What does Data Scientist do?

Today we are going to learn about what does Data Scientist do?

Well, generally speaking, Data Science is not a certain or a single one realm, it’s like a combination of various disciplines that are focusing on analyzing data and finding the best solutions based on them. Initially, those tasks were held by math or statistics specialists, but then data-experts began to use machine learning and artificial intelligence, which added optimization and computer science as a method for analyzing data. 

Artificial Intelligence, Machine Learning, Deep Learning, and Data Science — undoubtedly, these major terms are the most popular today. And although they are somehow related, they are not the same. So, before jumping into any of those realms, it is mandatory to feel the difference. Artificial Intelligence, Machine Learning, Deep Learning, and Data Science — undoubtedly, these major terms are the most popular today. And although they are somehow related, they are not the same. So, before jumping into any of those realms, it is mandatory to feel the difference. Data Science is the addition of meaning to arrays of data, visualization, collection of insights, and making decisions based on these data.

Machine Leaning is creating tools from extracting knowledge from data. In ML models can be trained on data independently or in stages:

So what does Data Scientist do? Here is all you need to know about it:

• detection of anomalies, for example, abnormal customer behavior, fraud;
• personalized marketing — personal e-mail newsletters, retargeting, recommendation systems;
• Metric forecasts — performance indicators, quality of advertising campaigns and other activities;
• scoring systems — process large amounts of data and help to make a decision, for example, on granting a loan;
• basic interaction with the client — standard answers in chat rooms, voice assistants, sorting letters into folders.

To do any of the above tasks you need to follow certain steps:

• Collection Search for channels where you can collect data, and how to get it.
• Check. Validation, pruning anomalies that do not affect the result and confuse with further analysis.
• Analysis. The study of data, confirmation of assumptions, conclusions.
• Visualization. Presentation in a form that will be simple and understandable for perception by a person — in graphs, diagrams.
• Act. Making decisions based on the analyzed data, for example, about changing the marketing strategy, increasing the budget for any activity of the company.

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