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Manipulating Time Series Data in Python

In this course you'll learn the basics of working with time series data.

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4 Hours16 Videos55 Exercises38,281 Learners4700 XPFinance Fundamentals TrackTime Series Track

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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.

  1. 1

    Working with Time Series in Pandas


    This 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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    How to use dates & times with pandas
    50 xp
    Your first time series
    100 xp
    Indexing & resampling time series
    50 xp
    Create a time series of air quality data
    100 xp
    Compare annual stock price trends
    100 xp
    Set and change time series frequency
    100 xp
    Lags, changes, and returns for stock price series
    50 xp
    Shifting stock prices across time
    100 xp
    Calculating stock price changes
    100 xp
    Plotting multi-period returns
    100 xp

In the following tracks

Finance FundamentalsTime Series


loreLore DiricknicksolomonNick Solomon
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Stefan Jansen

Founder & Lead Data Scientist at Applied Artificial Intelligence

Stefan is the Founder & Lead Data Scientist at Applied Artificial Intelligence. He has 15 years of experience in finance and investments, with a big focus on emerging markets.
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