A DataCamp course on the statistical modelling side of time series — correlation and autocorrelation, white noise and random walks, AR, MA and ARMA models, and cointegration. Hands-on in Python, it suits people with some Python and statistics who want to model time-dependent data properly.
Good for: Statistical modelling of time-series data in Python.
Less suitable if: You lack Python/statistics or want forecasting with ML only.
Skills you'll gain
Time seriesARIMAAutocorrelationStatistical modellingPythonForecasting
Is this course right for you?
A good fit if you…
You know Python and some statistics
You work with time-dependent data
You want proper time-series models
Consider something else if you…
You lack Python or statistics
You want ML-only forecasting
You already model time series
Requirements: Python and basic statistics.
Realistic time: Around 4 hours.
About this course
Time Series Analysis in Python covers the statistical modeling side of time series: correlation and autocorrelation, white noise and random walks, autoregressive (AR) and moving average (MA) models, combined ARMA models, and cointegration for modeling two series jointly — with examples weighted heavily toward finance (stock prices, interest rates, bonds) alongside a closing climate-data case study.
What you'll learn
Understand correlation and autocorrelation in time series
Distinguish white noise, random walks, and stationarity
Build and forecast with autoregressive (AR) models
Build and forecast with moving average (MA) and ARMA models
Apply cointegration models to jointly analyze two series
Apply time series methods to real finance and climate data
This course includes
4h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
What it costs
DataCamp runs on a subscription — roughly $14/month billed annually (more month-to-month), with the first chapter of each course free to try. A certificate of completion is included with the subscription.
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