What is Data Science?
In general, Data Science is a set of specific disciplines from different areas responsible for analyzing data and finding optimal solutions based on them. Previously, only mathematical statistics did this, then they began to use machine learning and artificial intelligence, which, as methods of data analysis, added optimization and computer science (that is, computer science, but in a broader sense, what is commonly understood in Russia)[1]
Main article - Data Science
And what do scientists from this sphere do?
First, programming, mathematical models, and statistics. But not only. It is very important for them to understand what is happening in the subject area (for example, in financial processes, bioinformatics, banking or even in a computer game) in order to answer real questions: what risks accompany a particular company, which sets of genes correspond to a certain disease, how to recognize fraudulent transactions or what behavior people correspond to players who need to be blocked.
Data Director - Chief Data Officer, CDO
Main article: Chief Data Officer (CDO)
Data scientist
A Data Science specialist is a data expert who often has a higher education in mathematics or statistics and often knows how to program on R or Python. The most popular datasientists also have knowledge in their respective areas of business.
Although skill sets vary from person to person, the data specialist's job is to help their employer solve the complex problems often associated with finding insights, optimizing business processes, and building predictive models. This role can be considered part of IT, or it can be integrated into one of the departments of the company. Of all the possible data roles, datasayentists tend to be the most experienced experts.
The main tasks of Data Scientist are:
- ability to extract the necessary information from a variety of sources
- Use real-time information flows
- set hidden patterns in data arrays
- statistically analyze them for making competent business decisions.
The main difference between data researchers and, for example, analysts is the ability to see logical connections in the system of collected information, and on the basis of this, develop certain business solutions. Data researchers collect information, build models based on it and actively use quantitative analysis[2].
It is this rare combination of competencies that determines the salary of a data scientist: in the United States it is $110 thousand - $140 thousand per year. "This vacancy is becoming more and more popular," said Laura Kelley, vice president of IT consulting and recruitment agency Modis (USA), on the pages of IT World. - Companies are increasingly focusing on information and applications. They need people who can manage a lot of data.
Michael Rappa, director of the Analytics Institute at the University of North Carolina, has been working with his colleagues for 6 years to develop a course that will train data scientists. "These specialists should be able to extract the right information from all kinds of sources, including real-time information flows, and analyze it for further business decisions," he says. "This is not only about the amount of information processed, but also about its heterogeneity and speed of updating."
Companies that try to solve this problem with the help of statisticians, computer or business analysts do not achieve the desired result. It is necessary to combine all these skills in one person. For example, business analysts perceive indicators such as product development and management, but are not able to analyze and adequately interpret data. Mathematicians and statisticians lack business knowledge. That is why, according to Rappa, data researchers need an interdisciplinary education - they should be able to solve business problems and create information models.
100% of graduates of the data research course developed by the Institute of Analytics received job offers before they completed their studies. Rappa also notes that the specialty itself - a data scientist - sounds more attractive than a "statistician" or "computer analyst."
Database Manager
According to Indeed, the database manager is responsible for "maintaining the organization's databases, including troubleshooting, information ordering, and reporting." They also help you identify the right hardware and software systems for your business.
Data Analyst
Data analysts collect and analyze large amounts of data for companies and make recommendations based on their findings. They can work in a variety of industries, including healthcare, IT, professional sports and finance, to improve processes, reduce costs, identify trends, and increase efficiency.
Data Modeling Specialist
Data modellers are system analysts who develop computer databases that transform complex business data into usable data in computer systems. They work with data architects to create databases that meet the needs of the organization using conceptual, logical, and physical data models.
Machine Learning Engineer
A machine learning engineer is an IT specialist who researches, creates, and designs self-starting AI systems to automate predictive models. He develops and creates AI algorithms capable of learning and making predictions.
Business Intelligence Developer
Business intelligence developers create systems and programs for the organization that allow users to find and interact with the necessary information. This may include dashboards, search functions, simulation applications, and data visualization applications. BI developers should have a deep knowledge of data science and best practices of user experience.
Why Data Scientist is sexier than BI Analyst
Due to the growing popularity of data science (DS), two very obvious questions arise. First, what is the qualitative difference between this recently formed scientific direction and the existing business intelligence (BI) direction for several decades and actively used in the industry? The second - perhaps more important from a practical point of view - how do the functions of specialists of two related specialties data scientist and BI analyst differ? In the material prepared specifically for TAdviser, journalist Leonid Chernyak answers these questions.
2020: MADE and HeadHunter Big Data Academy find out how the demand for Data Scientist in Russia is changing
On July 16, 2020, the MADE Big Data Academy from Mail.ru Group and the Russian online recruitment platform HeadHunter (hh.ru) compiled portraits of Russian Data Science and Machine Learning specialists. Analysts have found out where they live and what they know, as well as what employers expect from them and how the demand for such professionals is changing.
The MADE and HeadHunter Academy (hh.ru) have been conducting the study for the second year in a row. This time, experts analyzed 10,500 resumes and 8,100 vacancies. According to analysts, data analysts are one of the most popular in the market. In 2019, vacancies in the field of data analysis became 9.6 times more, and in the field of machine learning - 7.2 times more than in 2015. If we compare with 2018, the number of vacancies of data analysis specialists increased 1.4 times, in machine learning - 1.3 times.
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More active than other big data specialists are looking for IT companies (they account for more than a third - 38% - open vacancies), companies from the financial sector (29% of vacancies), as well as from the business services sector (9% of vacancies).
The same situation in the field of machine learning. But here the advantage in favor of IT companies is even more obvious - they publish 55% of vacancies in the market. Every tenth vacancy is placed by companies from the financial sector (10% of vacancies) and the business services sector (9%).
From July 2019 to April 2020, the summary of data analysis and machine learning specialists became 33% more. The former place an average of 246 resumes per month, the latter - 47.
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The most popular skill is Python. This requirement is found in 45% of data analyst vacancies and in half (51%) of machine learning vacancies.
Employers also want data analysts to know SQL (23%), know Data Mining (19%), mathematical statistics (11%) and know how to work with big data (10%).
Employers looking for machine learning specialists, along with Python knowledge, expect the candidate to own C++ (18%), SQL (15%), machine learning algorithms (13%) and Linux (11%).
In general, the supply in the Data Science market corresponds to demand. Among the most common skills of data analysts are Python (77%), SQL (48%), data analysis (45%), Git (28%) and Linux (21%). At the same time, ownership of Python, SQL and Git are skills that are almost equally common in the resume of specialists of any level. Experienced specialists are distinguished by advanced data analysis skills, including Data Analysis and Data Mining.
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Machine learning specialists in the top have skills such as Python (72%), SQL (34%), Git (34%), Linux (27%) and C++ (22%).
Moscow accounts for more than half (65%) of the vacancies of specialists in the field of data analysis and exactly half of the vacancies of specialists in the field of machine learning. In second place is St. Petersburg: 15% of vacancies of specialists in the field of data analysis and 18% of vacancies in the field of machine learning - in this city.
Compared to the first half of 2019 in July 2019 - April 2020, the vacancy rate of data analysis specialists in Moscow increased slightly - from 60% to 65%.
As for applicants, more than half of them are also in Moscow: 63% of data analysts and 53% of machine learning specialists. The second line is also behind St. Petersburg (16% and 19% of the resume, respectively).
2019
Big Data Academy MADE and HeadHunter have compiled a portrait of Russian Data Scientist
On September 13, 2019, Mail.ru Group announced that the MADE Big Data Academy, together with HeadHunter, studied several thousand vacancies and resumes and compiled a portrait of the Russian data scientist: age, where they live and work, skills, languages, education, etc.
Portrait of Russian Data Scientist
Where specialists live and work in Data Science, how old they are, which university they graduated from, what programming languages they speak, how many degrees they have - the MADE Big Data Academy from Mail.ru Group and the company's research service HeadHunter (hh.ru) studied the summary of 8 thousand Russian data scientists and 5.5 thousand employer vacancies and compiled a portrait of a Data Science specialist.
How much are Data Science experts in demand? Since 2015, the need for specialists has been constantly growing. In 2018, the number of vacancies under the heading Data Scientist increased 7 times compared to 2015, and vacancies with the keywords Machine Learning Specialist - 5 times. At the same time, in the first half of 2019, the demand for Data Science specialists amounted to 65% of the demand for the whole of 2018.
Who works for Data Science?
Mostly men work in the profession, among data scientists their share is 81%. More than half of people looking for work in data analysis are specialists aged 25-34 years. There are still few women in the profession - 19%. But interestingly, young girls are increasingly interested in Data Science. Among the women who posted the resume, almost 40% are girls aged 18-24 years.
But the summary of older applicants is quite small - only 3% of date scientists over 45 years old. According to expert estimates, this can be due to several factors: firstly, Data Science has few older people, and secondly, applicants with more experience are less likely to place their resumes on large search resources and more likely to find work on recommendations.
Where do Data Science professionals live and work?
More than half of the vacancies (60%) and applicants (64%) are in Moscow. Also, specialists in the field of data analysis are in demand in St. Petersburg, in the Novosibirsk and Sverdlovsk regions and in the Republic of Tatarstan.
What education does Data Science have?
9 out of 10 professionals seeking work in the field of data analysis have higher education. Among people who graduated from universities, the share of those who continue to develop in science and managed to get a degree is large: 8% have a candidate of sciences degree, 1% - a doctor of sciences.
Most of the specialists looking for work in the field of Data Science studied at one of the following universities: at the Bauman Moscow State Technical University, Moscow State University named after M.V. Lomonosov, MIPT, HSE, St. Petersburg State University, SPbPU, Financial University under the Government of the Russian Federation, NState University, K. Employers are loyal to the same universities.
43% of specialists at Data Science noted that in addition to higher education, they received at least one additional education. Most often, the summary mentions online machine learning courses and data analysis on Coursera.
What skills do Data Science experts indicate?
Among the key skills, Data Science experts indicate in the summary Python (74%), SQL (45%), Git (25%), Data Analysis (24%) and Data Mining (22%). Those experts who write in the summary about their expertise in machine learning also mention the ownership of Linux and C++. The most popular programming languages among specialists in Data Science: Python, C++, Java, C#, JavaScript.
How do Data Science experts work?
Employers want Data Science specialists to work in a fulltime office. 86% of vacancies posted are full-time, 9% are flexible, and only 5% of vacancies contain a proposal for remote work.
 | In the Russian market, specialists in the field of Data Science are in great demand: employers open more and more vacancies related to data analysis and machine learning, educational projects are launched, and a professional community is actively developing. Therefore, together with colleagues from HeadHunter, we decided to study the representatives of this profession in more detail and draw up a detailed portrait of the Russian Data Scientist. The obtained data and insights can be useful to specialists themselves, employers, and creators of educational courses, 'said Dmitry Smyslov, vice president of personnel and educational projects Mail.ru Group '
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| | Data scientists occupy a special position in the labor market in the IT sector, thanks to the ever-increasing demand from employer companies. That is why they became the object of our joint research with the MADE Big Data Academy. In it, we tried to consider this profession from different focuses, including on demand, skills, education, in order to make the most objective portrait of the Russian date scientist and attract as many talented youth as possible to this field. Moreover, the results of our analysis will be a useful reference for corporate educational platforms, such as the hh.ru School of Programmers and the MADE Big Data Academy, in training specialists based on real business requirements and tasks. | |
When preparing the study, they used data on the growth of vacancies, employer requirements and the experience of applicants posted on the hh.ru in the 1st half of 2019, and provided by the research service of HeadHunter.
IBM launches data certification
On January 29, 2019, IBM and The Open Group consortium launched certification of data processing and research specialists to formalize training in one of the most popular areas for career growth.
Lack of skills in data analysis has often been the subject of discussion in large companies. According to the LinkedIn study, more than 151 thousand jobs of data processing specialists remain unclaimed by the beginning of 2019. This is a problem both for companies that want to use data analysis tools, and for IT giants like IBM that sell such tools. Although automation, machine learning, and artificial intelligence can narrow the gap in part, the industry intends to attract as many hands as possible.
IBM and The Open Group will review the certificates of data processing and analysis specialists, evaluating their skills and qualifications. IBM announced that certification will be available to its own employees, as such a strategy can provide new career paths. The certificate will be issued after checking the design work and passing three levels of certification.
IBM also introduced an internal education program for training data processing and analysis specialists. The program is designed for 24 months and is intended for candidates who do not have higher education in this field. The training will consist of lectures, works performed under the supervision of the curator, and practical tasks. Those who have completed training and meet company requirements will reach the Open Data 1 level 1 specification.
The first group of five students selected from several hundred applicants has already begun their studies in January 2019. IBM intends to actively distribute the program throughout the United States, but did not indicate what proportion of students will be able to get a job directly at the company itself.[3]
2017: The Higher School of Economics will teach Data Culture in all undergraduate programs
HSE is the first Russian university to begin to form Data Science competencies for all undergraduate students. As part of the Data Culture project, a set of disciplines will expand and educational tracks on big data analysis will appear.
Data Culture is a generic term for data skills and culture. The Higher School of Economics believes that the launch of a project aimed at educating students in such skills is now relevant due to the huge potential for using big data and transforming professions that, one way or another, use or can use large amounts of information. The need of the market for specialists with data analysis competencies develops into the need to educate professionals in all subject areas who understand the capabilities and limitations of data sets, the potential and features of machine learning methods, and in a number of areas and are able to use these technologies and tools.
The Data Culture project will continue to integrate elements into the educational programs of the Higher School of Economics aimed at educating students about culture and data skills. It will expand the opportunities of students already absolutely all educational programs to form competencies related to Data Science. This will allow graduates in the future to quickly and effectively integrate into the solution of professional problems at the junction of subject areas and computer technologies, which today are advanced, but in the near future will become a familiar practice.
The project includes the development of separate courses on Data Science somehow castomized to the specifics of educational programs, as well as the formation of specialized educational tracks from such courses with different degrees of complexity: primary, basic, advanced, professional and expert levels. This is due to the large variety of educational programs, whose students are differentiated by basic competencies in the field of mathematics and computer science. For programs or their blocks, a system of Data Culture courses will be offered in a certain fork of the "end-to-end level of advancement." Moreover, these course systems will be determined by the specifics of the subject areas.
The implementation of Data Culture disciplines will take place in stages. In the 2017/2018 academic year, mandatory and elective courses in the field of Data Science will be included in the curricula for part of the educational programs, but there will be more than half of them. For example, humanities students, lawyers and designers will have an introductory course on digital literacy, economists' programs will be supplemented by a discipline on machine learning, political scientists - analysis of social networks, statisticians will have a course on programming and extraction and analysis of Internet data. Since 2018, all educational programs will join the project.
"The workload of students due to the increase in Data component of programs will not change. All disciplines are included not additionally, but inside the main body of educational programs. There are no more disciplines from this, our general model of undergraduate and graduate studies remains exactly the same in terms of the number of courses, a system of disciplines of the general cycle is being built at the undergraduate school, where, including, it is possible to include courses related to computer technology and data analysis, "said HSE Vice-Rector Sergei Roshchin.
For the implementation of the Data Culture project, it is planned to attract teaching staff both from the academic environment (teachers of the Faculty of Computer Science, employees of the Department of Mathematics of the Faculty of Economic Sciences and the University Department of Higher Mathematics, etc.) and from the industry (participants in data analysis communities, participants in thematic data analysis events held in IT companies). Moreover, faculty teachers who are already immersed in data work as part of their professional activities will also develop courses within the framework of the Data Culture project for students of their own and related faculties.
Data Management
- Master Data Management System and Project Catalog
- Regulatory reference information management systems in Russia.
Leading players and top trends
- Data management
- Chief Data Officer (CDO)
- Data Science
- Data scientist
- Chief Digital Officer, CDO
- Chief Digital Officer (CDO) in Russia
- CIO (Chief Information Officer)
- Chief information security officer (CISO)
- CFO - Chief Financial Officer
- System Administrator
Notes
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