The Data Scientist builds data-driven models that help make decisions in science, business and everyday life.


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Collecting data is a way to measure the processes around us…

It can work with unstructured arrays of information in various fields: from detecting elementary particles in experiments at the LHC, analyzing meteorological factors, analyzing data on vehicle movements to researching financial transactions, search queries, and user behavior on the Internet.

Data Science Jobs can be an illustrative example here. As a result, models are obtained that predict the weather, traffic congestion, demand for goods, find images where traces of the necessary elementary particles may appear, give decisions on granting a loan, can recommend a product, book, film, music.

What is Data Science?

Data Science is about applying scientific methods to work with data to find the solution you need. Broadly speaking, natural sciences are based on Data Science. For example, a biologist conducts experiments and analyzes the results to test his hypotheses. He must be able to generalize private observations, exclude accidents and draw correct conclusions.

A datasetist works with data just like a scientist in any other field. He uses mathematical statistics, logical principles and modern visualization tools to get the result.

Collecting data is a way to measure the processes around us. And scientific methods make it possible to decipher large amounts of data, find patterns in them and apply them to solve a specific problem.

What is a Data Science Specialist?

A dataset processes data arrays, finds new connections and patterns in them using machine learning algorithms, and builds models. A model is an algorithm that can be used to solve business problems - the Data analyst jobs article is worth reading to get even more info on that point.

As a result, the cost of the trip goes down and the quality goes up. In banks, models help to make more accurate decisions about issuing a loan, in insurance companies - they assess the likelihood of an insured event, in online commerce - they increase the conversion of marketing offers.

Global search engines, recommendation services, voice assistants, autonomous trains and cars, facial recognition services - all of this was created with the participation of datascientists.

Analyzing data is part of the dataset's job. But the result of his work is a model, a code written on the basis of analysis. This is the main difference between datascientist and data analyst. The first one is an engineer who solves a business problem as a technical one. The second is a business analyst, more immersed in the business component of the task. He studies needs, analyzes data, tests hypotheses, and visualizes the result.

A dataset solves problems using machine learning, such as image recognition or predicting material consumption in manufacturing. The result of his work is a working model according to the terms of reference, which will solve the business problem.

A Data Scientist goes through the same career stages as other IT professionals: junior, middle, team lead or senior. On average, each step takes from one to two years. A more experienced specialist understands business problems better and can offer the best solution for them. The higher the level, the less the datascientist is focused only on technical problems. He can evaluate the project and its semantic component.

Data Science Specialist Tasks

The tasks differ from company to company. In large corporations, a datasetist works in several directions. For example, for a bank, he can solve the problem of credit assessment and engage in speech recognition processes.

The stages of work on a task for datascientists from different spheres are similar - see this Data Science Internship description for example.

Each new iteration allows for a better understanding of the business problems, clarification of the solution. Therefore, each step is repeated over and over to develop the model and update the data.

Data Science works for startups and large corporations alike. In the first, specialists work alone or in small teams on individual tasks, and in the second, they implement long-term projects in conjunction with business analysts, data analysts, developers, infrastructure administrators, designers and managers.

The project manager with analysts takes over most of the work: communicates with the business, collects requirements, forms the technical task. Depending on the level and principles of work in the company, a Data Science specialist participates in negotiations or receives tasks from the project manager and analysts.

The next step is data collection. If the company does not have established processes for obtaining data, the dataset solves this problem as well. He implements tools that help to automatically receive and pre-clean, structure the necessary information.

Marking up your data is also a way to clean up your data. Each record is assigned a label by which you can determine the class of data: whether it is spam or not, the client is solvent or not enough.