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Data Science and Analytics (MSc)

Overview

This unique one-year Master of Science (MSc) degree program enables students to develop interdisciplinary skills and gain a deep understanding of technical and applied knowledge in data science and analytics. Graduates are highly trained, qualified data scientists who can pursue careers in industry, government or research.

Degree awarded: MSc

Administered by: Yeates School of Graduate Studies

Data Science and Analytics graduate program website

PDF fileData Science and Analytics graduate program calendar

Curriculum

  Degree Requirements: Master of Science  

 

Major Research Paper (MRP)

(Milestone)

DS8012

Research Skills

Pass/Fail

DS8005

Soft Skills, Communication and Ethics

Pass/Fail

DS8001

Design of Algorithms and Programming for Massive Data

1

DS8002

Machine Learning

1

DS8003

Management of Big Data and Big Data Tools

1

DS8004

Data Mining and Prescriptive Analytics

1

 

Two Elective credits

2

 

Electives
Course code Course name Credits

BP8113

Advanced Imaging

1

CP8202

Advanced Software Engineering

1

CP8203

Advanced Database Systems

1

CP8206

Soft Computing and Machine Intel

1

CP8304

Distributed Systems

1

CP8305

Knowledge Discovery

1

CP8311

Genetic Programming

1

CP8314

Advanced Artificial Intelligence

1

DS8008

NLP (Text Mining)

1

DS8009

Special Topics in Data Science and Analytics

1

DS8010

Interactive Learning in Decision Process

1

DS8011

Bayesian Statistics and Machine Learning

1

DS8013

Deep Learning

1

EF8903

Applied Econometrics

1

EF8913

Empirical Topics in International Finance

1

EF8914

Financial Econometrics

1

EF8933

Empirical Topics Int’l Trade

1

EF8937

Labour Economics

1

EF8944

Panel Data and NL Model Analysis

1

EF8945

Nonparametric Data Analysis

1

ME8118

Info Sys Analysis & Design

1

ME8127

Optimization Models

1

ME8140

Simulation Theory/Methodology

1

MT8310

Special Topics Info Sys Mgmt

1

SA8901

Geospatial Data Analytics

1

SA8911

Geodemographics

1

 

Major Research Project

The student is required to conduct an applied advanced research project. The project will be carried out under the guidance of a supervisor.  On completion of the project, the results are submitted in a technical report format to an examining committee and the student will make an oral presentation of the report to the committee for assessment and grading of the report.  The student is expected to provide evidence of competence in the carrying out of a technical project and present a sound understanding of the material associated with the research project. This is a “Milestone.”  Pass/Fail

DS8001- Design of Algorithms and Programming for Massive Data

NP-completeness, approximation algorithms and parallel algorithms. Study of algorithmic techniques and 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 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.  1 Credit

DS8002 – 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 and data mining.  1 Credit

DS8003 – 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  1 Credit

DS8004 Data Mining and Prescriptive Analytics

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.  Prerequisite:  DS8002  1 Credit

DS8005  Soft Skills, Communication and Ethics

The course will focus on communicating and presenting data analytics and modeling results. It aims at building the competency in story telling from numbers. The course also covers ethical and social impacts of data science, analytics and AI. Prerequisite:  DS8012  Pass/Fail

DS8006  Social Media Analytics

The course will cover fundamental concepts and tools in Social Network Analysis by showing how AI, math, and statistical methods are used to study them. The topics include: weblog analysis, centrality in social networks, influence, sentiment analysis and opinion mining, information cascades, multimedia analysis, reasoning and prediction with social media and modeling behaviour. The lab component of the class will use R or Python to develop and analyze network models. Prerequisite:  DS8002  1 Credit

DS8007  Advanced Data Visualization

Overview of data visualization. Basic visualization design and evaluation principles. Learn to acquire, parse, and analyze large datasets. Techniques for visualizing multivariate, temporal, text-based, geospatial, hierarchical, and network/ graph data using tools such as ggplot2, R, D3, etc. 1 credit

DS8008  NLP (Text Mining)

The course covers important topics in text mining including: basic natural language processing techniques, document representation, text categorization and clustering, document summarization, sentiment analysis, social network and social media analysis, probabilistic topic models and text visualization. Prerequisites: DS8002 and DS8003   1 credit.

DS8009  Special Topics in Data Science and Analytics

This course consists of lectures, seminars and readings covering the latest advances and research in data science and analytics. The course description will be announced prior to scheduling of the course. 1 credit.

DS8010  Interactive Learning in Decision Process

This course focuses on topics related to reinforcement learning. The course will cover making multiple-stage decisions under uncertainty, heuristic search in planning, Markov decision processes, dynamic programming, temporal-difference learning including Q-learning, Monte Carlo reinforcement learning methods, function approximation methods, and the integration of learning and planning. Other topics can be included as well.  Prerequisites:   DS 8002 1 Credit

 

DS8011  Bayesian Statistics and Machine Learning

This course will cover modern machine learning techniques from a Bayesian probabilistic perspective. Bayesian probability allows us to model and reason about all types of uncertainty. The result is a powerful, consistent framework for approaching many problems that arise in machine learning, including parameter estimation, model comparison, and decision making. We will begin with a high-level introduction to Bayesian inference, then proceed to cover more-advanced topics.  Prerequisites:  DS 8002  1 Credit

DS8012  Research Skills

This course will be an introduction to research preparation, experimental design, methods of data collection, exploratory data analysis, and understanding threats to validity of results with aim to prepare student for MRP work. Pass/Fail

DS8013  Deep Learning

The course aims to present the mathematical, statistical and computational challenges of building stable representations for high-dimensional data, such as images, text and data. The topics include:  Convolutional neural networks. Autoencoders, their sparse, denoising variants, and their training. Regularization methods for preventing overfitting. Stacked autoencoders and end-to-end networks. Recurrent and recursive networks. Multimodal approaches. Deep architectures for vision, speech, natural language processing, and reinforcement learning.  Prerequisite:  DS8002.  1 Credit

For course descriptions of non DS courses, go to the Program offering the course. 

  • BP – Biomedical Physics
  • CP – Computer Science 
  • EF – Economics 
  • ME – Mechanical and Industrial Engineering
  • MT – Master of Science in Management 
  • SA – Spatial Analysis