Data scientists — who they are, what they do, what responsibilities they have, and what you need to know.

In today's world, there are many promising and interesting professions, one of which is data scientist. This is precisely the field that is now accessible to many specialists. A data scientist is a person who engages in the analysis and processing of data to obtain valuable information and make important decisions. This profession has become especially important in the era of digital transformation, where the volumes of data are growing exponentially.

Who is a data scientist?

A data scientist is a specialist who plays a key role in transforming data into insights that help businesses make informed decisions and forecast trends. To do this, they must have analytical thinking, mathematical abilities, programming skills, and a deep understanding of the subject area. The history of the data scientist profession begins with statistics, which were used in various fields of knowledge. However, with the development of the Internet and social networks, statistical methods became insufficient, and there arose a need to combine statistics, programming, and business analytics.

What does a data scientist do?

A data scientist has many responsibilities, such as collecting data, cleaning and processing it, analyzing, developing machine learning models, interpreting results, visualizing data, and applying solutions in the company's work. This job requires the ability to work with large volumes of data from various sources, as well as proficiency in mathematical and programming skills. It is also important to be able to explain the results of the analysis to clients or other specialists.

What knowledge and skills should a data scientist have?

To become a data scientist, one needs to have technical knowledge and skills. The primary tools for a data scientist include programming languages Python and R, as well as knowledge of SQL for working with databases. It is also important to have knowledge of statistics and mathematical modeling. Skills in working with machine learning libraries (Scikit-Learn, TensorFlow, PyTorch), data processing (Pandas, NumPy, Dask), and data visualization (Tableau, Matplotlib, Seaborn, Plotly) will also be helpful. Additionally, important skills include analytical thinking, communication skills, Time management, and creativity.

Where can you learn data science?

There are several options for obtaining education in the field of data science. Ukrainian universities, such as the Kyiv Polytechnic Institute, Lviv National University, and Kharkiv National University, offer relevant programs. One can also study this profession through courses on educational platforms such as Coursera, DataCamp, Udemy, or on local courses, such as DAN.IT Education. Furthermore, self-education through books and materials is also an option.

Where to look for a data scientist job?

After obtaining education, one can search for jobs on various websites such as LinkedIn, Layboard.com, or participate in competitions and internships in companies. It is also worthwhile to attend IT conferences and network with professionals in the field of data science.

How much can you earn as a data scientist?

The salary of a data scientist depends on the level of experience and the place of work. Salaries can range from 800 to 1500 dollars per month for junior positions, from 2000 to 4000 dollars for middle positions, and 5000 dollars or more for senior positions. In international companies, salaries can be significantly higher.

Pros and cons of being a data scientist

Among the advantages of the data scientist profession are high demand in the labor market, good salary, dynamic development, and interesting tasks. However, there are also some disadvantages, such as a high entry threshold, monotonous work, and the need for continuous learning.

Overall, the data scientist profession creates opportunities for growth in the high-tech field and makes a significant contribution to business development. However, success in this profession requires perseverance, facing difficulties, and continuous learning.