It is quite often that we enter a web page and are being recommended products other users have bought when we select a certain one. Or, how many times have you entered a search in Google and it was able to end the phrase for you? Problem statement is a step in the Data Science Process more dependent on soft skills (as opposed to technological or hard skills), nevertheless being based on questions and data, sometimes a lot of data, it is beneficial to have some data analysis tool. In doing this, you might need to find yourself being stuck with the difficult process of doing it from scratch. To avoid this, our site offers you available, ready-made yet customizable templates that you can choose for your Analytics Problem Statement needs. Just browse along this article and choose the template that satisfies your needs the most.
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Data Analytics Problem Statement
The problem statement stage is the first and most important step of solving an analytics problem. It can make or break the entire project. When a business approaches a data scientist with a problem they want to solve, they will always define the problem in layman’s terms. This means the problem will not be clear enough, from an analytics point of view, to begin solving it right away. The problem needs to be well framed.
As the data scientist, you need to think of the problem statement in mathematical terms. This is easier said than done, but not impossible.
Phase 1: Understand Business Goals And Expectations
This phase is crucial, as the information provided to you will be the basis for your analysis. So take your time and make sure to get all the information you need from the business.
Step 1: Begin by understanding the business’s vision.
What do they hope to achieve by solving the problem? List their objectives.
Step 2: What are the pain point that they are facing?
Some of these may be perceived and others may be undiscovered. Don’t worry the undiscovered ones will pop up later in your analysis.
Step 3:What resources are available?
- Infrastructure: Required for the solution to get started, e.g. data modeling, security and authentication, user management.
- Pre-requisite features: These are user-level management features, e.g. configuration and settings pages, profile pages and other management pages.
- Transactions: This is the core of your solution, as these are activities your solution is automated around. For us, this would be bookings through our system, sales for our customers, cancellations, reminders and activities that save time and money.
- Business Results: This is where users can realize the solution has worked for them. E.g. reports, metrics, automated procedures, automated alerts.
Step 4: What are the potential benefits?
How will the company benefit from this analysis?
Is it worth pursuing?
Is there a problem, which when solved, would benefit the company more?
Step 5: What risks are there in pursuing the project?
Prevention is better than cure. By detecting potential risks early on, you can minimize negative impacts to get the best possible outcome. Also list the contingencies to potential risks.
Step 6: Determine if the expected benefits are realistic and attainable from a data point of view.
Inform the company in advance if any of their expectations are unrealistic.
Step 7: Determine the duration of the project.
ML problems perform best for short term goals, however the company may want to focus on long term goals.
Step 8: Perspective, see the problem from the business point of view and from their client’s point of view. This will help you produce better results that will benefit both sides.
Step 9: Ensure that you have the domain knowledge required for the particular problem. The company is the expert in their domain, so make sure you understand their industry well. Ask as many questions as you need.
What is an Analytics problem?
Analytical problem solving means reasoning using facts and logic. Analytical thinkers can take pieces of information, compare them and decide what the information is saying. They can assemble the information to produce new insight into the problem rather than simply restating the information.
How do you start a problem statement example?
Describe how things should work.
Explain the problem and state why it matters.
Explain your problem’s financial costs.
Back up your claims.
Propose a solution.
Explain the benefits of your proposed solution(s).
Conclude by summarizing the problem and solution.
How do you solve Data Analysis problem?
create a representation of the problem. consider the units involved. pay attention to the meaning of quantities. know and use different properties of mathematical properties and representations. apply key principles of statistics. estimate the probability of a simple or compound event.
You might find yourself getting caught up in the problem statement that you are making especially that you’ve got to do it from the very scratch. With this, it is highly encouraged of you to make available all the resources you can find online. What are you waiting for? Avail our templates in SampleTemplates now!
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