Artificial Intelligence & Big Data Analytics - EN

Across the Data Universe. Big Data Analytics and Artificial Intelligence Master Program at NSU

Description

The innovative project-oriented training in Data Science is led by the minds that envision and the hands that shape the future of the Big Data World. Your diploma will read Master of Science only because our "Wizard" degree is not yet official.

Why attend this program?

Research opportunities in real world-class science. You will learn not only how to apply data science and machine learning methods, but will have an opportunity to develop new or upgrade existing ones, for example improvement training procedure of state of-the-art neural network.
Project based and practical-oriented learning. You will gain experience in projects with real tasks from our partners. Program is designed so that you are involved in all stages of Data Science and Machine Learning project development and implementation cycle beginning with understanding of business needs, trough detailed project planning and management, ending up with implementation of new product or technology.
PhD opportunities. This program also opens up possibilities for a PhD in NSU and the world's leading universities.
Wide range of hot domains.You will have an opportunity to apply your data science and machine learning skills in leading scientific and industrial domains such as Oil and Gas, Healthcare, Social networks, Cognitive data science, Telecommunications, Instrumentation. Hackathons and data science competitions with tricky and unsolved tasks. You will have an opportunity to participate or to manage hackathons held by Big Data Analytics & Artificial Intelligence master program and our partners.
Career. Alumni of our department work for the following well-recognized companies: Microsoft, Facebook, Google, Parallels, Yandex, Kaspersky, Huawei etc.

Program goal

A true Data Scientist solves a problem by combining hardcore science and breakthrough data mining technologies with the inexplicable art of human understanding. As a student, you will take part in real projects, working with a team through all project stages to acquire deep knowledge and master your skills in analyzing the core problems of your customer, planning and managing project resources, engineering software, collecting and processing all sorts of data, and discovering the precious insights that put the whole data puzzle together.

Duration of study

Full-time study, 2 years

Language of instruction

English

Curriculum

1st year

1. Problem statement

	Business understanding	Ready solutions	Scripting
BA	Business Analysis: Communication & Requirements
Engineering	Python	Introduction to ML	Introduction to ML
Management	Requirements management	Requirements management	Requirements management
Mathematics	Operations Research	Information Theory and Cryptography	Information Theory and Cryptography
Advancing	Frontiers of BDA & AI	Hackathons	Hackathons
Research and Development	Laser physics / Social Networks Analysis /Oil & Gas / Quantum Machine Learning / Instrumentation / Healthcare / Telecom

2. Science

	Access to data	Mining	Presenting
Business	NDA	Internship	Presenting to SH
Engineering	Storage Technologies	Storage Technologies	Storage Technologies
Management	Project Management	Project Management	Project Management
Math	Machine Learning 2	Machine Learning 2	Machine Learning 2
Advancing	Natural Language Processing	Formal Semantics	Deep learning
Research and Development	Laser physics / Social Networks Analysis /Oil & Gas / Quantum Machine Learning / Instrumentation / Healthcare / Telecom

2nd year. Innovative

3. Management

	Deployment	Scaling
Business		Technology Entrepreneurship
Engineering	Distributed Computer Systems	Clouds
Management	Product Management	Product Management
Mathematics	Digital Image Processing
Advancing	Academic Writing	Biomedical Engineering

4. Thesis: Final State Certification

Program structure

First year, Fall Semester

Core courses	Credits
Business Analysis	4
Python programming language	4
Introduction to Machine learning	2
Frontiers of Big Data Analysis and Artificial Intelligence	2
Methods of Operations Research	4
Information Theory and Cryptography	2
Philosophy of Artificial Intelligence	2
Project Seminar	4
Scientific Seminar	2
Internship	4
Optional courses	Credits
Russian for foreigners	2

First year, Spring Semester

Core courses
Machine Learning	4
Frontiers of Big Data Analysis and Artificial Intelligence	2
Storage Technologies	4
Project Management	3
Scientific Seminar	2
Natural Language Processing	4
Internship	4
Research work	3
Electives
Deep Learning	4
Formal Semantics	4
Optional courses
Russian for foreigners	2

Second year, Fall Semester

Core courses
Digital Image Processing	3
Distributed Computing Systems	3
Technology Entrepreneurship	3
Project Management Practice	2
Scientific Seminar	2
Internship	5
Research work	6
Academic writing (English)	3
Electives
Social Mining	3
Biomedical Engineering	3
Web mining	3

Second year, Spring Semester

Obligatory courses
Research work	15
Pre-graduation practice	7
Scientific Seminar	2
Thesis defense	6

Master dissertation

Examples of topics

Development of a system for predicting blood sugar levels based on machine learning
Developing a subsystem for retrieving event data
Development of a module for an expert system for analysis and monitoring of communications, solving the subscribers identification problem
Developing Big Data platform for cognitive analysis
Applying the principle of rival similarity search for significant features in the processing of large amounts of data (Big Data)

Training base

Each student can select a domain based on his own interest. Courses specified for domains are supported by companies that have great experience applying data analysis techniques to solve innovative problems.

Oil and gas – oil products price prediction, well production optimization, health-safety environment. Companies: Digital Field technologies, Gazpromneft
Healthcare – medical experiment data processing; real-time patient data processing for alarming and prevention of risks, analytic modules for healthcare information systems. Organizations: Novosibirsk Research Institute of Circulation Pathology, Novosibirsk Research Institute of Traumatology and Orthopedics, Institute of fundamental medicine and physiology SB RAMS, Federal Neurosurgery center
Social networks – Identifying social event preparation by social network activity, A/B testing, semantic analysis, sentiment analysis, reputation management systems Companies: Game Banners Network (game development), Singularity.NET, Aigents Group
Telecommunications – network traffic analysis, advertisement targeting, mobile marketing. Companies: Eltex LLC (business), Eyeline Communications CIS (business), Huawei
Instrumentation – analyzing data from CERN, software for new electronic equipment. Organizations: Budker Institute of Nuclear Physics (scientific), Uniscan, LLC (business, instrumentation), TION (air purification)

Key personnel

Evgeniy Pavlovskiy

PhD in Math, certified EMC Data Science Associate

Ivan Bondarenko

Researcher at Neural Networks and Deep Learning Lab, MIPT, Solution Architect at DataMonsters, Neural networks for Natural language processing lecturer

Denis Bondarenko

Chief Technology Officer at IMTS.Pro, Storage Technology courses lecturer

Nickolay Tolstokulakov

Certified DLI NVidia teacher, Lecturer of Introduction to Machine Learning

Alexander Savostyanov

PhD in Biology, PhD in Philosophy, Senior Researcher at Research Institute of Physiology

Grigoriy Khazankin

Certified CCNA, leading engineer at Research Institute of Physiology, Distributed Computing Systems and Biomedical Engineering course lecturer

Alexey Stukachev

PhD in Math, Senior Researcher at Sobolev Institute of Mathematics, Formal Semantics course lecturer (Natural language processing)

Florian Gouret

Researcher in Novosibirsk State University Formal Semantics course lecturer (Natural language processing)

Valeria Idrisova

PhD in Math, Engineer-researcher at Sobolev Institute of Mathematics

Vyacheslav Mukhortov

Project Management course lecturer, director of Inteks LLC

Fees and Financial support

Tuition fees

Tuition fees is U.S. $6500 per year.

Up to 25% discounts available:
Program discounts are available. If the applicant is eligible for several discounts, they must choose one to apply to their contract for educational services. The discount is provided for the first year of study. The size of the discount is determined by an analysis of the documents submitted to the international recruitment office.

The documents necessary to evaluate applicant discount eligibility:

A resume that includes information about published articles, contact information for the person providing a recommendation from your place of work, links to sites with published source codes, and information about conferences attended as a participant or speaker.
A copy of published articles in English.
Certificates for conference participation.

Publications: In-specialized artificial intelligence or big data journals, computer science or other scientific journals, conference materials.
Experience: machine learning projects; personal open source machine learning projects; work as a business analyst; data analyst.
Conferences: presentation at a profile conference (artificial intelligence, big data); presentation at a non-profile conference; participation in profile conferences.

Scholarships

Every year foreign students have an opportunity to apply for the Russian Government Scholarship Program

Benefits: full tuition, monthly stipend.
Application deadline: depends on the country of applicant

You also have an opportunity to participate in the "Open Doors” Russian Scholarship Project and win a full scholarship to study Master’s program.

Benefits: full tuition, monthly stipend.

Career opportunities

PhD in math in Siberian scientific center. After completing the program, you will have an opportunity to go to graduate school in Novosibirsk State University.
PhD in math in the other world's leading universities. After completing the program, you will have an opportunity to go to graduate school Skoltech, MIT, Stanford University, etc.
Data scientist (also known as Machine Learning or Deep learning engineer, Modeler or DataMiner). You will be able to transform data into knowledge, build custom models, use machine learning for solving business problems.
Data engineer (also known as Data structure engineer or DevOps). You will be able to build data infrastructure and ETL pipelines, transform research models into production quality systems, develop architecture that helps analyze and supply data that helps analyze and supply data.

Entrance requirements

Copy of the 1st page of your travelling passport with your personal data
Legalized (if required) educational certificate
Legalized (if required) academic transcripts (transcript of record, university degree/s, diploma supplements)
2 photos
English language course marks sheet/TOEFL certificate or other international certificates (in case English is not your mother tongue and your previous education wasn't in English)
Curriculum Vitae
Motivation Letter (1-2 pages)
Medical certificate of overall health condition
HIV + AIDS certificates

Note! For enrollment you have to submit both original and notarized Russian translations of the required documents to the NSU Admission Office.

Application deadlines

fill the application form