School of Engineering and Digital Sciences
School of Engineering and Digital Sciences
School of Engineering and Digital Sciences
| MSc in Data Sciences Apply now |
Digitalization is a phenomenon which now permeates nearly all human endeavor and most aspects of daily life, encompassing the spheres of education, finance, health, industry, and culture.
It is now possible to collect data on the scale of Exabytes (1 billion gigabytes), from an incredibly wide range of digital sources, such as cameras, sensors (thermal, light, motion), smart phones, and medical devices. In addition to the data sources, we have enormous volumes of the routine transactional data of digitalized daily life, data that was previously uncollected or, if collected, ignored.
It is not just the data but the meta-data about the data, including the details of how, when, and where it was collected, that facilitates the identification of patterns, and, conversely, anomalies, in ways that are increasingly useful. This phenomenon of high-volume data collection is motivating the creation of advanced analytic techniques that can achieve results across disciplinary boundaries.
What began as “Big Data” has become “Data Science, enabling significant advances in applications ranging from health care (improved diagnostics and outcomes) to energy consumption (smart homes, factories, and cities). The ubiquity of digital devices is greatly expanding both the quality and quantity of data generated, with an effect amplified by the rapid emergence and utilization of the IPv6 protocols and the resulting plethora of “internet-of-things” applications.
The exponential growth in data sources and volume has presented non-trivial challenges to every step of data management: collection, storage, validation, preservation, transmission, access, analytics, and also raises new challenges to the related issues of anonymity, privacy, and security.
Data Science is the scientific discipline that covers the full range of the data life cycle. It includes both theoretical and practical methods for organizing, processing, and analyzing the data and transforming data into information and, increasingly, information into actionable “intelligence”.
In this regard, “intelligence” is feasible through major innovations in the application areas of Artificial Intelligence, Machine Learning, and Deep Learning, which are benefitting from new models of cognition and learning, and substantial improvements in computing resources and methods to manage computational complexity.
The result is that Data Science is achieving results that were previously conceived only speculatively or in the literature of science fiction. As but a single example, a machine recently taught itself chess over a weekend, and proceeded to defeat the top human players in the world.
Similar demonstrations are emerging in more practical application areas, such as medical imaging diagnostics, where AI analytics are achieving more accurate recognition rates than human experts.
The field of Data Science is a rapidly growing interdisciplinary specialty that is directly relevant to the national
development priorities for Kazakhstan.
The Master of Science in Data Science at Nazarbayev University will provide in-depth education in Data Science, incorporating key concepts from multiple fields. The program includes the following subject areas: Databases, Data Mining, Big Data, Business Analytics, Artificial Intelligence, Information Retrieval, Machine Learning, Deep Learning, Image and Video Processing, Bioinformatics, Cybersecurity, Data Analysis and Visualization, Mathematical and Statistical Modeling, Data Storage and Processing Infrastructures, and Cloud-based Solutions.
Aims and Objectives
The Master of Science in Data Science is a two-year full-time program (120 ECTS credits) at the School of Engineering and Digital Sciences (SEDS) of Nazarbayev University.
The first term of study is designed to provide a foundation in the fundamentals of the field, and to provide a baseline for all incoming admissions streams from several related but distinct fields of undergraduate study.
The program includes discipline core courses, and a set of topical electives that provide the continuity of study across the two years. The discipline courses are augmented by an arc of courses that provide milestones for program advancement and completion of the Master Thesis Project. This arc consists of a course in Research Methods, Research Seminar, Thesis Proposal, and then a final term to conclude and defend the work.
The program is aligned with professional society guidance from the ACM and IEEE disciplinary societies, and incorporates specific topical coverage as requested from Kazakh government and industry partners. The program provides a framework for collaboration with partners from education, government, and industry, so as to align opportunities for targeted research and collaboration on projects related to national development priorities as articulated in a series of national strategy documents such as Digital Kazakhstan, Kazakhstan 2030, the 100 Steps, and Kazakhstan 2050.
The Data Science program is unique in Kazakhstan, due to its interdisciplinary focus and a pedagogical approach which integrates research and emphasizes innovation. Starting from the first semester students interact closely with faculty. Graduates of the program are trained to become data science professionals prepared to enter careers in industry, government, or education.
It is now possible to collect data on the scale of Exabytes (1 billion gigabytes), from an incredibly wide range of digital sources, such as cameras, sensors (thermal, light, motion), smart phones, and medical devices. In addition to the data sources, we have enormous volumes of the routine transactional data of digitalized daily life, data that was previously uncollected or, if collected, ignored.
It is not just the data but the meta-data about the data, including the details of how, when, and where it was collected, that facilitates the identification of patterns, and, conversely, anomalies, in ways that are increasingly useful. This phenomenon of high-volume data collection is motivating the creation of advanced analytic techniques that can achieve results across disciplinary boundaries.
What began as “Big Data” has become “Data Science, enabling significant advances in applications ranging from health care (improved diagnostics and outcomes) to energy consumption (smart homes, factories, and cities). The ubiquity of digital devices is greatly expanding both the quality and quantity of data generated, with an effect amplified by the rapid emergence and utilization of the IPv6 protocols and the resulting plethora of “internet-of-things” applications.
The exponential growth in data sources and volume has presented non-trivial challenges to every step of data management: collection, storage, validation, preservation, transmission, access, analytics, and also raises new challenges to the related issues of anonymity, privacy, and security.
Data Science is the scientific discipline that covers the full range of the data life cycle. It includes both theoretical and practical methods for organizing, processing, and analyzing the data and transforming data into information and, increasingly, information into actionable “intelligence”.
In this regard, “intelligence” is feasible through major innovations in the application areas of Artificial Intelligence, Machine Learning, and Deep Learning, which are benefitting from new models of cognition and learning, and substantial improvements in computing resources and methods to manage computational complexity.
The result is that Data Science is achieving results that were previously conceived only speculatively or in the literature of science fiction. As but a single example, a machine recently taught itself chess over a weekend, and proceeded to defeat the top human players in the world.
Similar demonstrations are emerging in more practical application areas, such as medical imaging diagnostics, where AI analytics are achieving more accurate recognition rates than human experts.
The field of Data Science is a rapidly growing interdisciplinary specialty that is directly relevant to the national
development priorities for Kazakhstan.
The Master of Science in Data Science at Nazarbayev University will provide in-depth education in Data Science, incorporating key concepts from multiple fields. The program includes the following subject areas: Databases, Data Mining, Big Data, Business Analytics, Artificial Intelligence, Information Retrieval, Machine Learning, Deep Learning, Image and Video Processing, Bioinformatics, Cybersecurity, Data Analysis and Visualization, Mathematical and Statistical Modeling, Data Storage and Processing Infrastructures, and Cloud-based Solutions.
Aims and Objectives
The Master of Science in Data Science is a two-year full-time program (120 ECTS credits) at the School of Engineering and Digital Sciences (SEDS) of Nazarbayev University.
The first term of study is designed to provide a foundation in the fundamentals of the field, and to provide a baseline for all incoming admissions streams from several related but distinct fields of undergraduate study.
The program includes discipline core courses, and a set of topical electives that provide the continuity of study across the two years. The discipline courses are augmented by an arc of courses that provide milestones for program advancement and completion of the Master Thesis Project. This arc consists of a course in Research Methods, Research Seminar, Thesis Proposal, and then a final term to conclude and defend the work.
The program is aligned with professional society guidance from the ACM and IEEE disciplinary societies, and incorporates specific topical coverage as requested from Kazakh government and industry partners. The program provides a framework for collaboration with partners from education, government, and industry, so as to align opportunities for targeted research and collaboration on projects related to national development priorities as articulated in a series of national strategy documents such as Digital Kazakhstan, Kazakhstan 2030, the 100 Steps, and Kazakhstan 2050.
The Data Science program is unique in Kazakhstan, due to its interdisciplinary focus and a pedagogical approach which integrates research and emphasizes innovation. Starting from the first semester students interact closely with faculty. Graduates of the program are trained to become data science professionals prepared to enter careers in industry, government, or education.
Total Number of Credits: 120 ECTS
Year 1: Fall Semester (Semester 1)
Year 1: Spring Semester (Semester 2)
Year 2: Fall Semester (Semester 3)
Year 2: Spring Semester (Semester 4)
Program Core Courses
This course is designed to introduction object-based programming and programming concepts with Python, Algorithm analysis and design, basic data structures, and Scientific computation and visualization with Python. Moreover, the course will include the following sub topics; Introduction to Python and its basic data types; Object-oriented programming with Python; Recursion; Algorithm analysis; Searching and sorting; Abstract data types such as linked lists, stacks, queues, priority queues, trees, hashs and their implementation in Python; Vectors and matrices in Python; Plotting and visualization with Python; Solving systems of linear equations with Python.
The fundamental concepts of probability are essential to solve the data science problem. This course will introduce a broad range of probability theory and mathematical statistics that emphasizes the probabilistic foundations required to understand probability models and statistical. The topics covered include fundamentals of probability theory, statistical inference and graph data structure, including probabilistic models, random variables, useful distributions, expectations, the law of large numbers, the central limit theorem, point and confidence interval estimation, hypothesis tests, basic data structure and algorithms for graphs. After taking this course, students will be able to use calculators and tables to perform simple statistical analyses for small samples and use popular statistics packages (e.g., R Studio) to perform simple and sophisticated analyses for large samples.
Introduction to data mining techniques, including data preprocessing, data mining primitives, association rules, decision trees, cluster analysis, classification and machine learning, data visualization, and data warehousing. Applications from a wide variety of domains will be analyzed.
Recent advances in technology have led to orders-of-magnitude increases in the amount of data that is being produced and archived. This phenomenon has been generically referred to as Big Data. Examples of such data include internet traffic and logs, social media, large scale scientific projects, and health records. The process of deriving actionable insights from these large volume and heterogeneous data sets is referred to as Big Data Analytics, and poses new challenges in all aspects of computing. This course will review essential theory as well as statistical and machine learning techniques for data processing and examine their scalability. Students will gain an understanding of current best practices in Big Data processing and complete assignments and projects with a variety of relevant software tools.
This course is a one-semester course intended for graduate students in Data Science and Computer Science graduate programs. This course focuses on the design, analysis and implementation of database management systems. Most of the topics will be covered as case studies using commercial and open-source database components such that the students can learn a wide-range of tools and techniques and also assess the trade-offs. Student will implement their own database management system as the integrated course project. Therefore, strong programming skills are required. The course will cover topics such as relational, document, key/value data models, storage models and architectures, query languages (e.g. SQL), various types of indexing, transaction management and recovery, query processing, distribute/parallel database architectures, and big data and NoSQL database models and systems.
This course introduces students to representative research methodologies such as surveys, interviews, experimentation, and case studies. Students then review the fundamental approaches to research design, data collection, analysis, and presentation.
This course is a one-semester course intended for graduate students in Data Science graduate program. In this course, students will learn the concepts of process and project management, professional practices, software lifecycle, process models, requirements engineering, and design. Students will be taught theoretical concepts, applications, tools, processes, and practical insights to help them understand how to successfully manage projects in a wide variety of industries including software development and information technology. Students will be exposed to study aids, real-time projects, and tools to provide plenty of hands-on practice.
With the rapid advance of information technologies, data is emerging as the world's most important resource. Data-driven innovation offers a huge potential for economic, social and technological progress. In this course, students will learn and discuss concepts such as data-driven research and development, transforming traditional goods and services to data-intensive products, optimizing processes, logistics and delivery using data-driven techniques, and personalized marketing using data analytics, social networking and cloud computing. The course will also discuss how organizational evolution and optimization can be facilitated using data-driven techniques as an accelerator. The students will engage in a semester-long team-based project to create a data-driven innovation project proposal for a governmental or private organization.
The course requires students to select a topic, identify authoritative sources, generate an annotated bibliography, and compose a survey paper representing the current state-of-the-art in the selected topic area, and prepare a presentation based on the survey paper. The course includes a series of research presentations, presented by domain experts, to expose students to current research programs, and facilitate the thesis topic selection by students.
Students are led through the processes required to plan and initiate an independent research project that will eventually lead to a thesis. Early on, students are required to finalize their topic and secure the participation of a thesis advisor. They must then develop a research proposal based on a literature analysis of the chosen topic, defend the proposal, and then begin the execution of the project.
The student will conduct independent work under the direction of a supervisor on a research project in the student's designated area of inquiry. This work will culminate in a thesis document that the student must successfully defend. The project itself should demonstrate student mastery of the some or all of the stages of the Data Science life cycle, such as data acquisition, storage, preparation / cleaning, analytics & data mining, modeling & machine learning, and visualization.
Elective Courses Descriptions
The course starts with a review of basic probability and statistics and proceeds to detailed discussion of linear regression, quality measures, diagnostic tests and plots, and remedial methods. It continues with an extension to multiple linear regression, predictor subset selection, interactions, variable transformations, use of categorical predictors. Other topics include introduction to nonlinear regression and neural networks, logistic regression, Poisson regression and generalized review of basic probability and statistics (probability, random variables, special distributions, point and interval estimation, hypothesis testing, one-sample and two-sample t-tests, chi-square tests).
This course introduces the students to best-practice pedagogical methods and innovations in teaching, under the mentorship of senior faculty. The students will conduct classroom and laboratory observations of prominent instructors, using a variety of teaching and learning styles. Later, they will apply educational, instructional, and assessment methodologies in recitation sections of their respective disciplines under the supervision of an experienced faculty member. The will summarize their observations and experiences in a final report.
This course, which requires no background in business studies, exposes the students to fundamental ideas regarding innovation and entrepreneurship. Topics covered will include: how to identify business opportunities and to acquire customers, how to develop a business model, understand investments, and manage risks.
This course covers the mathematical and technological foundations of communication and data storage. We begin by introducing the idea of entropy as a way to quantify information and proceed to major results in the field such as the source and channel coding theorems. The second part of the course examines topics such as detection and estimation theory (but, in the framework of Information Theory). We also cover other concepts and meanings of information.
The roots of computer science can be traced to modeling things such as trajectories of artillery shells and cryptographic protocols, both of which pushed the development of early computing systems in the early and mid-1940’s. A principal approach to modeling and simulation is abstraction, so that real world systems can be effectively simulated on a machine. In this course, we will examine modeling techniques such as Monte Carlo methods, stochastic processes, queuing theory, and Markov chains. Assessment and evaluation of approaches will also be covered. Important application areas that may be investigated include computer systems and networks, diagnostics, economics and finance, and urban planning.
Financial practices have substantially benefited from the domains of computer science (CS) and artificial intelligence (AI), to the point that most trading in major financial markets are now governed by algorithms. This course aims to provide the CS students with the necessary background and guidance in order to have them practically apply their CS and AI knowledge in financial domains. The course consists of 4 parts: In the first part of the course, all necessary financial background will be provided. In the second part, tangible mechanics of the domain (like the Financial Information Exchange, FIX, protocol, and order book dynamics) will be covered. In the third part, algorithmic side of the financial transactions will be elaborated on. And in the last part, quantitative strategies (like portfolio construction, high-frequency trading, news handling, etc.) which make heavy use of statistics, AI, data mining, time series analysis, and performance evaluations will be covered.
Students will learn a number of important topics in the field of computational biomedicine. They will study various topics within the biomedical domain, including primers of anatomy, physiology as well as the basis of various biomedical imaging techniques. Signal processing tools, such as filter theory, artifact rejection, as well as PCA and CSP will be covered. Additionally, we will cover how uni- as well as multi-variate features can be employed for decoding. Practical computing sessions will be carried out and students will also perform a project, where all research-related steps will be covered.
This course is a one-semester course intended for M.S. students in Computer Science and Data Science graduate programs. It enhances the student’s knowledge in computer vision concepts and approaches such as image formation, camera imaging geometry, feature detection and matching, stereo, image classification, scene understanding, and deep learning. In class, we will develop the intuitions and mathematics of the vision approaches. We will expose students to a number of real-world applications that are important to our daily lives. To further differentiate between theory and practice, we will guide students through a series of research projects such that they will have chance to implement cutting-edge computer vision algorithms. Students will also gain experience through research papers, with an emphasis on proper computer vision practices with appropriate applications.
This course is a one-semester course intended for graduate students in Computer Science and Data Science graduate programs. It introduces the students to the concepts and existing models and techniques of deep learning. The main themes of the course are benefits, properties and challenges of Deep Learning; Introduction to Machine Learning and Optimization; Challenges and Common Approaches; Regularization; Convolutional Neural Networks; Deep Recurrent Networks and Sequence Learning; Generative Adversarial Networks; Autoencoders; Advanced Deep Learning Topics; Applications. The students will learn how to design a deep learning architecture. The course presents several common deep learning techniques and exposes the students in learning how to use deep learning in a very efficient manner. Students will gain experience through assignments and projects, with an emphasis on proper deep learning practices with appropriate applications.
Admission to Nazarbayev University programs is based on the principle of meritocracy, which implies the selection of applicants on the basis of their academic achievements, providing everyone with equal opportunities at admission, and facilitating the selection of the most promising and talented students without the influence of external factors. Thus, the requirements for admission to the programs of Nazarbayev University are the same for all applicants regardless of citizenship, social status, the status of the educational organization, the graduate of which the applicant is, etc. The duration of Masters degree programs in the School of Engineering and Digital Sciences is 2 years.
More details on Regular admissions are available on the NU website.
To learn more about financial aid and scholarship requirements, please visit the following link.
For detailed information on the application process for the specific graduate program you are interested in, please refer to the guide linked below.
Guide for Graduate programs candidates.
More details on Regular admissions are available on the NU website.
To learn more about financial aid and scholarship requirements, please visit the following link.
For detailed information on the application process for the specific graduate program you are interested in, please refer to the guide linked below.
Guide for Graduate programs candidates.