DAT 205 Module Four Data Analytics Lifecycle
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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
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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
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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
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