If you want to apply your new 'Python for Data Science' skills to real-world financial data, then this course will give you some very valuable tools.
First, you will learn how to get data out of Excel into pandas and back. Then, you will learn how to pull stock prices from various online APIs like
Google or Yahoo! Finance, macro data from the Federal Reserve, and exchange rates from OANDA. Finally, you will learn how to calculate returns for various time horizons,
analyze stock performance by sector for IPOs, and calculate and summarize correlations.
Price
Free
This course is free to enrol.
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