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 Intel-
ligence, 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.
Year 1: Spring Semester (Semester 2)
Year 2: Fall Semester (Semester 3)
Year 2: Spring Semester (Semester 4)