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Final Project: Python

NFL Fantasy Football Data Analysis

Goals:

  • Evalute data on a week by week basis for individual players
  • Group data to evaluate postion performance, individual performance, performance by day of week, and performance by week or season.
  • Will eventually be able to build into a bigger project with a GUI on a website for individuals to evaluate the data themselves by inputing options.
  • Data was scrapped from Pro Football Reference (https://www.pro-football-reference.com/)

Project due date: 7/23/2020, at 5:00 PM EST

You must be available during the class time to present your project.

Presentation structure (7-10 min)

  • Share your URL
  • What is your project?
  • Demo
  • What were the hurdles (Perhaps technology issues)?
  • Any advice that you can give to your classmates
  • QA

Overview

Let's kick it up a notch and build a modular and modern application using Python. You must submit your full project proposal and dataset to your instructional team by 6:00 PM EST Thursday (7/16/2020). Show us what you've got!

NOTE: Send us a Slack DM message, CC: Britt, Vonn, and Suresh

Requirements

Data Science project must:

  • Be a working, interactive, Python application
  • Must include grouping and data aggregation, and standard functions such as max, min, sum, and std etc
  • Must have at least 2 .apply methods()
  • Must have data visualization

NOTE: YOU'RE NOT ALLOWED TO RECREATE PROJECT 1 OR PROJECT 2

Your code must:

  • Be properly indented
  • Be written with semantic Python code
  • Be commented for the other developers

Necessary Deliverables

A Killer Project Proposal:

  • What is your project is all about
  • What are the three classes (or more), be ready to discuss it's functionality
  • If it's Data Science project, you must provide the link to the data set prior to the project approval

For the project:

  • Regadless of your what type of application you intend to build it must have a GitHub repo(USE YOUR PERSONAL GITHUB REPO)
  • Data Science project must include the dataset in the GitHub repo along with the Jupyter Notebook
  • DO NOT UPLOAD THE PROJECT FILES TO GITHUB, you must push your code the GitHub as you build the application

A Note on Plagiarism

Take a moment to re-familiarize yourself with the plagiarism policy, specifically on using work you find online and on work you do with other students.

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