Skip to main content

Machine Learning for Marketing Analytics in R

In this course you'll learn how to use data science for several common marketing tasks.

Start Course for Free
4 Hours17 Videos60 Exercises10,730 Learners
4200 XP

Create Your Free Account

GoogleLinkedInFacebook

or

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

This is your chance to dive into the worlds of marketing and business analytics using R. Day by day, there are a multitude of decisions that companies have to face. With the help of statistical models, you're going to be able to support the business decision-making process based on data, not your gut feeling. Let us show you what a great impact statistical modeling can have on the performance of businesses. You're going to learn about and apply strategies to communicate your results and help them make a difference.

  1. 1

    Modeling Customer Lifetime Value with Linear Regression

    Free

    How can you decide which customers are most valuable for your business? Learn how to model the customer lifetime value using linear regression.

    Play Chapter Now
    Customer lifetime value in CRM
    50 xp
    Benefits of knowing CLV
    50 xp
    Looking at data
    100 xp
    Simple linear regression
    50 xp
    Understanding residuals
    50 xp
    Estimating simple linear regression
    100 xp
    Multiple linear regression
    50 xp
    Avoiding multicollinearity
    100 xp
    Interpretation of coefficients
    50 xp
    Model validation, model fit, and prediction
    50 xp
    Interpretation of model fit
    50 xp
    Future predictions of sales
    100 xp
  2. 4

    Reducing Dimensionality with Principal Component Analysis

    CRM data can get very extensive. Each metric you collect could carry some interesting information about your customers. But handling a dataset with too many variables is difficult. Learn how to reduce the number of variables in your data using principal component analysis. Not only does this help to get a better understanding of your data. PCA also enables you to condense information to single indices and to solve multicollinearity problems in a regression analysis with many intercorrelated variables.

    Play Chapter Now

In the following tracks

Marketing Analytics

Collaborators

Nick SolomonChester Ismay
Verena Pflieger Headshot

Verena Pflieger

Data Scientist at INWT Statistics

Data analytics was already part of Verena’s skill set during her studies of political and administrative science and statistics. Since earning her degree in 2014 Verena has been applying her experience and expertise in training and data science to her work at INWT.
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