2-3 Milestone 1

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Southern New Hampshire University *

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Feb 20, 2024

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MILESTONE ONE 1 DAT 520 DECISION METHODS & MODELING December 9, 2023 2-3 Final Project Milestone One

MILESTONE ONE 2 I. Introduction The research question I chose for this project is “What in-game activity will most likely lead to winning in the National Football League?” I chose this topic because it was more relatable to my interests; I love sports and especially love football. There’s a saying that goes “Offense scores points, but defense wins championships,”; I feel this quote is relevant to the research question because it will allow me to analyze data and different in game techniques to see if there is any truth to this age old saying. “I feel the above question is very much an appropriate analysis technique-oriented question since the answer can only be aptly answered with the deep insight knowledge on understanding past data and the results will be easily determined at the end of analysis,” (SNHU, 2020). II. Data Appraisal The data set and information I will be using for research come from https://www.kaggle.com/datasets/kendallgillies/nflstatistics , https://www.kaggle.com/code/blueblowfish/nfl-data-analysis , and other articles from the Kaggle website. I will be using provided data in the PowerBI desktop also (see the screenshot below).

MILESTONE ONE 3 The “NFL Statistics” data set was originally intended to provide basic football stats and career stats for NFL players. I plan to analyze this data to find what in game activities lead to winning. The only limitations with this data set are that it also provides a lot of unnecessary data like player number, birthday, birthplace, etc. Once we eliminate what is irrelevant, the given data sets will provide a deep look at the important statistics like yards, passes, catches, sacks, wins, and more. “The identification of the column names and what it notifies and so on are really helpful for our analysis as we shouldn’t end up using the wrong columns for our research. Our analysis will fulfill the research question only when how effective

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MILESTONE ONE 4 we use the data and the important factors needed to be considered to complete this. A sample of the datasets is as shown, (SNHU, 2020). Data set utilizes reorganize, change, or compare data in the data set and are mostly used in batch jobs. The utilities allow us to manipulate data sets to gain desired outcomes (IBM, n.d.). III. Techniques The best steps to take when preparing data sets for analysis include Gather data, Discover & assess data, Cleanse & Validate data, Transform & Enrich data, and finally complete research (Talend, n.d.) . In this situation, data has already been gathered so we come in at step 2 and begin to access data. This step is about getting to know the data and understanding what has to be done before the data becomes useful in a particular context. The most important step in my opinion is the cleansing and

MILESTONE ONE 5 validation of data. As mentioned earlier, the provided data sets do include a lot of unneeded data, in this step we will take the opportunity to get rid of the stats we do not plan to analyze. IV. Defend & Evaluate The reasons behind the choice of these data sets and the research question are only after the thorough validation of the data sets available. As mentioned earlier, since I’m a sports enthusiast I was very much sided towards this topic. The research question as described is asking a query of how the games are won and what in game activities contribute to winning NFL games. When it comes to professional study and statistics, we need data models and techniques to make people understand the hidden outliers which will take the game to a win or loss status.

MILESTONE ONE 6 REFERENCES IBM documentation . (n.d.). https://www.ibm.com/docs/en/zos-basic-skills?topic=programs-data- set-utilities NFL statistics . (2017, June 9). Kaggle. https://www.kaggle.com/datasets/kendallgillies/nflstatistics/data SNHU. (2020). DAT 520 Milestone 1 . CourseHero. https://www.coursehero.com/u/file/71426547/DAT-520-Milestone-Onedocx/? userType=student Talend. (n.d.). What is Data Preparation? Processes and Example . Talend - a Leader in Data Integration & Data Integrity. https://www.talend.com/resources/what-is-data-preparation/

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