The Data Science Process — 8 Steps To A Successful Project

The Data Science Process — 8 Steps To A Successful Project

In this article, I am going to talk about the 8 major steps every data scientist needs to go through. The first time I worked on a data science project, I had no clear vision of what it takes to do a complete analysis.

When it comes to data science projects, there is a sense of an unclear pathway in regards to what the necessary steps would be to complete a data science project. In this article, I am going to talk about the 8 major steps every data scientist needs to go through. The first time I worked on a data science project, I had no clear vision of what it takes to do a complete analysis. I hope this article makes life easier for those that are new to data science and would like to have a step-by-step project plan. For those that are more experienced, I would love to hear in the comment section what your thoughts are on what the best approach is to take on a data science project. Would you do something differently?

8 Main Steps

Grab your favorite hot beverage and let’s get into it. I am going to guide you through every step and make it as simple as possible for you to understand every part of the project life cycle from start to end.

1. Look at the big pictur

Many people think that the first step is to obtain the data right away. However before we even get to that stage we first need to have a clear mind on what we actually want to solve. The first question you should always ask yourself and your boss is what problem we actually want to solve and what exactly is the objective of this project. We need to ask ourselves how the business or whoever is the end recipient of this project is going to use and make use of our project. It’s a crucial question to ask yourself and think about before you continue to the next step. The importance of asking ourselves this question can’t be underestimated. Asking why and what the purpose of our project is will lead you to answer the following questions:

  • How am I going to frame the problem.
  • What algorithm am I going to select.
  • What performance measure do I need in order to evaluate my model.
  • How much effort should I put into modifying and tweaking my model to make it perform better.

Suppose we are working on a project where the main goal is to a create model to estimate the housing prices in a particular city. Let’s say San Jose California for example, since the prices are ridiculously high over there. Once you have gathered all your information and understood clearly what your task is it’s time to design your system.

Ask your self how am I going to frame the problem that I am working on. Is it supervised, unsupervised, or Reinforcement Learning? Is there a chance that the problem is a classification problem, a regression task or something else? What techniques should I use. I am not going to explain what each of the above learning techniques mean, but you should know about them and I highly recommend to check out this link so you get a better understanding what the differences are.

data-science machine-learning big-data-analytics data-science-projects analytics

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