Track
Learn how to extract meaningful insights from time series data in R. Explore how to model, forecast, and visualize time series data.
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Course
Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.
Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.
Course
Learn the core techniques necessary to extract meaningful insights from time series data.
Learn the core techniques necessary to extract meaningful insights from time series data.
Course
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Course
Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.
Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.
Course
Learn how to visualize time series in R, then practice with a stock-picking case study.
Learn how to visualize time series in R, then practice with a stock-picking case study.
Time Series in R
Track
Complete
Associate Professor at Cornell University
This track is suitable for beginners. No prior knowledge is necessary, but if you have some basic familiarity with R programming, it will deepen your understanding of time series analysis.
This Track requires knowledge of R programming.
Jobs in financial markets, activities that involve critical infrastructure or utilities, and transport and logistics can all benefit from this Track.
This Track will equip you with the skills to analyze, interpret, and forecast time series data. You will develop a strong understanding of R programming as well as knowledge of important libraries, concepts, and methods.
This Track normally takes approximately 25 hours to complete.
A skill track focuses on teaching domain-specific technical skills and the necessary knowledge to build a successful career, whereas a career track focuses on developing deeper expertise for a given profession.
The datasets used in this Track are generally created from real-world examples.
To get the most out of this Track, you'll need knowledge of important R libraries, such as xts and zoo.
The tracks helped me complete my journey without feeling lost. Each course builds on the last, keeping me motivated and on track
DataCamp helped me transition from someone curious about data to someone actively applying these skills in my job
I've been using DataCamp for four years, and it's helped me transition from filling gaps in my skills to proactively creating value for my company
