“And if I don’t know math, am I hopeless?” - experts answer frequently asked questions about the professions in Data Science

Interest in Data Science continues to grow, the market needs good specialists. But the threshold for entering the profession is quite high, beginners are often stopped by myths and stereotypes about the field - “for a long time, it’s difficult, it’s better not to interfere without physical education.” We collected the most frequent questions and concerns of those who start a career in Data Science and asked the experts to answer them.

“What kind of mathematics is needed? If there is no matbase, am I hopeless? ”

Konstantin Basheva, an analyst-developer at Yandex and a teacher of the course “Python for data analysis”

The question about mathematics is ambiguous. Profound knowledge of mathematics is neither a necessary nor a sufficient condition. Of course, the one who knows her will be easier. But all the necessary knowledge is given either in the classroom or in additional materials.
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Here, as in sports. There are people who can run a marathon without training. The rest will be harder, but with sufficient preparation and they will run. The mathematical base is cool, but not critical.

Daria Mukhina, Skyeng Food Analyst, Netology Analyst Course Consultant

It seems that now a deep mathematical base can be replaced by the ability to google. On the Internet, a huge number of videos and articles where you can get the information presented is available - and you don’t need to go to university textbooks. The main thing is to know what you need.

Now more important is the skill to apply knowledge in a real task, and not just to possess it.

Elena Gerasimova, Head of Data Science in Netology

The concept of "profile technical or mathematical education" is becoming a thing of the past. Confident in their skills and domain knowledge specialist from the "humanitarian" university will not be compared with a graduate of MIPT in knowledge of mathematics - compare the usefulness of the business to solve problems.

Already known dozens of working algorithms and libraries that are able to take over the entire mathematical part of themselves without human intervention.

“Well, what is the easier background to enter the DS sphere? Obviously, this is math, and what else will help? "

Konstantin Basheva, an analyst-developer at Yandex and a teacher of the course “Python for data analysis”

Of course, the easiest way is to enter the sphere of DS to those people who have experience in training or working on a technical specialty.

Although the division into "techies" and "humanities" is very conditional, for Data Scientist, mathematics is not needed in grade 8, but higher. You can study everything yourself, but if a person graduated from a technical college, most likely, he already has the necessary base. Those who have programming experience and understanding of algorithms will also be easier. If a person is very hard on Python, he will have to be more difficult - after all, they will start talking about probability theory, then about neural networks.

The experience of studying at the Physics and Technology Department or working on engineering specialties greatly simplifies mastering DS. However, it must be remembered that there are still a huge number of near-DS specializations that can be reached without deep knowledge of mathematics. You don't have to be a Data Scientist, you can become an excellent BI analyst with a good understanding of the business.

"And who is still preferable for an employer: a person with knowledge of Python and a developer background, or a graduate with a strong math?"

Alexey Kuzmin, Head of Development at DomKlik, Data Scientist, teacher of machine learning courses in Netology

It all depends on the task. This is really a difficult choice, there is no ready-made recipe. I would take a developer - for the tasks of my company, such a profile is closer.

Konstantin Basheva, an analyst-developer at Yandex and a teacher of the course “Python for data analysis”

And we have analysts - more math. But in general, everything really depends on the task. If an employer has a high-load banking service, then he rather needs a developer who will quickly close a large number of technical tasks and help with DS and models with something. If a company has a project that is already set up and works smoothly, then younger employees may be suitable to support it.

“Should Kaggle be viewed as an aid to entering DS?” Do employers look at Kaggle masters? ”

Konstantin Basheva, an analyst-developer at Yandex and a teacher of the course “Python for data analysis”

Of course! High places on Kaggle is a great project in the portfolio. Sometimes the platform is criticized for “idealized” conditions. Of course, there is no platform fault in this. Usually, when a date to a scientist or analyst poses a problem, it begins not with building cool models, but with management, data and tools. Where to get the necessary data? How to handle all this? What are the obvious problems in the data? This part is usually not on Kaggle.

When the model is made, another stage begins - implementation. Besides the fact that the system should work in sales, it is necessary to prove its business value, teach colleagues to use it and, possibly, “sell” it to the customer.

Therefore, sometimes an employee builds cool models, but in real conditions, he experiences difficulties with the first and third part of the work. If a person has good communication skills, he has excellent programming skills and, in addition, builds accurate models - he does not have the price. In Kaggle, you will hone model building, but you will need a lot of applied skills to apply it in real projects.

“What competences, other than technical ones, are needed by a novice specialist so that the employer will notice him among the general flow?”

Alexey Kuzmin, Head of Development at DomKlik, Data Scientist, teacher of machine learning courses in Netology

Everything depends very much on the tasks and on the company profile. If this is a startup for 5 people, then an analyst who knows how to deal with personnel can be useful - simply because a startup has no people for personnel. If it is a large, serious, large company with projects that last for years in which the same people do the same tasks, then you will need a narrow specialist who knows only one specific direction and nothing else.

A separate advantage is software skills on sociability, stress resistance, performance, ability to understand the application area.

It is useful when a specialist has the skills to work with a business - then it is easier for him to understand the needs and tasks of the company, he can immerse himself in the problem and offer some alternative solution.

In addition, there is a huge shortage of specialists on the market with skills related to DS. For example, we have been looking for a Product Owner for a very long time with an understanding of DS so that it can create products that are based on artificial intelligence.

“How to navigate vacancies and are not afraid if new tools are indicated for you?” What you need to go and start working in the profession? "

Konstantin Basheva, an analyst-developer at Yandex and a teacher of the course “Python for data analysis”

Council banal - go to the interview. Often written in a job is different from the actual requests of the employer. The interview gives you the opportunity to learn over which project you are planning to work, what tools you will need to use and with which people to work. My advice is to perceive the text of a vacancy as a guideline, and not the truth in the final instance.

Adequate employers understand that if you worked with Google Cloud, and they use Azure, then this is not a problem - a specialist will quickly retrain. There are much more important things: what exactly do you have to do, how are the processes organized in a team - this can be found out only at a personal meeting. In the vacancies such details do not indicate.

“Is it true that there is no remote work on the DS market?”

Elena Gerasimova, Head of Data Science in Netology

Remote work at such positions in large IT companies is indeed rather the exception. Nevertheless, a lot of foreign companies with Russian representative offices are ready for the sake of saving on salaries and relocation to a remote format when performing their tasks.

Remote employees are also often looking for startups - if it’s principally a remote one, you should look for such vacancies.

In general, I consider that work in the office for analysts and Data Scientist is preferable - not working in the office, you are depriving yourself of the opportunity to learn from colleagues right at the workplace, to communicate with the team, to quickly resolve emerging issues (well, to enjoy the benefits of a good office: dinners, changing environment).

“What if I become a junior scientist at 40? What are my prospects? Where am I and how to move?

Konstantin Basheva, an analyst-developer at Yandex and a teacher of the course “Python for data analysis”

We had guys who after 30 turned from industrial professions to developers: it turned out that everything in the department is 5-8 years younger - but these are trifles.

Of course, if a person goes to DS in 65 years, then yes, it will probably be hard for him. And so there is a huge number of cases when people transferred to DS from very remote areas, for example, medicine, at the age of 30-40 years.

Another important point is that when moving to a new sphere, you must be prepared to lower wages. If a specialist has a family and three children, it will be stressful. In general, there are a lot of positive examples, and the salary level is growing in parallel with the new experience.

Elena Gerasimova, Head of Data Science in Netology

When moving to DS as an adult, the attitude and readiness to sacrifice some of their established principles and accept the rules of the game that are provided for in this environment are extremely important. We recently graduated with honors from a student with three children: he took leave to care for the duration of his studies, while his wife worked during this period. He really wants to be a date scientist, a very talented graduate and his motivation is stronger than the surrounding circumstances.

“How to beginner specialist to answer questions about the salary at the interview? How to evaluate yourself? "

Daria Mukhina, Skyeng Food Analyst, Netology Analyst Course Consultant

For any person, the question of salary at the interview is a stressful question. I think it is risky to try to joke on this topic somehow or avoid it. It is best to conduct a mini-study before the conversation, unload vacancies where the plug is indicated: the upper threshold, the lower threshold. Understand how much you need, conditionally, you need money to live on, then once again review salary forks at the level of the juna - and name the amount that fits in them but will not be lower than your subsistence level.

From the Editor

• Profession " Data Scientist "
• Online course " Big Data "
• Online course " Machine learning "
• Online course " Python to work with data "