Two students share insights about HSE’s Master of Data Science program
The Master of Data Science (MDS) program from HSE University launched in 2020 as the first and only master’s program on Coursera from Russia. The program was designed for students to master the latest theoretical and practical knowledge and focus on solving real industry problems. We spoke recently with second-year students Vasilina Denyakina and Valery Sedin about their learning experiences, the wide variety of data science research topics they’re engaging with, and their career goals. We recap our conversations below, beginning with Vasilina.
Thank you, Vasilina, for speaking with us today! Let’s get started by talking about your choice to enroll in this particular program. Why did you choose HSE University when applying for your master’s in data science?
When choosing my future specialization, I wanted to learn to make effective decisions that were based on data. The MDS program at HSE University was appealing for several reasons. First, students learn about programming and algorithms from scratch. This was a big advantage over the programs offered by other universities. Second, the program is fully online. This was particularly relevant during the pandemic. The timetable is also quite flexible. Finally, the third reason was the language—all my classes at ICEF were in English, and there are a lot of mathematical terms I find easier to understand in that language.
As a burgeoning professional in the field, we know practical skills are important, but what about theory? Does the program have a good balance of theory and practice?
Despite the practice-oriented nature of the program, there is still plenty of room for theory. All of the theoretical courses are useful. They provide a good foundation, and you can build up your understanding. If you need to dive deeper into a topic, an understanding of the basics makes it much easier for you to learn more about it yourself. Theory also helps solve practical tasks, as you can use it to build hypotheses when you don’t know which approach to take. The core subjects such as machine learning and deep learning are sufficiently practice-oriented and diverse in terms of tasks. The opportunity to solve a wide range of tasks in different fields is an advantage. I try to apply new knowledge to work right away. I hope to take my current projects to a new level in the near future, thanks to the experience I’m gaining here.
Can you tell us about a project that you found to be both interesting and useful?
I liked the deep-learning project where we had to create a neural network to generate captions for pictures. It wasn’t just informative in terms of learning the subject, but it was also fun. At first glance, it didn’t seem like it would be useful, but in the end, part of the architecture of the network formed a basis for working code.
How do you feel about career prospects in the data field? Do you feel there are good employers in Russia who are interested in hiring data science specialists?
Yes, in all kinds of fields—from giants like Yandex to small but steadily developing start-ups. I think that in the future, data science specialists will continue to be in high demand across industries. The amount of data around us is growing all the time, and the ability to work with it is an asset to anyone. Plus, data science is evolving every day. This is evident from the number of scientific articles published and the new technologies that are becoming part of our everyday lives: natural language processing, computer vision, drones, and much more,
Can you share some of the challenges you’ve faced as you’ve progressed through the program?
For a long time, the biggest difficulty was maintaining a work-life balance. I’m a perfectionist, so it wasn’t easy to get everything done at work, then switch over to studying and immerse myself in subjects that were, for the most part, completely new to me. Now I’m trying to take a simpler approach to everything.
How does that approach inform your connections with the learning community? And, do you think the online format of the program has allowed you to make beneficial connections?
There’s definitely no lack of communication between students. We have multiple ways of keeping in touch: email, Slack, the Coursera forums, and less-formal chats in Telegram.
Thank you so much for sharing your thoughts Vasilina! We’re grateful for your insights, and congratulations on all your accomplishments!
Now, we’ll shift our conversation over to talk with Valery Sedin. Valery, let’s begin with a very simple question. Why did you decide to pursue data science?
Data science is the future. Specialists in this field will change the world (and make it better, I hope!).
We couldn’t agree more! Now, we’re especially interested in talking about your master’s thesis, given your unique topic. Can you tell us how you ended up devoting your thesis to an analysis of women’s football?
HSE University is home to the nationally famous Laboratory of Sports Studies, and the choice of this topic in particular comes from a desire to conduct research that genuinely contributes something new to the field of sports science. Women’s football has been researched to a much lesser degree than men’s football, which means that there are more new discoveries to be made there. When I enrolled, however, I never dreamed that I would get the chance to write “Evaluating Performance in Women’s Soccer: A Machine Learning Approach” with Dmitry Dagaev, the head of the laboratory!
That sounds like an incredible opportunity. What can you tell us about how the analytical work was structured?
I used public data from the StatsBomb database of men’s and women’s tournaments. The database contains information on every action involving the ball in every match of every available tournament. We compared the effectiveness of these actions between women and men. The model is a fairly simple neural network. It was trained using a full batch of training data and data on women’s tournaments. The model can also be expanded to cover men’s football.
Your work sounds absolutely fascinating, and we’re grateful to you for sharing your experiences in the program!
Understanding how to leverage data is key to modern business success, and learners with the applied skills to do full-cycle work with data are in high demand across the global hiring landscape. We close our post today with some additional perspective from Dmitry Borisov, who teaches the online Python Advanced course in the MDS program, about how the program is optimized to help learners prepare for career success in the field:
“The amount of data is growing rapidly, and solutions based on data are used to create effective business strategies. So there is increasing demand in the market for people who can conduct full-cycle work with data: collection and logging, pre-processing and analysis, and the creation of predictive models. In order to make sure that graduates of the program have the necessary skill set to work with data, many of the courses in the master’s program are based on practical work. There are lots of tasks to solve, and each of them has unique solutions that are shared after the students submit their work. This provides the students with good templates and methods to improve their own solutions. During the course, students also have to deal with two complex tasks with a comprehensive review from the teacher.”
You can learn more about the Master of Data Science (MDS) program from HSE University by clicking here.
The post Preparing for Career Success in Data Science appeared first on Coursera Blog.
Free: 7-Days Trial from Coursera