"They come and think that now they will earn 500k per second, because they know the names of the frameworks and how to run the model in two lines from them."
Emil Maharramov leads the Computational Chemistry Services Group at biocad and in interviews faces the fact that candidates do not have a systematic understanding of the profession. They finish courses, come with well-tuned Python and SQL, can raise Hadoop or Spark in 2 seconds, complete a task according to a clear technical assignment. But at the same time, a step to the side is no longer. Although it is the flexibility of solutions that employers expect from their data science specialists.
What's happening in the Data Science market
The competencies of young professionals reflect the situation in the labor market. Here, demand significantly exceeds supply, so desperate employers are often really ready to hire completely green specialists and grow them for themselves. The option is working, but it is only suitable if the team already has an experienced team leader who will take over the training of the junior.
According to research by HeadHunter and Mail.ru, data analysts are among the most demanded on the market:
- In 2019, there were 9.6 times more vacancies in data analysis, and 7.2 times more vacancies in machine learning than in 2015.
- Compared to 2018, the number of vacancies for data analysts has increased 1.4 times, for machine learning - 1.3 times.
- 38% of open vacancies are in IT companies, 29% - companies from the financial sector, 9% - services for business.
The situation is fueled by numerous online schools that train those very juniors. Basically, training lasts from three to six months, during which students have time to master the main tools at a basic level: Python, SQL, data analysis, Git and Linux. The result is a classic junior: he can solve a specific problem, but he still cannot understand the problem and formulate the problem on his own. However, the high demand for specialists and hype around the profession often gives rise to high ambitions and salary requirements.
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Any employer would like his juniors to work without constant supervision and to be able to develop under the leadership of a team lead. To do this, a beginner must immediately possess the necessary tools to solve current problems, and have a sufficient theoretical base to gradually propose their own solutions and approach more complex problems.
Newcomers to the market are doing pretty well with tools. Short-term courses allow you to quickly master them and get started.
According to research by HeadHunter and Mail.ru, the most demanded skill is Python. It is featured in 45% of data science jobs and 51% of machine learning jobs.
Employers also want data scientists to know SQL (23%), be proficient in data mining (19%), mathematical statistics (11%) and be able to work with big data (10%).
Employers looking for machine learning specialists, along with knowledge of Python, expect the candidate to be proficient in C ++ (18%), SQL (15%), machine learning algorithms (13%) and Linux (11%).
But if juniors are doing well with tools, then their leaders are faced with another problem. Most course graduates do not have a deep understanding of the profession, so it is difficult for a beginner to progress.
. , Data Science, , .The very structure and duration of the courses does not allow you to go deeper into the required level. Graduates often lack the very soft skills that are usually missed when reading vacancies. Well, the truth is, which of us will say that he has no systemic thinking or desire to develop. However, as applied to a Data Science specialist, we are talking about a deeper history. Here, in order to develop, one needs a sufficiently strong bias in theory and science, which is possible only in long-term studies, for example, at a university.
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Chief Data Scientist
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There are many good Data Science courses on the market and getting an initial education is not a problem. But it is important to understand the direction of this education. If the candidate already has a strong technical background, then intensive courses are what you need. A person will master the tools, come to the place and work quickly, because he already knows how to think like a mathematician, see the problem and formulate problems. If there is no such background, then after the course there will be a good performer, but with limited opportunities for growth.
If you are faced with the short-term task of changing a profession or looking for a job in this specialty, then some systematic courses are suitable for you, which are short and quickly provide a minimum set of technical skills so that you can apply for an initial position in this field.The problem with the courses is that they provide fast, but minimal acceleration. A person literally flies into the profession and quickly reaches the ceiling. To come into the profession for a long time, you need to immediately lay a good foundation in the form of a longer-term program, for example, a master's degree.
Ivan Yamshchikov
Academic Director of the Online Master of Science in Data Science
, , . . , , , , , . , , - .The lack of a career ceiling is the main advantage of the master's program. In two years, the specialist receives a powerful theoretical base. This is how the first semester of the Data Science program at NUST MISIS looks like:
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- Introduction to Data Science. 2 weeks.
- Basics of data analysis. Data processing. 2 weeks
- Machine Learning. Data preprocessing. 2 weeks
- EDA. Intelligence data analysis. 3 weeks
- Basic machine learning algorithms. Ch1 + Ch2 (6 weeks)
At the same time, you can simultaneously gain practical experience at work. Nothing prevents you from getting a junior position as soon as the student masters the necessary tools. But, unlike the graduate of the courses, the master does not stop his studies at this, but continues to delve into the profession. In the future, this allows you to develop in Data Science without restrictions.
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