Data Analytics

What is a Data Analyst?

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What is a data analyst?

“And it is possible to build relationships with your dog by collecting information about her behavior.”

It is the data analyst who sees the hidden patterns and answers the most important business questions: “Is it possible to give a person a loan?”, “What color button works best?”, “Where to open a new shawarma tent?” And not only. We will tell you the main things about the areas of application, salaries, skills and career prospects of such a specialist together with the head of the analytics department Artem Borov.

What does a data analyst do?

A data analyst (or Data Analyst) is a big data analyst: he collects it, processes it, and draws conclusions. Based on his reports, companies make important decisions.

For example, in online trading, you can analyze how customers use promotional codes and what content is most interesting to site visitors, and based on this, decide which platforms to use for promotion. In large chain stores, based on the findings of analysts, they optimize logistics and work with the flow of customers.

What is Big Data

This is a huge amount of information that can only be collected and analyzed in an automated way.

Suppose you play with your dog every night after work. Once you noticed that the dog barks indecently loudly, running after the ball. At the same time, he chases after the rubber toy with the same joy, but in silence. For several days, you test a hypothesis: are you testing whether only the ball really causes such a reaction? Perhaps keep an observation diary, noting the noise levels of all toys. Once you are convinced that you are right, you decide to play ball with your dog only during the day or on weekends. Relations with neighbors are saved.

This data is “small”, it is easy to collect and calculate it manually, even in your head. Big data is terabytes of disparate information that needs to be collected piece by piece, processed and translated into “human language.” For example, a pet toy company can analyze the habits of hundreds of thousands of dogs to come up with an ideal new product for them.

Which companies need data analysts?

Big data is a key resource for business: it is used in IT, retail, finance, healthcare, gaming, e-sports, telecom, marketing. The coolest and most modern companies call themselves Data-Driven . They make strategic decisions based on data.

“In fact, a data analyst is needed in any company that has data,  ”  Artem Borovoy is sure . “ The conventional network of stalls with shawarma also needs it in an amicable way in order to analyze flows, understand where it is better to open a new outlet, and build logistics.”

Here are three situations in which a business might find a big data analyst useful:

Incomplete purchases. In an online store, users add products to the cart, but then leave the site without placing an order. The data scientist first figures out at what stage the user loses interest. For example, he leaves the site when he sees a complex registration form. Then he proposes and tests hypotheses that will help retain the customer and bring the result to the desired store (checkout).

“Bad” debts. The bank wants to minimize the number of customers who do not return loans. The analyst examines what characteristics of the customer indicate whether they will make payments on time. On this basis, the client will be approved or not approved the loan.

Checking the effectiveness of the design solution. Dating app creators want to understand how users react to the color of the button. The data analyst will have to test two prototypes: some users see an option with a blue button, the other with a red one. As a result, it helps the interface designer decide what color the button will work best.

Thanks to high-quality data analysis, you can:

  • identify the current and future needs of customers;
  • predict the demand for a product or service;
  • evaluate the probability of error for different actions;
  • control the operation and wear of equipment;
  • manage logistics;
  • monitor the effectiveness of employees.

All this helps the company learn more about itself, increase profits and cut costs.

What knowledge and skills does a data analyst need?

Here is the starter pack for the aspiring professional:

  • work with data using Google Sheets, Sublime, Excel;
  • use at least one programming language to solve problems: Python or R;
  • write queries to SQL databases;
  • implement reporting in BI systems: Tableau, Power BI, Google Data Studio, etc.
  • have basic knowledge of statistics.

Depending on the direction, specific instruments can be added. For example, a web analyst needs knowledge of Yandex.Metrica and Google Analytics.

What specializations do data analysts have?

In the profession of data analyst, there is a classic IT division into junior, middle and senior analysts. But, having basic knowledge of working with data, you can apply them in other directions. Here are some specializations.

A product analyst is needed if you need to develop a product based on metrics and data analysis. The product analyst dives deeply into the topic, conducts tests and research to understand which features are popular and which are not, what problems users have when using the product.

A marketing analyst helps to attract customers through advertising, optimize costs, based on the analysis of data on user behavior and clicks.

BI analyst designs systems for data analysis and storage, tests hypotheses and automates reporting. He helps the business to model different situations, draw the right conclusions and allocate resources between departments.

Demand for the profession

In June 2021, had over 13,000 data analyst job openings.

Data is accumulating at a tremendous rate. In 2018, analyst firm IDC predicted that in five years, from 2015 to 2020, the volume of digital data in the world will double to 40 zettabytes (one zettabyte is equal to a million million gigabytes) – but in fact, the accumulation of information is even faster: in 2020, the volume of information has already reached 59 zettabytes . According to the Big Data Association, the Big Data market in Russia is growing by 12% annually.

Companies need specialists to work with this amount of data. In 2019, there were 9.6 times more vacancies in the field of data analysis than in 2015.

How much does a data analyst make?

We  analyzed open vacancies on and Habr Career . The spread in salaries was quite large. As expected, it depends on the experience and the city in which the analyst works. An intern in Perm is offered 25 thousand rubles, and a data analyst in the Moscow office of an international company earns 200 thousand rubles.

The average salaries are as follows:

Trainees and junior specialists receive from 60 thousand rubles. 8% of vacancies list the amount below, but they mostly offer part-time jobs.

Heads of departments and senior analysts receive from 170 thousand rubles. Some vacancies offer more than 250 thousand rubles a month, but they require more than five years of experience in analytics and a large pool of competencies.

In the regions, the situation is different. The maximum salary you can count on is 100 thousand rubles. But many work remotely in their city, receiving a “metropolitan” salary. On, deleted data analyst jobs account for 15% of the total.

When do you become a data analyst?

67% of specialists come to this profession from other spheres: marketing, science and even civil service. Data analysts become when:

  • want to work in IT, but do not want to be engaged in “technology” 100% of the time;
  • want to increase their income;
  • interesting to work with data, but lack of technical skills;
  • I want to automate and simplify the processes they face;
  • understand that the current profession may disappear (for example, accountants), and are looking for a new promising direction.

Where to begin?

If you understand that the data scientist is your dream profession, it is worth taking a closer look at the path that will have to be taken.

To start learning, you just need to learn Excel: to know what pivot tables are and how functions work. It is also useful to improve knowledge of statistics, SQL and Python. This can be done with free courses or simulators.