Do I need to apply? What are the admission criteria?

No. This Course accepts all applicants regardless of experience and specific background.

What are the prerequisites for enrollment?

Machine Learning: Supervised and Unsupervised methods equivalent to those taught in the Intro to Machine Learning Nanodegree Program. Python: Experience with Python Programming including writing functions, building basic applications, and common libraries like NumPy and pandas SQL programming including querying databases, using joins, aggregations, and subqueries Comfortable using the Terminal and Github Probability and Statistics: Descriptive Statistics including calculating measures of center and spread Inferential Statistics including sampling distributions, hypothesis testing

How is this course structured?

The Experimental Design & Recommendations course is comprised of content and curriculum to support one project. We estimate that students can complete the program in 1 month. The project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.

How long is this course?

Access to this course runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our programs.

Can I switch my start date? Can I get a refund?

Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.

What software and versions will I need in this course?

You’ll need access to the Internet, and a 64 bit computer. Additional software: need to be able to download and run Python 3.7

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.

Enroll Now

Estimated time
1 month
Enroll by
June 14, 2023
Get access to classroom immediately on enrollment
Skills acquired
Interpreting Test Results, Recommendation Engine Fluency,

In collaboration with

What You Will Learn

Experimental Design & Recommendations
1 month 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
Python, Statistics, Machine Learning.
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.
- Data Scientist roles are growing by 45% year over year!

All Our Courses Include

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.
Real-time support
On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.
Workspaces
Validate your understanding of concepts learned by checking the output and quality of your code in real-time.
Flexible learning program
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
- Student community
- Real-time support

Succeed with personalized services.

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

Experienced Project Reviewers
Real-Time Support

Get timely feedback on your projects.

Personalized feedback
Unlimited submissions and feedback loops
Practical tips and industry best practices
Additional suggested resources to improve

1,400+
project reviewers
2.7M
projects reviewed
88/100
reviewer rating
1.1 hours
avg project review turnaround 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.