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.
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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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.
Python, Statistics, Machine Learning.
Understand how to set up an experiment and the ideas associated with experiments vs. observational studies.
Learch about Applications of statistics in the real world, establishing key metrics and SMART experiments: Specific, Measurable, Actionable, Realistic, Timely.
Learn about sources of Bias: Novelty and Recency Effects and Multiple Comparison Techniques (FDR, Bonferroni, Tukey).
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.
Understand the pitfalls of traditional methods and pitfalls of measuring the influence of recommendation engines under traditional regression and classification techniques.
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.
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.
Validate your understanding of concepts learned by checking the output and quality of your code in real-time.
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.
We provide services customized for your needs at every step of your learning journey to ensure your success.
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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 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.
How to run AB tests that produce clean, interpretable results and build personalized recommendation engines that keep users engaged.
On average, successful students take 1 month to complete this program.
No. This Course accepts all applicants regardless of experience and specific background.
Machine Learning:
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.
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.
Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.
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