Skip to main content

Dimensionality Reduction in Python

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

Start Course for Free
4 Hours16 Videos58 Exercises18,281 Learners4700 XPMachine Learning Scientist Track

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

Course Description

High-dimensional datasets can be overwhelming and leave you not knowing where to start. Typically, you’d visually explore a new dataset first, but when you have too many dimensions the classical approaches will seem insufficient. Fortunately, there are visualization techniques designed specifically for high dimensional data and you’ll be introduced to these in this course. After exploring the data, you’ll often find that many features hold little information because they don’t show any variance or because they are duplicates of other features. You’ll learn how to detect these features and drop them from the dataset so that you can focus on the informative ones. In a next step, you might want to build a model on these features, and it may turn out that some don’t have any effect on the thing you’re trying to predict. You’ll learn how to detect and drop these irrelevant features too, in order to reduce dimensionality and thus complexity. Finally, you’ll learn how feature extraction techniques can reduce dimensionality for you through the calculation of uncorrelated principal components.

  1. 1

    Exploring High Dimensional Data


    You'll be introduced to the concept of dimensionality reduction and will learn when an why this is important. You'll learn the difference between feature selection and feature extraction and will apply both techniques for data exploration. The chapter ends with a lesson on t-SNE, a powerful feature extraction technique that will allow you to visualize a high-dimensional dataset.

    Play Chapter Now
    50 xp
    Finding the number of dimensions in a dataset
    50 xp
    Removing features without variance
    100 xp
    Feature selection vs. feature extraction
    50 xp
    Visually detecting redundant features
    100 xp
    Advantage of feature selection
    50 xp
    t-SNE visualization of high-dimensional data
    50 xp
    t-SNE intuition
    50 xp
    Fitting t-SNE to the ANSUR data
    100 xp
    t-SNE visualisation of dimensionality
    100 xp
  2. 2

    Feature Selection I - Selecting for Feature Information

    In this first out of two chapters on feature selection, you'll learn about the curse of dimensionality and how dimensionality reduction can help you overcome it. You'll be introduced to a number of techniques to detect and remove features that bring little added value to the dataset. Either because they have little variance, too many missing values, or because they are strongly correlated to other features.

    Play Chapter Now
  3. 4

    Feature Extraction

    This chapter is a deep-dive on the most frequently used dimensionality reduction algorithm, Principal Component Analysis (PCA). You'll build intuition on how and why this algorithm is so powerful and will apply it both for data exploration and data pre-processing in a modeling pipeline. You'll end with a cool image compression use case.

    Play Chapter Now

In the following tracks

Machine Learning Scientist


chesterChester Ismayhadrien-d4e73b49-bc29-46b7-a485-2f598f38e3b9Hadrien Lacroixhillary-green-lermanHillary Green-Lerman
Jeroen Boeye Headshot

Jeroen Boeye

Machine Learning Engineer @ Faktion

Jeroen is a machine learning engineer working at Faktion, an AI company from Belgium. He uses both R and Python for his analyses and has a PhD background in computational biology. His experience mostly lies in working with structured data, produced by sensors or digital processes.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA