Intermediate 4 hours English Completion Certificate
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.
The honest take: this is the analytical core of a 5-course Time Series with Python track, and DataCamp lists a real prerequisite (Manipulating Time Series Data in Python) — it's not a standalone beginner course, but a focused statistics module for people already comfortable handling time-indexed data in pandas.
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
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