DAT 205 Module Four Data Analytics Lifecycle

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Southern New Hampshire University *

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205

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Industrial Engineering

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Jan 9, 2024

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DAT 205 Module Four Data Analytics Lifecycle Mary Flowers Instructions Fill in the tables below for each section. The tables will expand as you type. You may also insert images into the tables by using the copy and paste or Insert Picture features. Create a diagram of the phases of the data analytics lifecycle (DAL). Briefly describe the key points of what occurs during each phase. The Data Analytics Lifecycle is made up of six stages. 1. Discovery: The finding phase is the first step in phase one. During the discovery process, the data science team learns about and investigates the problem, builds context, and gets a better idea of what's happening. They know which data sources are needed and available for the job because they understand the problem. The team then develops an initial theory that can be checked with data later. 2. Data Preparation: In the second step of preparing data, steps are taken to study, preprocess, and condition the data before modeling and analysis. It needs an analytical sandbox, and the team has to process, load, and change information to get data into the sandbox. Tasks will likely be done more than once and may not always be in the same order. 3. Model Planning: In phase three of model planning, the team explores data to learn about relationships between variables and chooses key variables and the most suitable models. The 1
data science team develops data sets for training, testing, and production in this step. 4. Model Building: In step four, the team creates datasets that can be used for testing, training, and production. The team also thinks about whether the tools they already have are enough to run the models or if they need a more robust setting to run the models. 5. Communicate Results: In step five, after the model has been run, the team must compare the model results to the success and failure criteria set up during model planning. Considering alert assumptions, the team thinks about how to explain findings and results to other team members and stakeholders, in this case, my supervisor and Two Sisters, Inc., in the best way. The team will pick out the most critical findings, figure out how much those findings are worth to the business, and write a story to sum up and explain the findings to stakeholders. 6. Operationalize: The team communicates on a broader scale about what the project will do for them and sets up a pilot project so that work can be controlled before it is done for the whole company of users. This method allows the team to see how the model works in the production setting on a small scale and make changes before deploying it fully. The team sends final reports, explanations, and codes to the supervisors and Two Sisters, Inc. Select one phase of the DAL. Discovery – Phase one Describe a data analyst’s role in your chosen phase. Let’s discuss the first phase of the Data Analytics Lifecycle - discovery! In this exciting phase, the role of the data analyst is to embark on a journey of learning and exploration. They dive deep into the problem, gaining valuable context and understanding. With keen eyes, they uncover the treasure trove of data sources required and already at their disposal that can later be tested with data. Delving deep into the problem will allow the analyst to gain beneficial context and understanding to determine the geographical areas of current donors and those with little to no donors. This will allow the analyst to overlay social media and call record data to determine additional data. The analyst must review the tools and technology to ensure that future phases have the right resources and don't have to go back and rethink the research phase. For the project to be successful in the long run, the process may require the team to think about a job or skill that doesn't exist in the company but would need to be acquired if necessary. Even though the discovery process may take a lot of time, it is vital to determine what the key stakeholders want and how they might gain from the finished project. It is also crucial to discuss the project with the stakeholders and others involved to find out more information that might be useful for the project or to get information from a similar project. By making the critical pain points as straightforward as possible, the team can deal with them early in the project. Interviewing the project sponsor will also help the data analyst ensure they are on the same page and set standards to save time. The data analyst should be ready and think of different things to ask. The answers will help the team figure out what the project is about and what the goals and aims are. One of the most essential parts of the finding process is coming up with the first ideas. To do this, you need to develop ideas to test with data. Most of the time, you should start with a few key hypotheses to try and then come up with a few more. By beginning with a few assumptions, the team compares their answers to a trial or test results to find more ways to solve a problem. Having more ways to solve a problem will give the team a much larger set of notes to work with and more ways to decide on the project's most important results. During the research stage, the analyst can figure out what data the team needs to solve the problem, how much data they need, what type of data they need, and when they need it. When making the analytic plan, you must know the subject area, what 2
kind of problem you're trying to solve, and what type of data sources you'll use. Setting success criteria early in the project helps describe the issue and makes it easier for the team to decide which analytical methods to use later. Cite all references in APA format. Data science and big data analytics: discovering, analyzing, visualizing and presenting data. (n.d.). O’Reilly Online Learning. https://learning.oreilly.com/library/view/data-science- and/9781118876138/10_chapter-02.html#c02-01 3
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