Data science courses

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

In this course youll learn how to perform inference using linear models.

Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.

Learn to set up a secure, efficient book recommendation app in Azure in this hands-on case study.

Learn all about how DataCamp builds the best platform to learn and teach data skills.

This course is for R users who want to get up to speed with Python!

Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.

In this course, you’ll learn to classify, treat and analyze time series; an absolute must, if you’re serious about stepping up as an analytics professional.

Explore HR data analysis in Tableau with this case study.

Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.

This course will show you how to combine and merge datasets with data.table.

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

In ecommerce, increasing sales and reducing expenses are top priorities. In this case study, youll investigate data from an online pet supply company.

Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.

Learn to use the Bioconductor package limma for differential gene expression analysis.

Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.

Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.

Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.

In this course youll learn how to leverage statistical techniques for working with categorical data.

Learn how to visualize time series in R, then practice with a stock-picking case study.

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.

Learn how to access financial data from local files as well as from internet sources.

Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!

Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.