Workshop: 029 Foundations of Data Science 

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Prerequisites

Students should be comfortable with mathematics and coding.

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Workshop Description

Introduction to Data Analysis using Computational Methods: Data Analysis is an essential part of all STEM fields and the Social Sciences. Students learn how to evaluate data for validity, fit trendlines, and eventually analyze the complexity of data sets during this course. How many variables does the data stream depend on? What kind of reliable predictions can be made from a particular data set? How many data points are needed to make reliable predictions? At what data acquisition rate is required in order to avoid artifacts or unnecessary crowding of storage space. What computational tools are most effective? Computational tools used will be Python, R, and Excel.

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Sample Research Topics

• Scientific Understanding of Learning, Especially Deep Learning Algorithms.

• Inferring From Noisy and Incomplete Data.

• How to computing Systems for Data-Intensive Applications

• Building large scale generative based conversational systems (Chatbot frameworks)

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Past Students Research Projects

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