Start 2021 right. We are looking for a job with a salary of 170 rubles


  1. Why exactly this salary?
  2. What is Data Science and Big Data?
  3. How did Data Scientists become the new elite?
  4. What exactly can a data scientist do and where can he work?
  5. Do you have to be a techie to work with big data?
  6. How long will Data Science be fashionable in the world?
  7. What kind of knowledge should a Data Science specialist have?
  8. What will happen after training? Who will hire?

Why exactly this salary?

Why not! 170 thousand rubles is a good salary, which will allow you to stand on your feet more confidently and be calmer about the news about the rise in prices for milk or gasoline. However, try to try on a similar salary for yourself. Perhaps it will suit you and like it.

However, joking apart, 170 rubles a month, or more than 000 million rubles a year, is the average salary of a Data Science specialist with about two years of experience.

Since there is a shortage of personnel in the field of Data Science, even entry-level specialists with 1 year of experience receive an average of 120 thousand. However, you cannot stop at the beginning of the journey. Thus, the salary of advanced specialists exceeds 250 rubles.

Start 2021 right. We are looking for a job with a salary of 170 rubles

What is Data Science and Big Data?

On the one hand, the potential salary looks extremely attractive. On the other hand, it is difficult to pretend when the name of the profession does not explain too much what it is all about.

Big Data, “big data”, or big data are huge amounts of data that accumulate in systems. And this is not about interstellar flights, but about the most commonplace things. For example, grocery stores make thousands of purchases every day, banks conduct hundreds of thousands of financial transactions every day, and users perform millions of actions on websites. All this data is collected in arrays of information with which you need to do something. After all, this data contains useful insights on how to conduct business more efficiently and correctly.

Big Data specialists have come to be called data scientists, or data scientists. These are people whose task is to analyze big data, find patterns in it and provide answers to questions.

How did Data Scientists become the new elite?

If even 10 years ago everything was carried in the hands of programmers and it was believed that only their future belongs to them, today everything has changed. And humanity is relatively close to the point where machines can write code for themselves. But the question of data analysis, strategic planning and inventing algorithms is unlikely to ever be handed over to artificial intelligence. It is for this reason that Data Scientists have become the new elite. There are still few people who think with algorithms, they are in high demand, and they receive more than the market average.

Start 2021 right. We are looking for a job with a salary of 170 rubles

What exactly can a data scientist do and where can he work?

Despite the complex English-language terminology, Data Scientists in general are engaged in simple and understandable tasks.

For example, a chain of coffee shops is thinking about where to open a new outlet. Previously, such decisions were made on the basis of the experience of the territorial manager or just a whim. Now it’s the job of a big data scientist. Such a specialist will take data on all coffee houses in the city, add the rental cost and information about known passenger traffic to this data array. After analyzing this information, he will be able to offer the best option for opening a new point.

Another common example is grocery store assortment. Not everyone pays attention to this fact, but in a typical supermarket near your home there are 20 to 50 thousand products. In a hypermarket, the assortment can consist of 150 thousand unique items. And, for example, a data scientist can find implicit patterns, suggest ideas for the location of product groups, or which products can be discarded and which category to expand.

Other examples are the development of recommendation services that offer interesting options for users of music, films or goods in an online store, a selection of opponents in an online game, an analysis of a medical or banking database in order to find out who can be offered a loan or invited for examination.

Data Scientists are important in almost every industry today. Everything is clear about the areas of business and banks, but, in addition, data Scientists are important in production and in insurance companies to assess the likelihood of equipment breakdown and insurance claims, in transport companies for laying the best routes, in agriculture in order to select the optimal land use systems. And even in meteorological services, modern weather forecasts are prepared using Data Science. However, the weather still wins all forecasters …

Do you have to be a techie to work with big data?

This is one of the main myths due to which there is still a shortage of personnel in the market. For those wishing to become specialists in Data Science, one of the main skills is the ability to think in algorithms, that is, to come up with the most logical and correct sequence of actions to solve a particular problem. And programming languages ​​(in this case, Python) are needed only in order to clothe a thought in a computer-understandable code.

By the way, the Python programming language is considered one of the easiest languages. Python as a high-level language allows you to operate with semantic commands. That is, instead of thoroughly prescribing all the variables and each step, memorizing complex syntax, you can use general commands that are understandable from the school English course – print, check, type, if, else, except. As a result, Python code is 3-5 times shorter than, for example, C ++ or Java code. Because of its simplicity, Python is often called the programming language of the future.

How long will Data Science be fashionable in the world?

A reasonable question. I don’t want to spend time developing in the profession, and after a couple of years to see that demand has dried up. In the case of Data Science, you don’t have to worry about this. Analysts believe that it will be possible to speak well of the transition of the market to a mature phase if by 2027. It is believed that by this year the staff deficit in developed countries will begin to subside.

It is also important to understand that Data Science has its own specializations depending on the tasks to be solved. For example, entry-level professionals tend to focus on data analytics. At the same time, you can either focus on direct data analysis or become a Data Engineer who is engaged in the creation and maintenance of data infrastructure. To some extent, the demand for this profession can be compared with auto mechanics. Cars can be powered by an internal combustion engine or electricity, but they still need to go to the workshops for maintenance and repair. So it is with the field of Data Science.

What kind of knowledge should a Data Science specialist have?

As mentioned above, the initial knowledge base is practically unimportant. When it comes to Data Science, it often happens that the previous (not related to “data science”) knowledge, on the contrary, will help to quickly understand the process. For example, if you’ve worked in sales in the past and understand customer motivation, then it would be more helpful to analyze the relevant database than if you’re just a good Python programmer.

However, if we approach the assessment of knowledge formally, then an accomplished Data Science specialist knows the Python programming language, knows databases and SQL, knows the basics of algorithms and mathematical statistics.

What will happen after training? Who will hire?

After training, you will be able to compose yourself a clear and understandable resume that will arouse the interest of potential employers. In your resume, you can write:

  • I can program in Python
  • I use libraries pandas, numpy, matplotlib, plotly, skleam
  • I write complex SQL queries
  • I work in Hadoop and Spark frameworks
  • I can work with any SLE (GitHub, BitBucket etc)
  • I create chat bots and neural networks
  • I understand the principles of building a data infrastructure
  • I understand big data and cloud storage for data
  • I know mathematical statistics, probability theory and basic ML algorithms
  • I bring models to production
  • I write robots for trading with AI
  • I can provide a portfolio with proven cases and projects


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