MSc Data Science

1 Pillar

Students begin the programme with foundational knowledge of programming and mathematics, including data structures and algorithms, statistics and machine learning. Within the first pillar of the programme their knowledge of mathematics, programming and data analysis will be significantly extended. The programme also offers the opportunity to obtain key soft skills for the professional world including technical project management, writing and presenting.

Topics

• Combinatorics And Graphs
• Object-Oriented Programming (C++)
• Data Structures and Algorithms
• Databases
• Theory of Probability and Statistics
• Practical Unix
• Introduction to Interaction Design
• Discrete Optimization
• Master's Machine Learning
• Python
• Networks
• Java Programming
• Big Data Analysis/Machine Learning - 2
• R
• Convex Optimization
• Leadership and Group Dynamics
• Technical Writing and Presenting
• Сomplexity Theory
• Technical Project Management
• Nonlinear Optimization
• Statistical Data Analysis
• Capstone Project - 1
• Seminars & Workshops - 1

2 Pillar

Within the second pillar of the programme students will primarily focus on learning key applications of the data science as well as advanced methods in mathematics and data analysis. A significant part of the study will be allocated to the completion of the Capstone project. Through completion of the programme, students will learn to conduct data analysis on any scale, develop the software necessary for analysis and present the results in a professional and efficient ways.

Topics

• Parallel and Distributed Computing
• Statistical Data Analysis - 2
• Software Design
• Stochastic and Huge-scale Optimization
• Foundations of Cryptography
• Map Reduce
• Distributed Databases
• Text Mining
• Game Theory
• Neural Networks and Deep Learning
• Social Network Analysis
• Time Series
• Robust Optimization
• Image and Video Analysis - 1
• Information Retrieval
• Auctions
• Statistical Data Analysis - 3
• Information Theory
• Image and Video Analysis - 2
• Machine Translation
• Data Visualization
• Algorithms in Bioinformatics
• Spectral Graph Analysis and Data Science Applications
• Web Graphs
• Capstone Project - 2
• Seminars & Workshops - 2

• Junior Data Scientist: This is a typical starter role for data science BSc graduates. The position requires strong knowledge of mathematics with an emphasis on statistics and machine learning as well as on concepts related to programming and databases. As part of a team, junior data scientists begin gaining first hand experience with industrial data analysis while learning from more senior colleagues.
• Data Scientist: Data scientists combine a mathematical background and practical experience with domain expertise which enables them to conduct advanced data analysis necessary to make predictions that facilitate good business decisions. The job requires an understanding of both mathematics and business objectives. Data scientists need to be technical experts with the competencies necessary to handle large volumes of diverse data as well as possess strong programming skills.
• Senior Data Scientist: A senior data scientist becomes a link between the core of the business management and the entire analysis team. The role requires the ability to communicate effectively, in order to both grasp business goals and to communicate the insights brought to them by the analysts. Senior data scientists also direct relevant programming and application-building projects, as well as the formulation of experiment design and they have a leading role within a data science team.
• Principal Data Scientist: A principal data scientist is a data visionary who finds ways to answer business questions by analysing available data within the enterprise and on the internet. The job often requires the creation of new methods of analysis and working at the intersection between industry and academia.
• Chief Data Officer: A chief data officer is a business leader who handles responsibilities at the intersection of data analysis, information technology and business strategy. The CDO determines the long-term direction for controlling risks and generating value from information governance as well as data management and analysis. Most importantly, the CDO is an agent of change uncovering new levels of efficiency by reporting in-depth insights based on knowledge harvested from data.

Apply now to receive up to 50% off your tuition fee and a discounted application fee.