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Students will learn the fundamentals of an end-to data science workflow in Jupyter - including Jupyter tips, tricks, and best practices. We'll also discuss various modeling techniques and their implementations in Python.
Juypter users looking to learn the fundamentals of an end-to data science workflow within Juypter.
We expect students to have some programming experience and basic familiarity with math, such as linear algebra.
Day 1: Recommendation Engine
– Overview of data + its wrangling
– Item-item correlations + finding similar items
– User similarity + predicting user ratings
– Collaborative filtering
– Evaluating model performance
Day 2: Anomaly Detection
– Data format and goal
– Limitations of time-series data
– De-trending and seasonality
– Windowing and local scores
– Setting thresholds for classification
– Online learning
Christian Moscardi has lived in NYC for the past 6 years, having previously developed a CMS for food blogs, worked for Google, and researched and taught at Columbia. Extracurricular activities include cooking, piano, and exploring New York.
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