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 SFU Announcing Master program in Big Data

SFU Announcing Master program in Big Data
News for English Test VocabularyThe School of Computing Science at Simon Fraser University is offering a NEW Professional Masters program in Big Data. This four-semester, hands-on program will prepare you for an exciting and well-paid career as a data scientist.

This program is intended for students with some previous programming experience who wish to learn about the state-of-the-art in big data analysis.

Applications are NOW OPENApplication deadline is April 1, 2014 for the Fall 2014 Cohort. We will keep admissions open until we fill available slots for the Fall 2014 cohort. Our goal is to notify students of acceptance to the Program by May 1st, 2014.

Why Big Data?

Big Data refers to tremendous amounts of information that is becoming increasingly available due to widespread use of the Internet and computing systems.

Deluge of Data: According to former Google CEO Eric Schmidt: "Every two days now we generate as much data as we did from the dawn of civilization until 2003".

Big Decisions: These massive quantities of data can be used to generate powerful insights in domain ranging from business and entertainment, to politics and healthcare.

Netflix predicts success of House of Cards series using Big Data analysis
  • How Obama campaign used Big Data to rally voters
  • Big Data is transforming healthcare
  • Shortage of Big Data Specialists
According to a 2011 report from McKinsey Global Institute, entitled: Big Data, the Next Frontier for Innovation, Competition and Productivity .

"There will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions."

Program objectives and outcomes

The program curriculum will cover the following areas:
  • Analysis of scalability of algorithms to big data.
  • Data warehouses and online analytical processing.
  • Efficient storage of big data including data streams.
  • Scalable querying and reporting on massive data sets.
  • Scalable and distributed hardware and software architectures.
  • Software as a service. Cloud Computing (e.g. Amazon EC2, Google Compute Engine).
  • Big data programming models: map-reduce, distributed databases, software for implementing streaming and sketching algorithms.
  • Dealing with unstructured data such as images, text or biological sequences.
  • Scalable machine learning methods such as online learning.
  • Data mining: methods for learning descriptive and predictive models from data.
  • Distributed algorithms over very large graphs and matrices.
  • Social media analysis.
  • Visualization methods and interactive data exploration.
  • Students will complete 30 units of graduate work. These units are divided into three main sections: 15 credits of graduate course work; 12 credits of specialized lab work; 3 credits for co-op.
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