{"id":22481,"date":"2020-12-03T11:45:00","date_gmt":"2020-12-03T11:45:00","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/a-laymans-guide-to-data-science-part-3-data-science-workflow\/"},"modified":"2021-05-21T03:29:50","modified_gmt":"2021-05-21T03:29:50","slug":"a-laymans-guide-to-data-science-part-3-data-science-workflow","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/a-laymans-guide-to-data-science-part-3-data-science-workflow\/","title":{"rendered":"A Layman\u2019s Guide to Data Science Part 3: Data Science Workflow"},"content":{"rendered":"\n

This is Part 3 of this series. Here is Part 1: How to Become a (Good) Data Scientist \u2013 Beginner<\/a> Guide and Part 2: A Layman\u2019s Guide to Data Science. How to Build a Data Project <\/a>.<\/p>\n\n\n\n

By now, you have already gained enough knowledge and skills about Data Science<\/a> and have built your first (or even your second and third) project<\/a>. At this point, it is time to improve your workflow to facilitate further development process.<\/p>\n\n\n\n

There is no specific template for solving any data science problem (otherwise you\u2019d see it in the first textbook you come across). Each new dataset and each new problem will lead to a different roadmap. However, there are similar high-level steps in many different projects.<\/p>\n\n\n\n

In this post, we offer a clean workflow that can be used as a basis for data science projects. Every stage and step in it, of course, can be addressed on its own and can even be implemented by different specialists in larger-scale projects.<\/p>\n\n\n\n

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Data science workflow<\/figcaption><\/figure><\/div>\n\n\n\n

Framing the problem and the goals<\/strong><\/h2>\n\n\n\n

As you already know, at the starting point, you\u2019re asking questions and trying to get a handle on what data you need. Therefore, think of the problem you are trying to solve. What do you want to learn more about? For now, forget about modeling, evaluation metrics, and data science-related things. Clearly stating your problem and defining goals are the first step to providing a good solution. Without it, you could lose the track in the data-science forest.<\/p>\n\n\n\n

Data Preparation Phase<\/h2>\n\n\n\n

In any Data Science project, getting the right kind of data is critical. Before any analysis can be done, you must acquire the relevant data, reformat it into a form that is amenable to computation and clean it.<\/p>\n\n\n\n

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Acquire data<\/strong><\/figcaption><\/figure><\/div>\n\n\n\n

The first step in any data science workflow is to acquire the data to analyze. Data can come from a variety of sources:<\/h3>\n\n\n\n