Framework of a Typical Data Science Project
Uncover the steps involved in a data science project.
We'll cover the following...
There are seven significant steps in a typical data science project. It's essential to break down the steps you need to take in order to complete your data science project. This approach applies to everyone, whether they are new or experienced.
Problem definition
The first step is defining the problem in the clearest way possible. Remember, clarity is power. If you have an obvious goal, then you will achieve it way sooner than if your goal is not very clear. For example, suppose you work in HR analytics, and the leader(s) wants you to do something to "reduce attrition rates for one of our clients."
Now, this problem definition is not clearly defined. It would be clearer if they said, "Reduce attrition rates below 10% by the end of this financial year for one of our clients." It would be clearest if they said, "Reduce attrition rates below 10% by the end of this financial year for one of our clients using on-site and off-site employees' records for the past 10 years." This is sometimes a difficult task for leaders and data scientists.
Author's note: Once, I received a query from a senior analyst who was working in a big e-commerce company in India. The query was, "how can I use statistics concepts to solve a particular problem I'm facing?" The problem he had didn't need any more statistical solutions, but his team leader was forcing him to do something using statistics. This happens when leaders don't understand the need for data science.
I had one more client who didn't know what he wanted to achieve. He was analyzing American football data and initially, he said that he wanted to compare American football experts' recommended scores with actual scores for each player. However, it took around four days to change this problem definition to ...