Stefan Jansen
Founder & Lead Data Scientist at Applied Artificial Intelligence
Stefan has applied data science to business, investment and policy decisions for over 15 years. He has built an early warning system for financial crises, and worked as advisor to Central Banks and the World Bank before moving into fintech. He is founder and lead data scientist at Applied Artificial Intelligence, and instructor at General Assembly.
See MoreCollaborator(s)
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Lore Dirick
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Nick Solomon
Datasets
Course Description
In this course you'll learn the basics of manipulating time series data. Time series data are data that are indexed by a sequence of dates or times. You'll learn how to use methods built into Pandas to work with this index. You'll also learn how resample time series to change the frequency. This course will also show you how to calculate rolling and cumulative values for times series. Finally, you'll use all your new skills to build a value-weighted stock index from actual stock data.
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1
Working with Time Series in Pandas
FreeThis chapter lays the foundations to leverage the powerful time series functionality made available by how Pandas represents dates, in particular by the DateTimeIndex. You will learn how to create and manipulate date information and time series, and how to do calculations with time-aware DataFrames to shift your data in time or create period-specific returns.
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Basic Time Series Metrics & Resampling
This chapter dives deeper into the essential time series functionality made available through the pandas DataTimeIndex. It introduces resampling and how to compare different time series by normalizing their start points.
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Window Functions: Rolling & Expanding Metrics
This chapter will show you how to use window function to calculate time series metrics for both rolling and expanding windows.
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Putting it all together: Building a value-weighted index
This chapter combines the previous concepts by teaching you how to create a value-weighted index. This index uses market-cap data contained in the stock exchange listings to calculate weights and 2016 stock price information. Index performance is then compared against benchmarks to evaluate the performance of the index you created.
