Data Science Training For UX Designers

SEATTLE

What You’ll Learn

INTRODUCTION TO R PROGRAMMING

Introducing R
R data types
Basic opertors
Reading writing data
Plotting in R

EXPLORING AND VISUALIZING USER DATA IN R

Why explore and visualize user data?
Reading user and session data from Google Analytics using R
Pie charts, box plots, histograms, density plots, scatter plots
Why is segmentation of data important? Slicing and dicing data to demystify user behavior
Advanced exploration and visualization techniques

INTRODUCTION TO ONLINE EXPERIMENTATION AND A/B TESTING

Why A/B testing or multi-variate testing is needed?
Looking at some interesting A/B tests
Understanding concepts of A/B and multi-variate testing
Statistical fundamentals for A/B testing
Some common pitfalls of A/B testing

PROJECT

Setup a landing page
Hook up Google analytics with the landing page
Setup an A/B test on a landing page (could be as simple as two different colors of buttons on the page)
Seed it with some social media advertising budget
Analyze user behavior using the exploration and visualization techniques we learned on day 1
Analyze any differences in user behavior between A and B versions

Exclusive Alumni Resources

Course Material

Receive four handbooks authored by our data scientists and engineers and three best-sellers to help you keep learning.

Online Portal

Exclusive access to our online learning portal that contains advanced topics, tutorials, code samples, and reference material

Mentoring

Our world-class mentoring team will help you work on a specific design project.

Networking

Stay connected with our alumni spread across the world for collaboration and career opportunities!

Alumni Love Us

5.0

on SwitchUp

4.8

on Course Report

“I’ve never learned more from an external training, especially in such a short time. The instructors are excellent – clearly experienced and passionate about data and what it can do for attendees. I would highly recommend the experience to anyone who has or is thinking they may have a question about data and what can be done with it.”