MS in Data Science/Analytics in Canada
Data is probably the most omnipresent virtual quantity of the 21st century. With astronomically high quantity of data being generated, every minute, almost as a score card of all the events, and non-events occurring around. But this data is useless unless its vast resource can be tapped for further use. That is easier said than even attempted. When it comes to large quantities of data, traditional database extraction tools provide very limited access to this formidable quantity of Big data. To make the best of the seemingly untappable resource, a new field of data extraction, visualization, management and manipulation has come about – Data Analytics or Data Science.
People who indulge in this data mining
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Though there might be the need to catch up to a more universal curriculum, of sorts, quite a few universities are coming up with MS or Professional Certificates in Data Science/Analytics.
Canada, being one of the top countries for higher education, has now begun their bid to get into the data race. In this article, we will list some of the known universities, across all the Canadian provinces, that offer a formal training in handling big data. The list is in no particular ranking order. It can referred to as the first step towards looking for degree programs for the data chaser. For a more exhaustive picture, you would be well advised to take the research into the pages of individual universities that you might be interested in.
Canadian Universities with graduate degrees in Data
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Expected prior training in languages like Java, Python,C++
5) Carleton University
Specialization in Data Science by participating Masters Programs
Application Fee: $100
Tuition:~$14,000 per year
6) University of Waterloo
MMath Computer Science – Data Science Specialization
Courses listed here
Application Requirements: TOEFL/GRE. Background in Computer Science, Engineering or related degree. Formal training in programming languages, data structures, operating systems, algorithms, computer architecture, calculus, linear algebra, probability and statistics.
MMath Statistics – Data Science Specialization
Courses listed here
Application Requirements: TOEFL/GRE. Bachelors in Statistics, Mathematics or related degree. Training in calculus, linear algebra and computer science.
Tuition: ~$6,400 per term
7) St. Thomas University
MS in Data Science
Application Requirements: TOEFL (MELAB, IELTS). Any Undergraduate
Tuition details here
8) Saint Mary's University
M.Sc. In Computing & Data Analytics
Courses listed
Manyika, J. (2011, May 1). Big data: The next frontier for innovation, competition, and productivity. McKinsey & Company. Retrieved May 13, 2014, from http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
Nearly 700 students are enrolled in the Informatics degree program at IUPUI with 540 at the undergraduate level and 157 at the graduate level. Of the nearly 700 students, over half (365) are enrolled in the Media Arts and Science program, with 340 at the undergraduate level and 25 at the graduate level. Almost 70% of the students enrolled in the Media Arts and Science Program are full time students, with 35% of those students in the Media Arts and Science Program at 25 years of age or older. Of the 340 undergraduate students in the Media Arts and Science program at IUPUI, 93 are part time students, 71 are a minority race and 113 are 25 years of age or older.
From May, I signed up for online courses, completed introductory R, Big data analysis at Lynda and continuing Python and Systematic programming courses at Coursera and
Davenport, Thomas H., Paul Barth, and Randy Bean. "How Big Data Is Different." MIT Sloan Management Review. N.p., 30 July 2012. Web. 18 Mar. 2014. .
During preparation, I realized that technology and data had the power to cross the line which separates us from our virtual reality. This motivated me to explore more about the field of data and I took a six-month internship at Bharti Airtel, an Indian multinational telecommunication services company, where I had my first formal exposure with the field of data analytics. The broad objective of the project at Airtel was to analyze the vast data of call records using Oracle and come up with the factors influencing call drops in a particular
I am currently enrolled in the Business Administration with an Emphasis in Business Intelligence program for the College of Business at GCU. I chose this program as I have work experience with both business administration and intelligence and would like to sharpen my skills while earning a degree to accommodate my knowledge. The course topics touch on management, database structure, accounting, finance, business analytics, and marketing. The program includes courses such as Introduction to Management, Business Programming, Web Analytics and Introduction to Computer Technology.
According to Gartner, “Big data is high-volume, high-velocity and high-variety information assets that demand cost effective, innovative forms of information processing for enhanced insight and decision
Science: The Key to Moving Canada Forward Imagine a world where a common cold means imminent death. Food is scarce and electricity didn’t exist. This would be the world without science.
The fascination about information management, the seminar on ‘Hadoop vs RDBMS’ as well as the exposure to data-ware housing made me realize the need of a concrete base in MIS. My long term goal is to conduct research in the field of Information Systems and I look forward to develop my career in the field of MIS and a graduate degree at University of ______, _______will be the right step in that direction.
For the past couple of decades the majority of businesses have wanted to construct a data-driven organization or company. Furthermore, companies around the world are considering harnessing data as a basis of competitive advantage over other companies. As a result, business intelligence and data science use are popular in many organizations today. The increase in adoption of these data systems is in response to the heavy rise in communications abilities the world over. Which, in turn ,has increased the need for data products. Indeed, the Data Scientist profession is emerging to be one of the better-paying professions due to the urgent need of their labor. This paper is going to discuss what business intelligence is all about and explain data science that is usually confused to be similar to business intelligence. I will tackle a brief overview of data scientists and their role in organizations.
My ultimate goal in my career is to make a commendable contribution to the computing world either by starting an innovative venture or doing research which would change the dimensions of future computing and the way we perceive the computers. Now I have determined that a dedicated higher study on Computer Science will enable me to aggregate all my previous knowledge & experience on the field and encourage in achieving my career goal. I am looking at Graduate studies to provide me with the required expertise to carry out higher studies.
Big data originated with web search companies that encountered problems with querying large amounts of both structured and unstructured data. With regard to its background, “big data came into being when web search companies developed ways to perform distributed computing on large data sets on computer clusters” Floyer (2014: 1). Big data then spread to enterprises due to their adoption of developing, processing and dissemination of data.
"(Davenport, 2006) Data is compiled to enhance business practices. When samples are taken, they are used to examine research and understand how to solve problems or why situations are as they are. Furthermore, in this article, Thomas Davenport discusses analytics from a business
Data visualization software also plays an important role in big data and advanced analytics projects. As businesses accumulated massive troves of data during the early years of the big data trend, they needed a way to quickly and easily get an overview of their data. Visualization tools were the solution. When a data scientist is writing advanced predictive analytics or machine learning algorithms, it is necessary to visualize the outputs and monitor results to ensure that models are performing as intended. This is because visualizations of complex algorithms are generally easier to interpret than numerical
I got admitted for Bachelor program in Electronics and Communication Engineering at reputed Parul Institute of Engineering & Technology, Vadodara, Gujarat, India and have cumulative GPA of 7.94. I have been deeply interested by subjects such as VLSI technology & design, Microcontroller and interfacing, Advanced Microprocessors, Wireless communication and Data Communication and networking.