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DS 5100 Course (Fall 2021)

Programming for Data Science

The objective of this course is to introduce basic data analysis techniques including data analysis at scale. Students will be introduced to essential programming techniques in Python, an increasingly prominent language for data science and “big data” manipulation.

This course is project based, consisting of a semester project and final project presentations.

Evaluation Rubric

1. Introduction: Describe the project scenario.

2. An appropriate data set was used.

3. Appropriate data structures are used.

4. Data pre-processing.

5. Data Analysis / Data Processing.

6. Testing: Describe any test-driven development and/or unit tests.

7. Results displayed appropriately for each test.

8. Explanation of Results & Conclusions.

9. Presentation skills and video.

Course Project Presentations

EDA of regulated banks based

on FDIC data

Group 1

Dima Mikhaylov, Lauren Bassett, Cullan Bedwell, Casey Nguyen



Behavioral Risk Factor

Surveillance System

Group 3

Andy Ortiz, Uyen Nguyen, Lee Ann Johnson, JD Pinto

Analysis on COVID-19 Patients
with Pre-existing Conditions

Group 5
Seth Galluzzi, Connie Cui, Alex Bass, Peumali Surani

Natural Disasters

Group 7

Haley Egan, Maggie Houck, Will Johnson, Diana Morris

Food Environments

Group 2

Maxwell Alexander Jones, Kaia Lindberg, Grace Lyons, Mani Shanmugavel

Quantifying the Home-field

Advantage in the NFL

Group 4

Anoop Nath, Marin Lolic, Cepehr Alizadeh, Seth Harrison

Tokyo Olympics
Group 6
Reilly Meinert, Max Ryoo, Said Mrad, Sydney Masterson

Key factors leading to hesitancy

for the COVID vaccine

Group 8

Emma Cooper, Aishwarya Pradhan, Charlie Barry, Christopher Lee



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