Yandex Blog

As one of Europe's largest tech companies, we believe we have a responsibility to create learning opportunities for the current and future generations of data scientists.  As many industries continue to enhance their businesses with AI and data science, it's critical to teach students the skills they need for the jobs of tomorrow.  While we began our education initiatives in our core market of Russia, our education goals are part of a broader global learning environment.  We're committed to steps that reflect the needs of learners around the world and extend the reach of our academic programs to as many individuals as possible. 

Successful education is rooted in the exchange of knowledge - sharing expertise and learning of experts from different parts of the world.  Today we are expanding our global efforts in multiple ways and continuing to engage education professionals from around the world to enhance both our programs and others’.

Adapting to Local Learning Environments

The Yandex team members who founded our Yandex School of Data Analysis headed to Israel in 2018 to establish a program suited for local learners.  Through collaboration with local experts, we created Y-DATA at Tel Aviv University, a one-year career advancement program meant to bridge the gap between online courses and a full-time Master of Science program.  YSDA personnel designed the Y-DATA curriculum to precisely reflect the needs of the data science community in Israel.  Y-DATA provides fundamental skills for data science careers but also uniquely offers engaging hands-on projects with local companies. 

Our team in Israel has continued to take steps to integrate Y-DATA into the local data science community and boost the exchange of knowledge.  For example, the Y-DATA team hosts regular meet-ups in Tel Aviv that are open to anyone interested in learning more about machine learning and data analysis.

Offering Programs for Global learners by Global Experts

We welcome international experts to contribute to our programs for learners both in Russia and elsewhere in the world.  By welcoming experts from different institutions and education systems, we drive more diverse learning opportunities for students. 

One example is the Machine Learning in High Energy Physics summer school.  Each year since 2015, we've helped organize a ten-day machine learning summer program.  Working with one of our academic partners in Moscow, the Higher School of Economics, we annually stage the school at a different European host and welcome experts from around the world to teach.  This year, the DESY research center in Hamburg, Germany, hosted 71 postgraduates and postdoctoral researchers from 17 countries.  MLHEP is an excellent way for us to engage with global learners interested in machine learning, as well as academic institutions across Europe.

Similarly, we invite experts from around the world to teach at our Natural Language Processing (NLP) Week that we host at Yandex headquarters.  This past year we welcomed two international data scientists to NLP Week.  Wilker Aziz of the Institute for Logic, Language and Computation at the University of Amsterdam taught courses on latent variable models, deep generative models, and advanced topics; Mirella Lapata of the School of Informatics at the University of Edinburgh taught semantic parsing.

Establishing Online Courses for Learners Everywhere 

We believe it's critical to make education opportunities as accessible as possible.  By partnering with Coursera, the leading online education provider, we bring data science tools to an even larger audience, unhindered by physical location.  We host several online specializations on Coursera in English, which are open to anyone around the world interested in data science. 

In April, we attended the Coursera Partners Conference to engage other academic partners.  We shared more about our experience leading the way with collaboration between industry and academia.  Through in-depth cooperation with academia, we can help define the skills today’s learners need for the jobs of tomorrow.  We were also honored to accept an award from Coursera for our Advanced Machine Learning specialization.

Engaging in Open Dialogues with International Representatives 

Besides bringing our programs to an international audience and adapting them to local learners, we also share our expertise and engage in cross-cultural dialogue.  By meeting with representatives who have a similar passion for education, we can share our knowledge and learn how other education leaders are shaping their tech programs.  Russia has traditionally excelled in STEM, and we are always eager to share our current work and vision for the future in this area. 

As part of our efforts to engage with the global education community, we recently welcomed Shamma Al Mazrui, the Minister of State for Youth Affairs for the United Arab Emirates, to our Yandex office.  Our academic team spoke with Al Mazrui about our shared interest in expanding access to data science education.  Al Mazrui discussed the various education projects of the UAE, and we presented information on Yandex's academic programs.  Our team shared how the Yandex.Textbook service provides elementary school teachers with interactive assignments.  For older learners, Al Mazrui learned how Yandex.Practicum provides tools for adults seeking to retrain themselves for new careers in data science.

Learning more about the education systems of other countries is an excellent way for us to tailor our academic programs to a global audience.

We look forward to creating more opportunities for global data science learners and expanding the reach of our educational programs.

We’ve long felt that being one of Europe’s largest tech companies means we have a responsibility to help educate current and future generations of data scientists.  We’re continually looking for ways to advance machine learning for our users and the greater AI community, and one way of doing that is to encourage data science learning.  Our education initiatives offer opportunities for a broad range of learners, from those interested in online courses to professionals looking for career advancement in computer science.  Many of our education programs stem from our collaborations with higher education institutions, which enable us to work with the brightest scientific minds to teach diverse topics in machine learning. 

The annual Machine Learning in High Energy Physics summer school which we help organize is an excellent example of our commitment to academic collaboration.  The Yandex School of Data Analysis and the Laboratory of Methods for Big Data Analysis at Moscow’s Higher School of Economics (HSE) have annually staged the summer school since 2015.  Each year, we work with a different scientific partner in Europe to host the summer program.  This year, the DESY research center in Hamburg, Germany, will host the fifth MLHEP summer school from July 1st to July 10th.  The program will welcome 71 postgraduates and postdoctoral researchers from 17 countries, with most coming from the EU, the United States, and Russia.  

The MLHEP summer school focuses on the emerging fields of data analysis and computational research in High Energy Physics (HEP), also known as particle physics.  Machine learning helps solve essential problems in HEP that range from online data filtering and reconstruction to offline data analysis. Over ten days, students at the summer school will have both a theoretical and practical introduction to machine learning in HEP, covering topics from decision trees to deep learning and hyperparameter optimisation.  Students will have the opportunity to apply what they learn with concrete examples and hands-on tutorials.

Participants in previous years have come from all over the world with diverse backgrounds to enhance their machine learning skills.  

“During the MLHEP school, I widened my understanding of machine learning methods,” says Mikkel Bjorn, a DPhil student in Elementary Particle Physics at the University of Oxford.  “I learned new ideas about where the techniques we studied can be useful in the work of myself and my group.” 

Alexey Kharlamov, a recent graduate of HSE, adds that “Most of all I liked the atmosphere of the program, which cultivated an interest in machine learning as a result of working with both motivated students and excellent teachers who love their subject.  In such an environment, it’s exciting to develop your data science skills.”

The MLHEP summer program emphasizes both theoretical knowledge and practical application to ensure students come away with applicable skills.  We organize a related machine learning competition that spans two to three months to provide a continued opportunity for students to apply their knowledge.  The competition is inspired by Yandex’s long-standing relationship with CERN, where researchers from Yandex have been working with physicists to solve issues related to matter and energy.  In particular, students will be creating solutions related to the Large Hadron Collider beauty experiment at CERN. The competition will require students to process particle information using modelling techniques.  The two-part contest will be similar to Kaggle machine learning competitions and take place in a co-learning environment, encouraging students to work together to solve challenges.

Lecturers from the Faculty of Computer Science at HSE, a department Yandex co-founded, will teach most of the sessions.  As we’re always eager to promote an atmosphere of collaboration in our education initiatives, we’re excited to announce they will be joined by several guest lecturers from Facebook, Oracle, Caltech, and more, who will be teaching sessions on causal inference, probabilistic programming, and other machine learning topics.

The MLHEP summer school is yet another exciting opportunity for Yandex to collaborate with academia and encourage data science learning.  For more information about the program, please visit the website and follow @yandexcom on Twitter to get updates during the summer school!