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Answers
Answer:
What is the question you have not showen
Explanation:
Six phases of the Data Science Life Cycle
Phase 1: Exploring
The goal in the first phase is to define a question to answer. To do this it's important that the data scientist and the business person are working together. A few questions they might ask one another are;
What is the main objective of the business
What are the desired outcomes the business wants to have to happen
What are the pain points of the business?
Defining the question used in a data science project is the key to its success.
Phase 2: Data Discovery
It's important to be very concise in this phase. Getting to a phase that is further along in the project then discovering that the data won't answer the question will lead to trouble. Data due diligence is a key factor in data science success.
Phase 3: Data Prep
Once you know you have the data you need, and you have a good question, then you go into data prep. Depending on how sophisticated your business is, this could be a very big step, or it could be a very small step. In the most ideal situation, you're usually just taking a couple of different tables and joining them together and organizing them in the way that the data scientist would like. Then we get into the data science process part of things.
Phase 4: Exploratory Analysis
First, we go into the exploratory analysis phase, and if you've watched any of our past videos, you would see the episode we did on diagnostic analytics. That's very reminiscent of what's going on here. We're really trying to figure out how our different variables are related to one another and what their distributions look like, what correlates to what—trying to get a good view of what's going on with our data.
Phase 5: Model Design
Once we understand that, we go into model design, which is where we really put our thinking hats on, as we want to understand or define the mathematical approach that will most likely give us the results that we want. You need to consider everything that we've seen in here to do a good design.
Phase 6: Build It!
After we have it designed, we go ahead and build the model. Usually, we program this in a language like R or Python, etc., and once we have that built out, we deploy it into the business process, so it can be used by the business person.
All of this really fits nicely with the scientific method, because it allows us to iterate through. The scientific method is all about hypothesis testing, so if you have a question and you think that this particular variable is going to give you the answer that you need, you go through this cycle to test it. You say, "Well, I think it's going to work." And then you build it in the model, and then you see whether it did or did not work, or if it gave you the results that you were trying to get.
Conclusion
You really need to get a definition, and then you go through and find the data, and then go through the whole hypothesis testing again to see if you are right. Is there, in fact, a big difference between customer types, and how will it impact the model results?
Once you are satisfied with the model results you created, you go ahead and deploy it. You integrate it into the business, and then you're ready to go. You can just deploy the prices that you want that are most optimal to hundreds of stores for thousands of products, which is pretty amazing.
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