In the early 10s, analysts were not in high demand. Digitalization was not yet as global as it is now.


Data Science: first impression International business: competing in the global marketplace
About the first acquaintance with Data Science

I graduated as an engineer-economist and started working for a telecommunications company there. In it I learned about os path realpath and predictive models. Then I was not doing analytics yet, but just studying presentations from Moscow. All this fascinated me, but I understood that for professional work with data I still need to learn, so I entered the statistical department of the Moscow University of Economics.

In the early 10s, analysts were not in high demand. Digitalization was not yet as global as it is now. True, my skills were more theoretical, and the average business did not have enough computing power. Therefore, the practical application of knowledge was then rare.

Since then, for 7 years now I have been working in the restaurant business, where I analyze sales and customer base. To update my knowledge and consolidate it in practice, I decided to master the profession Python.

From Python Analytics to Data Science

I learned about SkillFactory through Facebook ads. I saw a banner with an offer to study on the course "Data Analysis in Python". The thought was just spinning in my head that it was necessary to master this language in order to work in Data Science.

After training, I did some manual data analysis using Python. The method helped to identify consumer insights that were very different from those promoted by our partners from New York. For example, we were sure that we have many regular customers, but in fact it turned out that most of the customers came to the restaurant only once. The management was very surprised at this discovery.

It became clear to me that in SkillFactory you get really useful applied skills, so I did not consider any other educational platform for teaching Data Science.

The clustering method

The topic of my graduate project in Data Science was also related to the customer base of our restaurant. It was more of an exploratory one. I did the segmentation of visitors using the clustering method. Simply put, using the algorithm, I divided customers into several groups, taking into account their consumer behavior.

I like the clustering method because it is creative. You never know how an algorithm will divide people. Within the same group, customers are somewhat similar, but in different groups people are different: someone goes to the institution a certain number of times a month, some always spend about the same amount, others often order a specific dish. And only when you read the description of the behavior, you understand by what parameters the algorithm formed the groups. People at work said the project was cool and helped them a lot. At that moment, I decided that I needed to devote even more time to my studies and went on administrative leave for six months.

About the diploma project and prospects in the profession

This algorithm, created initially for work, became my graduation project on the Data Science course. New knowledge and checking helped to improve it. I rewrote the program itself with an object-oriented approach, added information logging and launch with keys, as required by the quality standards in the field of Data Science. I wanted to do what I’m not ashamed to show and say: "This is my graduation project, and I am proud of it."

When I returned from vacation, I joined the new team as a data scientist. Now I will deal with modeling and forecasting sales growth.

In the future, I see myself as a person who will be able to do all the work in the field of big data and machine learning. I now have a broad outlook in this area and the ability to see the problems that can be solved using Data Science. The main thing is not to stop looking for something new: follow the changes in the industry, use the best tools and be able to revise the usual approaches to work.

Can a Humanitarian Become a Data Scientist?

Often times, people may not know themselves. It happens that they go to a humanitarian university only because of the circumstances. The most common reason is to avoid military service. In fact, it turns out that a person is a "techie" to the core, and he perfectly reveals himself in IT.

There were several humanities students on the Data Science stream where I studied. Some of them have reached the end of their training. If there is an aspiration, all roads are open. But, of course, it will be more difficult for such people. For successful studies, they need to independently fill in the gaps in knowledge - mathematics and programming.