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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
In collaboration with
  • IBM Watson

What You Will Learn

  1. 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.

      • 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.

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              Introducing new Udacity Single Courses

              Our students asked and we listened. You can now get the in-demand tech skills you need faster and for less money by enrolling in one of our new, one-month Single Courses. You’ll get the specific job-ready skills you need in as little as four weeks and for a fraction of the cost.

              Of course if you are looking for a more robust, in-depth education, you can still enroll in one of our 3-6 month Nanodegree programs.

              Both programs are part-time and online, and they both offer 24/7 support, quality Udacity-produced content, courses created with the help of top tech companies, and more. You can always start with a Single Course and upgrade to a full Nanodegree program if you like.

              All our programs 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.

              • 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.

              • 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

                • Technical mentor support
                • Student community

              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

              Mentors available to answer your questions.

              • Support for all your technical questions
              • Questions answered quickly by our team of technical mentors
              • 1,400+

                technical mentors

              • 0.85 hours

                median response 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

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              Experimental Design & Recommendations

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