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Program Courses and Requirements

The requirements for successful completion of the MSc Data Science and Analytics are:

  1. Four (4) required foundation courses

  2. Two (2) required elective courses

  3. One (1) seminar course

  4. Major Research Project

1. Foundation Courses

DS 8001 Designs of Algorithms and Programming for Massive Data

To introduce students to the theory and design of algorithms to acquire and process large dimensional data. Advanced data structures, graph algorithms, and algebraic algorithms. Complexity analysis, complexity classes, and NP-completeness, approximation algorithms and parallel algorithms. Study of algorithmic techniques and modeling frameworks that facilitate the analysis of massively large amounts of data. Introduction to information retrieval, streaming algorithms and analysis of web searches and crawls.

DS 8002 Machine Learning

Overview of artificial learning systems. Supervised and unsupervised learning. Statistical models. Decision trees. Clustering. Feature extraction. Artificial neural networks. Reinforcement learning. Applications to pattern recognition Overview of artificial learning systems. Supervised and unsupervised learning. Statistical models. Decision trees. Clustering. Feature extraction. Artificial neural networks. Reinforcement learning. Applications to pattern recognition and data mining.

DS 8003 Management of Big Data and Big Data Tools

The course will discuss data management techniques for storing and analyzing very large amounts of data. The emphasis will be on columnar databases and on Map Reduce as a tool for creating parallel algorithms that can process very large amounts of data. Big Data applications, Columnar stores, distributed databases, Hadoop, Locality Sensitive Hashing (LSH), Dimensionality reduction, Data streams, unstructured data processing, NoSQL, and NewSQL. 

DS 8004 Data Mining and Prescriptive Analysis

The course teaches to use data to recommend optimum course of action to achieve the optimum outcome and to formulate new products and services in a data driven manner. The course will cover all these issues and will illustrate the whole process by examples. Special emphasis will be given to data mining and computational techniques as well as optimization and stochastic optimization techniques. 

2. Electives

Students may take electives from the following:

  • Advanced Methods in Data Visualization (NEW)
  • NLP (text Mining) (NEW)
  • Geospatial Data Analytics (SA8901)
  • Applied Econometrics (EF8903)
  • Empirical Topics in International Finance (EF8913)
  • Financial Econometrics (EF8914)
  • Panel Data and Nonlinear Model Analysis (EF8944)
  • Nonparametric Data Analysis (EF8945)
  • Empirical Topics in International Trade (EF 8933)
  • Topics in Labour Economics (EF 8937)
  • Fundamentals of Social Network Analysis for Data Science (New)
  • Geodemographics (SA8911)
  • Fraud Detection and Privacy (NEW)
  • Engineering Big Data Systems (ME8118)
  • Nonparametric Data Analysis (EF8945)
  • Optimization Models (ME8127)


Academic definitions for existing courses are available in the
Graduate Calendar.



  • Simulation Theory and Methodology (ME8140)
  • Social Media Analytics (NEW)
  • Community Analytics (SA8931)
  • Biostatistics (NEW)
  • Semantic Technologies (NEW)
  • Advanced Imaging (BP8113)
  • Managerial Decision Modeling (MTI8310)
  • Advanced Software Engineering (CP8202)
  • Advanced Database Systems (CP8203)
  • Soft. Computing and Machine Intelligence (CP8206)
  • Distributed Systems (CP8304)
  • Knowledge Discovery (CP8305)
  • Advanced Artificial Intelligence (CP8314)
  • Genetic Programming (CP8311)
  • Ecoinformatics (BLG678)

3. Seminar Course

DS 8005 Soft Skills, Research and Communication

The course will focus on communicating and presenting data analysis results. It aims at building the competency in story telling from the numbers. 

4. Major Research Project