Experimental Design & Recommendations

Course

Learn two of the most in-demand skills in the entire field of data science! By the end of this course, you’ll know how to generate personalized recommendations based on user data, as well as run statistically valid tests that produce clean, interpretable results.

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06Days04Hrs31Min40Sec

Estimated time
21 Hours
Enroll by
October 12, 2022
Get access to classroom immediately on enrollment
Prerequisites
Python, Statistics, Machine Learning

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What You Will Learn

Experimental Design & Recommendations
21 hours to complete
The world’s leading tech companies — including Amazon, Netflix, and Spotify — all use recommendation engines to engage their users and experiments to improve their products. In this course, you’ll get a comprehensive breakdown of the techniques and considerations that go into building these systems (including pitfalls that invalidate your results if you’re not careful!). You’ll also complete two hands-on projects using real data from Starbucks and IBM’s Watson Studio platform.
Prerequisite knowledge
1. Experiment Design
  Understand how to set up an experiment and the ideas associated with experiments vs. observational studies.
2. Statistical Concerns of Experimentation
  Learch about Applications of statistics in the real world, establishing key metrics and SMART experiments: Specific, Measurable, Actionable, Realistic, Timely.
3. A/B Testing
  Learn about sources of Bias: Novelty and Recency Effects and Multiple Comparison Techniques (FDR, Bonferroni, Tukey).
4. Introduction to Recommendation Engines
  Distinguish between common techniques for creating recommendation engines including knowledge based, content based and collaborative filtering based methods and implement each of these techniques in Python.
5. Matrix Factorization for Recommendations
  Understand the pitfalls of traditional methods and pitfalls of measuring the influence of recommendation engines under traditional regression and classification techniques.
6. Course Project: Design a Recommendation Engine with IBM
  IBM has an online data science community where members can post tutorials, notebooks, articles and datasets. In this project, you will build a recommendation engine, based on user behavior and social network in IBM Watson Studio’s data platform, to surface content most likely to be relevant to a user.

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Real-world projects from industry experts
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
Technical mentor support
Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you, and keeping you on track.
Workspaces
Validate your understanding of concepts learned by checking the output and quality of your code in real-time.
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Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.

Course offerings

Class content
- Real-world projects
- Project reviews
- Project feedback from experienced reviewers
Student services
- Technical mentor support
- Student community

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We provide services customized for your needs at every step of your learning journey to ensure your success.

Get timely feedback on your projects.

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Additional suggested resources to improve

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2.7M
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88/100
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1.1 hours
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Support for all your technical questions
Questions answered quickly by our team of technical mentors

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technical mentors
0.85 hours
median response time

Learn with the best.

Josh Bernhard
Data Scientist at Nerd Wallet
Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.
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.