MSc in Applied Artificial Intelligence & Data Science
The Master of Science (MSc) in Applied Artificial Intelligence & Data Science at Nazarbayev University offers a rigorous and innovative curriculum designed to prepare students for high-demand careers in the fields of artificial intelligence (AI), machine learning (ML), and data science. This graduate program is tailored to provide both theoretical knowledge and practical skills in applying AI and data science techniques to real-world problems. The program is designed for both full-time students and working professionals, offering the flexibility of part-time study options.
The MSc in Applied AI & Data Science program is structured to equip students with in-depth expertise in the rapidly evolving fields of AI and data science. As part of this program, students will gain a solid understanding of the core concepts in machine learning, statistical methods, and data science and analysis. They will also explore specialized areas such as Data Science (DS), Business Intelligence, Artificial Intelligence (AI), Machine Learning (ML), big data analytics, AI applications in healthcare and other industries.
The MSc in Applied AI & Data Science program is structured to equip students with in-depth expertise in the rapidly evolving fields of AI and data science. As part of this program, students will gain a solid understanding of the core concepts in machine learning, statistical methods, and data science and analysis. They will also explore specialized areas such as Data Science (DS), Business Intelligence, Artificial Intelligence (AI), Machine Learning (ML), big data analytics, AI applications in healthcare and other industries.
| Curriculum and Learning Approach The curriculum is comprehensive, combining foundational knowledge with advanced topics in AI and data science. The core courses cover subjects like machine learning algorithms, deep learning, natural language processing, and data visualization. Students will also have the opportunity to engage in practical, hands-on projects where they can apply their skills to real-world datasets, making them job-ready upon graduation. In addition to core courses, the program offers specialized electives, allowing students to tailor their education to their specific interests. Some of the core and technical electives include topics such as: • Machine Learning Theory and Practice • Big data analytics • Data Mining and Decision Support • Probability and Statistics for Data Science • Artificial Intelligence • Business intelligence • Database Management Systems • Process and Project Management • Forecasting Time Series Data • AI for healthcare applications • Natural Language Processing • Deep Learning • Modeling & Simulation • Applied AI Project • Advanced Artificial Intelligence • Ethical AI |
| Stage One - Foundations of Applied AI & Data Science | |||||
|---|---|---|---|---|---|
| Course | School | ECTS Credits | Workload | ||
| Class Hours | Self-study Hours | ||||
| Core | DS 501 Fundamentals of Data Science | SEDS | 6 | 45 | 120 |
| DS 502 Probability and Statistics for Data Science (Mathematical Foundations for AI) | SEDS | 6 | 45 | 120 | |
| DS 507 Database Management Systems | SEDS | 6 | 45 | 120 | |
| SEDS 591 Research Methods | SEDS | 6 | 45 | 120 | |
| SEMESTER SUBTOTAL: | 24 | 180 | 480 | ||
| Stage 2 – Machine Learning & Data Analytics | |||||
|---|---|---|---|---|---|
| Course | School | ECTS Credits | Workload | ||
| Class Hours | Self-study Hours | ||||
| Core | CSCI 597 Machine Learning Theory and Practice | SEDS | 6 | 45 | 120 |
| DS 551 Process and Project Management | SEDS | 6 | 45 | 120 | |
| CSCI 545 Big Data Analytics | SEDS | 6 | 45 | 120 | |
| Technical Elective 1 | NU | 6 | 45 | 120 | |
| SEMESTER SUBTOTAL: | 24 | 180 | 480 | ||
| Stage 3 – Advanced Data Analytics & Visualization | |||||
|---|---|---|---|---|---|
| Course | School | ECTS Credits | Workload | ||
| Class Hours | Self-study Hours | ||||
| Core | DS 504 Data Mining & Decision Support | SEDS | 6 | 45 | 120 |
| DS 541 AI for Business Intelligence | SEDS | 6 | 45 | 120 | |
| DS 552 Data Driven Innovation | SEDS | 6 | 45 | 120 | |
| Elective | Technical Elective 2 | NU | 6 | 45 | 120 |
| SEMESTER SUBTOTAL: | 24 | 180 | 480 | ||
| Stage 4 – Master Degree | |||||
|---|---|---|---|---|---|
| Course | School | ECTS Credits | Workload | ||
| Class Hours | Self-study Hours | ||||
| Core | DS 695 Applied AI Project | SEDS | 18 | 135 | 360 |
| SEMESTER SUBTOTAL: | 18 | 135 | 360 | ||
Course Description
The course serves as a gateway into the MS program in Data Science, designed to complement the other MS DS core courses so as to provide a baseline for students from a wide range of degree programs (e.g. CS, Math, Physics, Robotics, Economics). The course begins with an overview of Data Science as an emerging discipline, and proceeds with the typical characteristics of Big Data
and the phases of the Data Science Project Life Cycle. In parallel, we conduct a comprehensive review and assimilation of programming principles and practice. Each major data science theme is accompanied by topical programming tasks, utilizing authoritative data sets, implemented in the Python programming language and utilizing the relevant libraries. This immersive series of exercises is designed to build confidence and competence in programming, and develop practical
skills for the application of data science principles using real-world data.
and the phases of the Data Science Project Life Cycle. In parallel, we conduct a comprehensive review and assimilation of programming principles and practice. Each major data science theme is accompanied by topical programming tasks, utilizing authoritative data sets, implemented in the Python programming language and utilizing the relevant libraries. This immersive series of exercises is designed to build confidence and competence in programming, and develop practical
skills for the application of data science principles using real-world data.
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.
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 aims to provide the students with a set of principles and skills that will be of paramount importance for conducting their research and disseminating the corresponding results. The course provides an overview of the scientific method and its application, project planning, working in teams, interaction with sources, and assessment of research results. Students are also taught the fundamental principles necessary for writing technical reports, scientific papers, and research proposals, and for preparing and delivering presentations: the focus of this part is on the connection between the employed research methods and the way in which the results are presented, rather than on the writing/presentation style in itself. Finally, students are exposed to a general introduction on moral reasoning, with subsequent focus on ethics in professional practice and research activity. The latter topic will focus on data management and intellectual property rights, and on the description of phenomena such as plagiarism and data fabrication.
Students will examine a variety of machine learning algorithms and techniques, from the concepts and theoretical basis to specific application domains. The course will cover representative examples, from first principles, and implement selected algorithms in the Python language.
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.
Big data is currently a ‘hot topic’ with a vast area of applications. This course will focus on actual methods that are used within research as well as industry and will enable students to identify as well as implement big data techniques to various sources of data.
The course will start with setting the context by exploring the relationships between data mining, artificial intelligence, machine learning, deep learning, and statistics fields. It will then introduce knowledge representation, input preparation (preprocessing), fundamental data mining techniques (like probabilistic modeling, linear modeling, association mining, etc.), minimum description length (MDL) principle, validation techniques, decision trees, association rules, instance-based learning models, data transformations, data projections, Bayesian methods, and ensemble learning techniques (bagging, boosting, stacking, etc.) within the supervised, unsupervised and semi-supervised learning frameworks. Data visualization and warehousing methods along with examples from various application areas will also be visited to illustrate the use of data mining in decision support systems and processes.
The AI for Business Intelligence (BI) course aims to equip students with the knowledge and skills to integrate artificial intelligence (AI) into BI processes. The curriculum covers key AI technologies including machine learning and natural language techniques and their applications in data analysis, predictive modeling, and decision support systems. Students will learn how to leverage AI to enhance data collection, processing, and visualization, enabling more informed and strategic business decisions. The course emphasizes practical applications, providing hands-on experience with BI tools and techniques relevant to contemporary business environments.
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.
Students will work in teams on an AI project with an emphasis on teamwork, achievements, problem solving, reporting, continuous improvement, and regular deliverables. Initially, teams will review related work, and develop a project plan and design.The project will culminate with a full report describing the details, problems, challenges and results. Additionally a final presentation will be made by the group.
Program Elective Courses
This course is designed to provide students with a comprehensive understanding of time series analysis and its applications in forecasting. The curriculum covers essential topics such as stationary processes, autoregressive moving average (ARMA) models, and seasonal adjustments. Students will learn to develop and interpret models to predict future data points based on historical patterns, equipping them with valuable skills applicable in fields like economics, finance, and environmental science. The course emphasizes both theoretical foundations and practical applications, ensuring that participants can effectively analyze and forecast time-dependent data.
Information retrieval (IR) is a discipline concerned with processing, storage, and search of information in large collections of unstructured documents. With development of the internet IR techniques have become a key enabler for accessing the vast amount of online information, with strong social and economic impact. This course is intended for students interested in theoretical foundations of IR and practical experience in building IR systems. During this course, we will discuss a wide variety of IR tasks, approaches, and tools. The course is accompanied by a set of practical assignments that reflect the main steps of the IR pipeline: document processing, indexing, search, and evaluation of results. In addition, we will discuss key research papers in the field of IR in a series of seminars.
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.
Computer networks are the core of modern communication. The activities of modern communications mostly run over Internet Protocol and are computer-controlled. Furthermore, with the usage, and rapid developments of wireless technologies such as cellular networks (including 3G, 4G, 5G, wireless LAN) and infrastructure-less technologies (Ad-Hoc Networks, WSNs), wireless communication systems have also become very important for modern communication. This course covers the distinct concepts and protocols in computer/communication network architectures. Addressing schemes, wireless variants of well-known routing and transport layer protocols, mobile IP and network design issues are considered.
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 covers the latest advances in probabilistic, statistical and network-based computational models for real world problems such as computer vision and decision making. Topical coverage will begin at hidden Markov Models, through Markov networks, up to conditional random fields. Additionally, we will never cover novel advances in artificial neural networks such as convolutional neural networks, deep learning models and recurrent neural networks.
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.
Students will examine a variety of machine learning algorithms and techniques, from the concepts and theoretical basis to specific application domains. The course will cover representative examples, from first principles, and implement selected algorithms in the Python language.
This graduate course is intended for students interested in the theoretical foundations of natural language processing and gaining practical experience in building such systems.During the course, we will discuss a variety of tasks, approaches, and tools, accompanied by a series of practical assignments covering common natural language processing tasks.
| Practical Learning & Industry Connections One of the distinguishing features of this MSc program is its strong focus on the practical application of AI and data science. Students are encouraged to work on industry-relevant projects, applying their skills to solve real-world challenges faced by businesses, governments, and healthcare institutions. Through collaborations with industry partners, students have the opportunity to gain exposure to the latest AI technologies and practices used by leading companies. The MSc in Applied AI & Data Science program benefits from strong links with industry partners, both locally and internationally. These partnerships provide students with access to real-world data, internships, and networking opportunities. Through collaborations with leading companies, students gain valuable experience and insights into the practical application of AI and data science. |
Career Opportunities
Graduates of the MSc in Applied AI & Data Science program will be well-equipped to pursue a variety of roles in AI and data science, including positions such as:
• Data Scientist
• Artificial Intelligence Engineer
• Machine Learning Engineer
• Data Analyst
• AI Developer for Healthcare Solutions
The program is designed to meet the growing demand for highly skilled professionals who can analyze and interpret large datasets, develop AI models, and create data-driven solutions to complex problems. Additionally, the emphasis on privacy preservation and interoperability makes graduates particularly well-suited for roles in sectors where data privacy is critical, such as healthcare and finance.
• Data Scientist
• Artificial Intelligence Engineer
• Machine Learning Engineer
• Data Analyst
• AI Developer for Healthcare Solutions
The program is designed to meet the growing demand for highly skilled professionals who can analyze and interpret large datasets, develop AI models, and create data-driven solutions to complex problems. Additionally, the emphasis on privacy preservation and interoperability makes graduates particularly well-suited for roles in sectors where data privacy is critical, such as healthcare and finance.
Meet the professors
How to apply?
- Education – A bachelor's degree in a relevant field or valuable experience in AI and Data Science.
- English proficiency – Studied in English or hold an IELTS certificate with a score of 6.0+
Tuition Fee
16 000 $
The total program cost for the whole period
Stay Updated & Get Personalized Assistance
For more details on admission requirements, deadlines, and program costs, please contact:
Yenlik Molgozhdarova
Senior Manager for Academic and Student Affairs
email: yenlik.molgozhdarova@nu.edu.kz
phone: +7 7172 70 66 13
Additionally, we offer both online and offline meetings to explore opportunities for customized corporate training programs.
Yenlik Molgozhdarova
Senior Manager for Academic and Student Affairs
email: yenlik.molgozhdarova@nu.edu.kz
phone: +7 7172 70 66 13
Additionally, we offer both online and offline meetings to explore opportunities for customized corporate training programs.
Interested in applying?
Please fill out our the form below.