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Big Data Analytics M.Sc.: Applied Modelling and Quantitative Methods

Part of the prestigious School of Graduate Studies at Trent University, the Big Data Analytics M.Sc. is a new stream in the program of Applied Modelling & Quantitative Methods.

The Big Data Analytics Stream is a Master’s of Science degree focusing on the rapidly growing field of data science. A professional program that can be completed in as little as 12 months, this exciting new stream prepares graduates with the tools and techniques they need to work with, and understand big data.

The field of data science is an evolving one, requiring skills from application domains, mathematics and computer science. Studies in this program focus on:

· Foundations of Modelling

· Mathematical aspects of modelling

· Computational aspects of modelling

· Statistical Aspects of Modelling

· Data Visualization

· High performance computing

· Big Data

· Data Mining

· Introduction to Databases

Career-Ready in the Growing Field of Data Science

Trent is one of the first universities in Canada to work to fill the training gap for professionals in data science. Graduates of this program are uniquely prepared with the tools and techniques they require to work with and analyze today’s increasingly complex data sets in all areas of the sciences, as well as the business world. The program emphasizes practical skills in visualization, data mining, cloud-based approaches and parallel programming skills and prepares graduates for careers including:

· Chief Data Officer

· Data Scientist

· Data Solutions Architect

· Business Manager

· Business Analyst

Trent Graduate Studies Learning Environment

Classes take place on Trent’s architecturally-renowned Peterborough Campus with access to leading-edge resources. State-of-the-art learning facilities in intimate class settings are enhanced by sweeping views of the Otonabee River. The new “library of the future” in the Bata Research and Innovation Centre and the Trent University Research and Innovation Park currently under development promise opportunities to connect with entrepreneurial initiatives in a nexus for academic and business connections.

Students will interact with their peers in this stream and others, and each will be assigned to a faculty supervisor.

Courses

Big Data courses are divided into modules of learning that provide a broad base of knowledge with in-depth analysis and skills development.

AMOD 5210H: Foundations of Modelling
Modelling process and basic types of models
Models from the natural and social sciences
Floating-point Representation and Model Validation

AMOD 5220H: Mathematical aspects of modelling
Methods of modelling
Analytical modelling and computer simulation
Forecasting

AMOD 5230H: Computational aspects of modelling
Principles in system modelling and simulations
Discrete-event system simulations
Simulation design

AMOD 5240H: Statistical Aspects of Modelling
Statistical approaches to modelling
Applications of statistics
Univariate and multivariate procedures

AMOD 5250H: Data Analytics with R
R programming language
Statistical analysis
Visualization techniques

AMOD 5430H: Data Visualization
Tools and techniques for data visualization
Univariate and Multivariate visualizations
Spatial, graph, and time series data

AMOD 5420H: High performance computing
Advanced computer architectures
Programming for shared and distributed memory machines
Performance evaluation and parallel algorithms

AMOD 5410H: Big Data
Challenges and opportunities
Cloud-based approaches
Software tools

AMOD 5440H: Data Mining
Data Types and preprocessing
Classification techniques
Clustering approaches

AMOD 5450H: Introduction to Databases
Relational Model and Relational Algebra
Entity-relationship diagrams
Introduction to SQL

Degree Requirements for the Big Data Analytics Stream

· Three courses from the following selection: Foundations of Modelling, Statistical Aspects of Modelling, Mathematical Aspects of Modelling, Computational Aspects of Modelling, Introduction to Data Analytics with R

· Four courses from the following selection: Data Visualization, High Performance Computing, Data Mining, Introduction to Databases, Big Data

· Completion of a research paper

· Seminar presentation

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