Big Data

5 Steps to become a Data Analyst

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The ability to work with data is a valuable skill that opens up the prospect of becoming a super-demanded and highly paid specialist for its owner.  It tells where to study and how to become a data analyst that employers will fight for.

The secret of the popularity of the profession 

The profession of data analyst was relevant and in demand until 2020, but the pandemic gave it a new impetus. Everyone saw that data can be used to solve problems on a global scale.

Countries published statistics (and continue to do so), scientists predicted the spread of the epidemic, doctors used technologies that made it possible to determine the likelihood of a covid diagnosis by the nature of the cough and CT scans.

“ Business in the new environment had to transform – and the best results were achieved by companies that reformatted strategies based on data.”

They found points of growth and adapted to the new reality faster than others. Thus, the pandemic has also demonstrated the importance of digitalization, which has ceased to be a fashionable trend and has become a matter of survival.

Growing demand is due not only to business needs, but also to a high threshold for entering the profession – because of it, qualified specialists are constantly in short supply. Even to start a career in data analytics, you need deep knowledge of higher mathematics and programming, and training courses for such professionals from major market players are the longest (training takes from one and a half to two years).

See also:

  • What does a data analyst do and how to become a data
    scientist or data analyst: which of these professions suits you best?
  • Who are you in data analysis? Ten Roles in a Data Analyst Team

What should a data analyst be able to do? 

Jobs for data analysts can be found in almost all industries, but their skills are most in demand in the IT field (it accounts for 38% of open vacancies) and the financial sector (29%). Closes the top 3 with a large backlog of business services (9%).

Within the profession, there are different specializations. Some people are engaged in computer vision, others in geodata analysis, and others in word processing. In small organizations, the boundaries are often blurred – they need “universal fighters”, but in large companies, the depth of immersion in one direction is welcome.

At the same time, data analysts have the opportunity to change their specialization and follow the path of cross-functional career growth. This is a plus, since development within one company is always less stressful than when changing employers: you do not need to delve into the corporate culture and infrastructure again.

In terms of required knowledge and skills, most companies require data analysts to be proficient in Python (45%). Employers also want them to know SQL (23%), own data mining (Data Mining) (19%), mathematical statistics (11%) and be able to work with big data (10%). 

This is the base set. Further professional development will require knowledge in the field of data engineering (Data Engineering), infrastructure support, implementation of models and maintaining their life cycle, rapid prototyping of solutions. A data analyst must clearly understand the goals of product implementation, be able to assess the economic effect and risks.

The shortest way to the profession

A person who is interested in building a career in data science should study as much information as possible from a variety of sources. The more serious the publication and the better the information is systematized, the more likely it is to be reliable. It is better to follow the following five steps:

Understand how Data Science works

To study what professions there are and how they differ: in this area there are data engineers, data analysts, data managers and a dozen other areas that differ greatly in functionality and tasks. For example, the work of a data scientist and a data engineer is similar in many ways, but these specialists are not interchangeable and perform different tasks.

The position of Data Engineer is applied, while Data Scientist is more creative and analytical. To get into the profession, you need to thoroughly study the approaches and specifics of working in companies of different sizes: from a small start-up to an international corporation.

Find out which tasks and models are especially relevant

What is trending now and where, most likely, the subject area will develop in the near future. To keep your finger on the pulse, you can study reports from top conferences: it is there that the latest ideas are published. Often something worthwhile can be found in the preprints on There are many materials for beginners on the Technostrim channel.

Decide what type of data and in what direction you are interested in working

It is important to understand what is closer to you: marketing or e-commerce, voice assistants or drones. Due to the huge scope, there will always be something interesting and new in these professions. For example, in the banking industry, a data analyst can solve a credit score problem and engage in speech recognition processes.

Data Science will help marketers analyze loyalty card data and understand which groups of customers it is better to target which ads, and in the field of logistics it will allow to study data from GPS trackers and optimize the transportation route.

Assess the market situation: make a list of companies that have interesting tasks and technologies, a suitable corporate culture

You can refer to company tech blogs to better understand how things work from the inside, use technical articles from  IT professionals. From them you can often understand what a particular company does in terms of working with data, and how interesting it might be for you. 

Find out what skills these companies require from data analysts, compare them with their own and, if necessary, master the missing ones

Carefully read the vacancies and answer yourself where your gap is, and what skills you still need to upgrade. There are now many courses and online resources such as Coursera, Udacity, DataCamp that can help fill in these gaps. 

The shortest way for a person who already has little experience in IT is to study at courses that are conducted by large IT companies, for example, at the MADE Big Data Academy.

This is convenient because you do not have to spend time and effort searching for and selecting information. Students receive a ready-made study plan and everything that may be needed to study the material. And the icing on the cake can be a job offer. 


Finally, I want to give a little advice to novice data analysts who are looking for their first job right now. You should not create the illusion at the interview that you are well versed in what you actually know only in general terms and what you have not applied in practice.

It’s better to honestly say that while you are not in the subject, but you can quickly figure it out . This perfectly characterizes you as an employee – and if you are invited to work, the manager will be able to properly distribute the workload and set realistic deadlines for completing the task. 

It is impossible to know everything.

More recently, computer vision specialists were the most in demand, and today word processing is much more popular. Tomorrow, probably, in most vacancies, skills in working with graph neural networks and recommender systems will be mentioned.

In this industry, everything changes very quickly, but employers always give preference to specialists who seek to deepen and update knowledge in accordance with business needs and are able to quickly switch to new tasks.

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