Many data science projects are launched with good intentions, but fail to deliver because the correct process is not understood. To achieve good performance and results in this work, the first steps must include clearly defining goals and outcomes, collecting data, and preparing and exploring the data. This is all about solving problems, which requires a systematic process.
Data science should be implemented in a way that enables decision making to follow a systematic process. To be able to have that, we need a plan and a methodology to do a data science project. Sadly, most data science projects fail because the people involved don’t understand clearly what they have to do, or what are the most important things for a company. Your solution needs to be tied to the goals and objectives of the company or its departments.
In this article, I’ll talk about the first steps of a data science project and what to do to achieve good performance and results in your work. This is a first approach to the whole picture of a data science project that I’ll be talking about in later articles.
But first, I’ll tell you a little story on how data science develops in a company (in a common scenario). This is what happens:
- You have a lot of data that you have been collecting for months or years, and someone says: “We have a lot of data, we have to do something about it.”
- The company decides to create new areas to start thinking about how to use data to make decisions. New people are hired to work in these newly created fields.
#2020 jul tutorials # overviews #beginners #data science
