Today, Data Science specialists are needed in almost all areas of business. It's not just about financial
The demand for data science specialists is differentqualifications are growing every year. According to the MADE Big Data Academy from Mail.ru Group and the recruiting portal HeadHunter, in 2019 there were 1.4 times more vacancies in the field of data analysis compared to 2018. And the number of vacancies in the field of machine learning has grown 1.3 times.
The earnings of data scientists are also growing. According to HH.ru, even a junior in Russia receives about 120 thousand rubles, while a business analyst can already count on 170 thousand rubles and more, and a big data analyst - from 200 thousand rubles.
Who is in demand and why?
Most often in Russia they are looking for data scientistsfinancial and IT companies. And the most common requirement for applicants is knowledge of the Python programming language. It occurs in 45% of jobs in data science and nearly half (51%) in machine learning.
Of course, the number of data scientists is also growing. According to HH.ru, 246 data analysis specialists and 47 machine learning specialists post their resumes every month.
The list of requirements from applicants also includes:
- knowledge of SQL;
- possession of data mining (Data Mining);
- confident knowledge of mathematical statistics;
- ability to work with big data;
- possession of C ++, Git, Linux.
At the same time, about 65% of vacancies in the field of analysisdata and 50% of vacancies of specialists in the field of machine learning are in Moscow. St. Petersburg ranks second in Russia (15% and 18%, respectively). Of course, job seekers are also mostly concentrated in the two capitals. But today, in order to undergo training, it is not necessary to move somewhere, but to work in a remote format, on outsourcing, is already becoming a new format for organizing the workflow.
Where to study for a data scientist and what is needed for this?
There are several approaches to learning indata scientist. One of them is a more classic one, to enter a university in IT areas. This can also be done abroad. For example, studying for a Master in Data Science at an American university can cost you a very impressive amount: from $ 30 thousand to $ 120 thousand. Even online courses at foreign universities in this specialty cost at least $ 9 thousand. There are those who are not ready spend on your training on such a scale, because such costs still need to be recouped, but this will not happen immediately. For example, data scientist Rebecca Vickery, who has been working in this field for 10 years, has created her own program, according to which she independently studied Data Science. This approach also has its drawbacks: lack of feedback and support from a mentor or teacher, distance from the team, working alone, and, finally, many find this learning process boring.
Another option is online training inspecialized digital schools such as SkillFactory. Students there are not only taught a set of techniques and techniques, but also taught to learn. In addition, each student will have a mentor who provides support and assistance, and all the work done in the learning process can not only be used as a portfolio. While still a student of SkillFactory, the future data scientist enters the industry community - this not only helps to find a job, but also to communicate with colleagues and share experiences. The online school is convinced that it is not enough just to learn new technologies - you need to master new approaches and new ways of thinking. And it is difficult to cope with it alone. Therefore, all students give each other feedback, exchange code, help to find errors and share problems and real cases.
What a Junior Data Scientist should be able to do:
- use basic algorithmic constructs and Python data structures to design algorithms;
- visualize data using Pandas, Matplotlib, Seaborne;
- create industrial quality models using classic machine learning and neural networks to solve Data Science problems;
- evaluate the quality of the model (precision / recall);
- integrate the solution into production and business in general;
- work with data warehouses of different types;
- work with big data analysis tools;
- receive data from web sources or via API;
- apply methods of mathematical analysis, linear algebra, statistics and probability theory for data processing.
If these skills seem very difficult to you, then you can take the Data Scientist Profession courses.
Who is a data scientist and what should he be able to do?
At its core, Data Science is next"Evolutionary" step of humanity in working with data. Earlier mathematicians and statisticians solved similar problems. Now, with the advent of artificial intelligence, optimization and computer science have entered the methods of data analysis, which means that a new approach to finding solutions based on data has become much more effective than the previous "analog" methods.
A data scientist's job begins with collectingbig data sets: structured and not. Then they are converted into a format that is easy to read. The next stage: visualization and work with statistics. Machine and deep learning, probabilistic analysis, predictive models, and neural networks are used as analytical methods.
Five bases for a data scientist
- Artificial Intelligence (AI) is an areadedicated to creating intelligent systems that work and act like people. AI is related to the similar goal of using computers to understand human intelligence, but is not necessarily limited to biologically plausible methods. Intelligent systems existing today have very narrow areas of application. For example, programs that can beat a person at chess cannot answer questions.
- Machine learning -creating a tool for extracting knowledge from data. ML models are trained on data independently or in stages: training with a teacher on data prepared by a person and without a teacher - working with spontaneous, noisy data.
- Deep learning -building multilayer neural networks in areas where more advanced or faster analysis is required and traditional machine learning fails. "Depth" is provided by a number of hidden layers of neurons in the network that perform mathematical calculations.
- Big Data - working with bigvolume of often unstructured data. The specificity of the sphere is the tools and systems that can withstand high loads.
- Data Science - inthe core of the area is the empowerment of data sets, visualization, collection of ideas and decision-making based on that data. Data scientists use some of the machine learning and Big Data methods: cloud computing, tools for creating a virtual development environment, and much more.
Like any other profession, mastering DataScience starts with the basics - the study of mathematics, linear algebra and, of course, statistics. For a serious understanding of Data Science, a future specialist will need a real university course in probability theory (including calculus). Fortunately, today such materials are easy to find on the Internet or even sign up for one semester at the best universities in Russia on the Open Education platform. Or take the full Data Science course at SkillFactory, where basic knowledge will be the first step in mastering a new profession. Mathematical knowledge is primarily important in order to analyze the results of applying data processing algorithms. Of course, there are strong engineers in machine learning without such education. But these are mostly rare cases.
The second step in becoming a data scientist is programming. It is enough to learn at least one language, having mastered all the nuances of its syntax. As mentioned above, one of the most in-demand languages is Python.
Machine learning - the third componentdata scientist profession, when he no longer needs to write instructions for computers to perform certain tasks. ML consists of three main forms: supervised learning, unsupervised learning, and reinforcement learning. You can read more about each type of training in our large material with Professor Jan Lekun.
And finally, the last step is Data Mining (analysisdata) and data visualization, which is an important research process and involves the analysis of hidden data models in accordance with various options for translating into useful information that is collected and formed in data warehouses to facilitate business decisions designed to reduce costs and increase income.
Despite the fact that education can be obtained ina fairly short time, a data scientist needs to confirm his qualifications regularly by taking highly specialized courses, participating in hackathons, open competitions and when searching at work. Independent confirmation of your qualifications will be an advantage. For example, the advanced profile on Kaggle, which has a rank system. You can go from novice to grandmaster. For successful participation in contests, the publication of scripts and discussions, you receive points that increase your rating. In addition, the site notes which competitions you participated in and what your results are.
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