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Graduates of Russia’s First Online Master’s Programme in Data Science Talk About Their Studies at HSE University

Graduates of Russia’s First Online Master’s Programme in Data Science Talk About Their Studies at HSE University

© HSE University

The English-taught Master's programme ‘Master of Data Science’ run by the HSE Faculty of Computer Science and its industrial partner, Yandex, brings together students from more than 30 countries. The programme is implemented completely online. Among the graduates of the second intake of the programme are Calvin Tee from Singapore and Lyubov Sharaborina from Moscow, Russia.

‘We did more than 200 practical projects and tasks of various sizes’

Calvin Tee from Singapore started managing data in his own logistics company two years ago and decided to gain fundamental knowledge in data science and analytics. He was keen to understand and follow research and developments in this field, but industry training and fragmented self-study outside of an academic programme did not help him to form a relevant knowledge base.

Calvin Tee

Why did you choose HSE University for a degree in Data Science?

I chose the programme at the front end of the pandemic. With shutdowns of workplaces and schools, a programme that was not dependent on on-campus attendance made sense. The syllabus of the MDS programme appealed to me as being very structured, and in particular I appreciated that it sought to cover the mathematical foundations, since I definitely had knowledge gaps in that area. 

Also, the passion of the academic leaders and study office in promoting the programme left a very positive impression.  

What projects on the programme did you find most interesting?

We did more than 200 practical projects and tasks of various sizes over the course of our programme! The most interesting ones for me were those that bridged theory and implementation—these were often challenging to get through, but were rewarding learning experiences. I very much enjoyed the insights gained from working in a research team for my final thesis on fine-grained recognition of pets—the energy and dedication of my supervisor, Dr Nikolay Arefyev, and my team mates were very inspiring!

The most interesting aspect was learning not just from the programme, but also from the perspectives and insights of my fellow coursemates, many of whom are experienced practitioners. The most useful thing for me is the confidence and savviness to engage in the data science field that I have gained.

I first seek to explore opportunities at work to apply myself in my core domain of logistics. Beyond this, I will be looking out for worthwhile projects to do or contribute to, to build up my credibility, track record and network in data science to yield more professional avenues for the future.

The Master of Data Science programme prepares professionals in three fields:

 Data Scientist—machine learning professionals capable of solving both traditional business problems (demand forecasting, churn prediction, text data analysis, segmentation, etc) and more advanced tasks (question-answering system design, image analysis, realistic sample generation etc).

 Machine Learning Engineer—professionals at the intersection of data science and development who understand and utilise advanced technologies for big data collection, storage and analysis. They are able to write effective code and design complex systems related to machine learning-based services.

 Researcher in DS—machine learning professionals who are familiar with state-of-the-art outcomes, understand the theoretical foundations of machine learning, and are able to work to improve existing methods.

What was the biggest challenge while studying?

The most challenging aspect would have to be balancing my time between studies, work and family. My studies took many a late night, not to mention numerous weekends where I was nearly absent to my family and friends. I needed to be very disciplined with time management, and I made adjustments to my furnishings at home to have a study space that allowed me to be undisturbed and focused. I also sought to be active on chats with fellow course mates located around the world—it really helps that one is not alone on this journey!    

To future students of the programme, I would recommend the following: be clear about your motivation for taking the programme—write it down and refer to it when your resolve wavers. Be upbeat and actively participate—the course experience is what we collectively make it to be. Determine your thesis topic early—finding fruitful paths and getting productive takes time.  

‘What seemed like science fiction a decade ago is reality today’

Lyubov Sharaborina already has a degree, but when she got interested in natural language processing (NLP), she decided to go into data science.

Lyubov Sharaborina

Why did you choose HSE University to study data science?

I considered several programmes, including long-term courses, but settled on HSE University. The Master of Data Science is a full-fledged master's programme, and the remote format made it possible to combine study with work. Another decisive factor was the fact that HSE University accepted students with any background (with a bachelor's degree in any field) upon successful completion of the entrance examinations, and the programme included all mathematical disciplines, from discrete mathematics to mathematical statistics.

The study of data science is linked to the solution of real industry cases. How practical is the programme?

The study process ensures that all new knowledge is integrated into practice, and there were a lot of practical tasks. The training required a lot of independent work and the study of additional materials. Students without a mathematical background may find it harder. I spent about ten hours more each week than my classmates to complete the assignments. I was particularly interested in doing tasks on algorithms and NLP. Without exception, all the assignments were ‘real-life’ tasks that one can face in an interview or at work.

What is your final project about?

It is about machine reading methods for named-entity recognition. This is an advanced technology that allows you to extract useful information from texts. Throughout my work, I had a supervisor who was always available to answer my questions. I was completely confident in the quality of my project, so I was not afraid of anything in the defence process. Some of my classmates have even written academic papers based on their research.

Is it convenient to study online?

The remote format of study turned out to be productive for me. I could listen to lectures and complete assignments when I had time. Deadlines have always kept me alert. I sometimes had questions after the lectures, and I could always discuss them with the tutors and my classmates. We had a friendly atmosphere, and everyone was ready to help each other.

How are you going to develop professionally?

I teach Python and plan to develop as a data scientist in the future. I still want to work as an NLP specialist and I am on my way to achieving that dream.

Data science is constantly evolving. What seemed like science fiction five or ten years ago is becoming real today. For example, in the field of NLP, neural networks have made it possible to create realistic images based on text. An example of this is the Imagen neural network from Google—a kind of illustration that indicates a very high level of understanding of the text. I think that data science will see huge growth in the near future.

The deadline for admissions to the Master of Data Science programme has been extended to August 19.

By Ekaterina Zinkovskaya, HSE eLearning Office