Course

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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  • DAYS
  • HRS
  • MIN
  • SEC
  • Estimated Time
    21 Hours

  • Enroll by
    September 22, 2021

    Get access to classroom immediately on enrollment

  • Prerequisites
    Python, Statistics, Machine Learning
In collaboration with
  • IBM Watson

What You Will Learn

Syllabus

Experimental Design & Recommendations

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.

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.

  • 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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Data Scientist roles are growing by 45% year over year!

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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 Courses Include

Real-world projects from industry experts

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

Technical mentor support

Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you and keeping you on track.
Workspaces

Workspaces to see your code in action

Validate your understanding of concepts learned by checking the output and quality of your code in real-time.
Flexible learning program

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 OfferingsFull list of offerings included:
Enrollment Includes:
Class Content
Real-world projects
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Project reviews
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Project feedback from experienced reviewers
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We provide services customized for your needs at every step of your learning journey to ensure your success!
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2.7M projects reviewed
88/100 reviewer rating
1.1 hours avg project review turnaround time
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  • Unlimited submissions and feedback loops
  • Practical tips and industry best practices
  • Additional suggested resources to improve
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Mentors by the numbers
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0.85 hours median response time
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  • Questions answered quickly by our team of technical mentors

Experimental Design & Recommendations

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  • Monthly Access

    Pay as you go


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    • Maximum flexibility to learn at your own pace.
    • Cancel anytime.
  • 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 21 hours to complete this program.
  • Benefits include

    • Real-world projects from industry experts
    • Technical mentor 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.
    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 21 hours.

    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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