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How to become a Data Scientist?

Margareth Nail

Data Scientists are among the most sought after in the labor market at the moment. For those who do not know (many still do not understand the essence of this profession), Data Scientist is a person who is able to draw meaningful conclusions from large databases, using the mathematical apparatus and programming. BNT asked several experts about how you can become such a superman and how long it will take.

Where are Data Scientists in demand?

21-02-2019 14:55:52  |   Investments
The Data Scientist profession is now at its peak of popularity. Many companies are now actively looking for qualified specialists in this field and are ready to offer interesting projects. Predictive analytics and prescriptive analytics are gaining momentum in various industries, from information technology to agriculture. For example, one of the experts collected data from sensors on farm animals in order to build models predicting their possible diseases.

“Working with big data has become a separate industry, parts of which are applicable to any business: advertising, e-commerce, finance, manufacturing. Wherever there is more than one parameter, there is a place for deep data analytics”,

— comments Anton NEMKIN, managing partner of AT Consulting. For example, in the UK, Data Scientist's initial salary is about 40,000 pounds a year or more. Qualified employees in this area and in high demand all over the world, and this demand is rapidly growing, says Adam LICHTL, PhD, who is the founder of Pacific Data Science and adds that there has been an extensive amount of coverage about the workforce shortage in this field:

“Everything is becoming digitized, new data is available all the time, and volumes of data are increasing. Businesses need to use this data wisely to make good decisions. Unfortunately, there’s a talent gap for available data scientists because, like other highly skilled professions, it takes years of training and experience to do the job effectively and efficiently.”

What skills should Data Scientist have?

Experts have identified the following key areas in which Data Scientist should be competent:

  • mathematical basis (probability theory, mathematical statistics, optimization methods, linear algebra);
  • programming skills (python, SQL, C ++, and very desirable Hadoop);
  • knowledge models and machine learning methods (ML), practical experience of using ML;
  • communication skills with the ability to understand the business and its objectives.

Data scientist should be able not only to analyze large databases to build complex models, but also to make meaningful conclusions from this analysis that are useful for the sphere in which he operates. "In addition to owning the technical knowledge base, data scientist should be able to explain the methods of analysis and the results to people from other professional fields", — adds Anna RUMYANTSEVA, Data Scientist at Hitachi Vantara.

Who can become a Data Scientist?

18-01-2019 14:30:02  |   Guest posts
Of course, for young professionals who have a mathematical or IT-education it’s easier to become a data scientist. However, if you have patience, then you can become an analyst of big data at almost any age, assuming that your math knowledge is above basic level and analytics is your natural ability.

There is some news for the humanitarians — it would not be easy without a basic knowledge of mathematical analysis, looking into an endless stream of numbers. The good news is that the toolkit is being simplified, and knowledge becomes more accessible. There are many platforms (KNIME, DataRobot), that democratize the profession and simplify the process of creating models. It allows people with different skills to get acquainted with the work of Data Scientist. However, according to the experts, the fundamental knowledge of math is still very important for a career: it allows to master new algorithms in this area, and it will be impossible to work with some certain projects without it.

Anybody can become a data scientist if they have the skills and knowledge needed, says Carol DONOHUE: “I am 56, and have just made the transition. From my undergraduate, masters and Ph.D. classes I have linear algebra, statistics, and advanced calculus knowledge which when I took a machine learning class last year, about 75% of the knowledge is unchanged.”

How long should you study?

Andrei MOGORAS, head of the machine learning, data processing and analysis department at Penenza.ru platform, remarked that this is very individual:

“I think you can get basic skills in mathematics, programming and machine learning in a few months, and then only practice, the more, the better. In my opinion, an average specialist needs at least a year of practical application of ML. The experience of participating in ML competitions (kaggle, etc.) is also very useful, but it does not replace the practice of solving real-life business problems.”

However, in order to become a high-end specialist, you must obtain a university degree in physics, applied mathematics or programming. In addition, self-study and participation in projects related to Data Science will be needed. It depends on the enthusiasm and willingness to work hard a lot. Of course, it would be nice to take a profiled internship that can take 1-2 years. The whole cycle will take about 6 years in total. A further improvement of kills depends on what kind of career a person wants to build.

Experts are sure that, it is necessary not only to get an education in order to become true Data Scientist, because diverse practical experience and natural abilities are also important. As for the training, there are many special Data Scientist training courses available now, as well as specialized technical universities.

Of course, you can learn almost everything at any age, but in order to become a first-class expert in the field of Data Science, you need to put maximum effort. If you’ve got no love for mathematics and analytics, and if you are far from programming, then it is better to choose some other profession.

Image courtesy of Medium

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