If you can’t measure it, you can’t improve: how does a young Azerbaijani deal with the ‘oil of the 21st century’? 
May 25, 2023

If you can’t measure it, you can’t improve: how does a young Azerbaijani deal with the ‘oil of the 21st century’? 

Exclusively for the YEA magazine, I conducted an interview with a young data specialist, Nijat Hajiyev.

– Nijat, tell us a little about yourself  please.

My name is Nijat Hajiyev. At the moment, I am studying for a  master’s degree in the field of data science (WSB Akademia)  in Poland. At the same time, I am working  at Amazon as a subject matter expert. I have a bachelor’s degree in marketing and as I was finishing this  degree, I began to pay attention to the data and IT side of things. It was then I realised that this area is to my liking, so I decided to continue my education in this field, combining it with courses from the internet. And now, I’ve been in this field for two years already and I am an Amazon Web Services (AWS)  and Google certified cloud data professional.

As a graduate,  I started as a marketing assistant with the leading Azerbaijani investment company, Gilan Holding and then became a social media manager and paid media analyst at the Baku-based FIL agency. I went on to hold marketing analyst and acting manager positions in various local companies. This eventually led me to the Polish branch of the American financial services company, The State Street Corporation. It was my first overseas experience and was fundamental to my transformation into a data analyst.

Tell us in more detail how and why you became sufficiently interested in data science to take courses, and eventually to get a master’s degree in this field.

In my fourth year as an undergraduate, I worked at the Savadly company and spent time at the Lotfi Zadeh Technology Centre in Baku, named after the Azeri-American mathematician who founded the concept of fuzzy logic. There, I was surrounded by people who were immersed in IT and as I communicated more and more with them, I began to gradually immerse myself also in the field of IT, and to learn from them.  I had a desire to test myself further in this area, in this new field of activity. So, I started doing research on which of the IT specialties might be the most suitable for me, and decided to stop at data. I started taking courses. The more I learned about new features and tools, the more I realised that this was  my area. Ultimately, I decided to study current job trends. Given the fact that in the future the data analytics market will be large, I decided to develop expertise in this area.

One phrase that I heard that inspired me at the time and which I always think is worth mentioning is that data will be the “oil” of the 21st century. And indeed, data plays an increasingly large role in our lives, especially during the last 20 years.

Well, that sounds pretty convincing. But, perhaps, many readers have only heard about data science in the brochures of online courses or university programmes, while not delving into the essence of this discipline. How would you briefly describe a data analyst?

For me, working with data is about how  a company uses the data it owns, in order to explore the current state of the business. Data analytics helps a  company to understand what level it is at now, and where and how to move further. In short, this is a powerful tool that allows you to describe the past and present of the company, and thus plan its future. Keeping data accurately and being responsible for it, is far from an easy job. So companies hire qualified data analysts who can deliver quality work with data. In addition, you must constantly be aware of changes in  data, and be on the alert.

It is worth noting here the difference between data analytics and data science. Data science is a higher level of this area. It includes machine learning and algorithms, building models and forecasts, as well as IT solutions for business.

What obstacles and challenges did you confront while studying data analytics?

Just as it was not easy to choose which IT field to work in, so the same was true of the field of data. All the time you have to choose which instruments to pay more attention to, where to start, what to learn first, etc. Whether to rely on your background or on technical tools. Those are the problems I’ve run into the most. And as I identified  the correct tools, I needed to figure out how to actually learn to use them. Drawing up a training plan is quite difficult for beginners. Therefore, I studied videos by YouTube vloggers with many years of experience. While these resources are great, they are not enough. In the initial stages, it is quite difficult to maintain discipline and consistency without losing motivation. You see, a beginner wants to develop skills and at the same time gain new knowledge, in a word, to move quickly. And when it doesn’t work, it reduces motivation. I stopped training several times, but then continued again.

What is the difference between a university data analytics education and the courses that you took?

There is a difference in the training schedule. For example, the university offers the study of data analytics in the form of a two-year master’s programme. And over these two years, at different semesters, different areas of both data science and data analytics are studied gradually and separately. An online course, on the other hand, is faster, but also more loaded. Online courses can be  more flexible and adaptive to market requirements, unlike the university curriculum. The teachers in the courses are more application-oriented, while in the university they are more theory-oriented.

You are going to complete your postgraduate degree, how will you use your data skills in your dissertation?

I am working on a very interesting case. The effects of artificial intelligence on unemployment. I used data skills to determine how manufacturing automation drives up unemployment. In the process of working on my thesis, I also created a machine learning model. Through exposure to the data I provided on manufacturing automation, the model was able to understand when it was presented with  a nuanced picture with positive and negative arguments. This is called sentiment analysis. Then after learning this, I gave it another set of data  that  it had never seen before  and it was already able to evaluate whether this was  positive or negative

After fully building this model, companies will be able to use it so they can classify reviews in their internal systems. This increases the image of the company and brings greater efficiency.

Nijat, what are your goals and expectations for the near future?

Currently I am at Amazon, and AWS is the largest company in the cloud sphere with around a third of global cloud market share.  AI and cloud machine learning is becoming very popular nowadays – a trend that I believe will continue well into the future. In the long term, I plan to focus on automations using AI in order to make people’s lives easy. I think for some tasks computers can work well and enable people to  spend more time with their families. I expect to see a rise in such technologies and am looking forward to taking my seat on the journey.

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