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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.
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.
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.
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