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.
Experimental Design & Recommendations
Course
Learn to design experiments and analyze A/B test results. Explore approaches for building recommendation systems.
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 November 30, 2022
Interpreting test results • Smart experiments • Experiment control • Recommendation engine fluency
Basic statistical modeling • Data wrangling • Python for data science
Course Lessons
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.
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