Experimental Design and Recommendations

Course

Learn to design experiments and analyze A/B test results. Explore approaches for building recommendation systems.

Built in collaboration with

IBM

Advanced

1 month

Real-world Projects

Completion Certificate

Last Updated February 17, 2024

Skills you'll learn:

Interpreting test results • Smart experiments • Experiment control • Recommendation engine fluency

Prerequisites:

Basic statistical modeling • Data wrangling • Python for data science

Course Lessons

Lesson 1

Intro to Experimental Design & Recommendations Engines

Why do we care about experiment design and recommendation engines? In this lesson, you'll get an overview of the topics you'll learn in this course.

Lesson 2

Concepts in Experiment Design

In this lesson, you will learn about conceptual topics that must be considered when designing and running an experiment, in order to ensure good, interpretable results.

Lesson 3

Statistical Considerations in Testing

In this lesson, you will learn how statistics can be used to benefit the design of an experiment, as well as additional statistical tests that can be used to analyze results.

Lesson 4

A/B Testing Case Study

In this lesson, you will go through an A/B Testing case study to see how the conceptual and statistical concepts covered in the previous lessons can be applied in experiment designs.

Lesson 5

Portfolio Exercise: Starbucks

In this lesson, you will analyze data that was originally used in screening interviews for data scientists at Starbucks.

Lesson 6

Introduction to Recommendation Engines

In this lesson, you will learn about the different methods used to create recommendation engines.

Lesson 7

Matrix Factorization for Recommendations

In this lesson, you will learn how machine learning is being used to make recommendations.

Lesson 8 • Project

Project: Recommendation Engines

Put your skills to work to make recommendations for IBM Watson Studio's data platform.

Taught By The Best

Josh Bernhard

Staff Data Scientist

Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.

Mike Yi

Data Analyst Instructor

Mike is a content developer with a multidisciplinary academic background, including math, statistics, physics, and psychology. Previously, he worked on Udacity's Data Analyst Nanodegree program as a support lead.

The Udacity Difference

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Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

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Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.