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
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
“Constant efforts, dedication, devotion and determination are the key to success” This principle I have followed in my career. Setting goals and striving hard to accomplish them has always been my strength in professional as well as personal life. I have always been amazed by computers, code –programs and was keen to understand the underlying meaning, gimmick, structure and pattern of how programs actually work. Learning about simple Foxpro programs, excel sheet formulae, C programs at an early age heightened my interest in computer programs. This childhood interest urged me to do a diploma in information technology and later bachelor’s degree in the same area. Having completed six years course in Information Technology, and two years of experience in IT. I stand by the principal “Stay Hungry” and this has helped me in achieving my goals. Standing by this I would like to explore ahead and pursue MS in MIS. I would like emerge to put to use this knowledge to pursue MS program in MIS.
Companies have transformed technology from a supporting tool into a strategic weapon.”(Davenport, 2006) In business research, technology has become an essential means that many organizations use in their daily operations. According to the article, Analytics is a major technological tool used. It is described as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions."(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 standpoint. He refers to organizations that have been successful in their usage of data and statistical analysis. In addition, he also discusses how data and statistics can be vital in the efforts to improve the operations of businesses.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
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
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
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.
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.
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.
When I searched books about Big Data in an e-book store, there were a lot of books, and honestly, it was not easy to decide which book I had to read. There were already a lot of information and data in the online bookstore. The main reason why I decided to read this book among the a lot of books about Big Data was that I thought there would be much more practical information in this book.
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
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.
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.
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