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Experimental Design & Recommendations


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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  • 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
  • IBM Watson

What You Will Learn

  1. 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.

      • Statistical Concerns of Experimentation

        Learch about Applications of statistics in the real world, establishing key metrics and SMART experiments: Specific, Measurable, Actionable, Realistic, Timely.

        • A/B Testing

          Learn about sources of Bias: Novelty and Recency Effects and Multiple Comparison Techniques (FDR, Bonferroni, Tukey).

          • 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.

            • 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.

              • 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.

              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.

              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.

              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.

              Experimental Design & Recommendations

              Get started today

                • Learn

                  How to run AB tests that produce clean, interpretable results and build personalized recommendation engines that keep users engaged.

                • Average Time

                  On average, successful students take 1 month to complete this program.

                • Benefits include

                  • Real-world projects from industry experts
                  • Real-time support

                Program Details

                • 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.
                  • 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

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