How is Cloud Computing for Big Data at Lambton College Toronto? Is it worth doing?

Alfonso Llanes, studied at Florida International University

Answered just now

Cloud Computing, Big Data and Machine learning are no longer the sole domain of data scientists. The skill to apply machine learning to vast amounts of data is greatly increasing in its importance and being widely adopted.

Oracle Big Data Predictions for 2017-18

“We can expect a huge increase in the availability of machine learning capabilities into tools for both business analysts and end users—impacting how both corporations and governments conduct their business. Machine learning will affect user interaction with everything from insurance and domestic energy to healthcare and parking meters.

The Internet of Things is for more than inanimate objects. Everything from providing a higher level of healthcare for patients to enhancing customer experience via mobile applications requires monitoring and acting upon the data that people generate through the devices they interact with. The enterprise must simplify IoT application development and quickly integrate this data with business applications. By blending new data sources with real-time analytics and behavioral inputs, enterprises are developing a new breed of cloud applications capable of adapting and learning on the fly. The impact will be felt not only in the business world, but also in the exponential growth of smart city and smart nation projects across the globe.”

Whether the question is about The Lambton College Cloud Computing for Big Data, Ontario College Graduate Certificate is worth the time or investment becomes purely academic.

Big data is being thought in many colleges and universities worldwide with variance on the curriculum, methodology, equipment labs and the academics or researchers teaching the subjects of enhanced virtualization deployment, cloud computing and Big Data theory.

Harvard University Division of Continuing Education offers an Introduction to the Challenges and Opportunities of Big Data, the Internet of Things, and Cybersecurity. The course work is divided into three parts, each presented by leading MIT experts in their field. “MIT researchers continue to conduct ground­breaking research on topics that are presented ranging from RFID to cloud technologies, from sensors to the World Wide Web.”

Engineering and Physical Sciences Research Council New Castle University Digital Institute (EPSRC)

New Castle University offers an advance degree in its EPSRC Centre for Doctoral Training in Cloud Computing for Big Data. This is an innovative and highly prestigious program offer only to a selected 11 students per year to study for a PhD.

Most industrialized countries are facing a huge skills gap in this area as the demand for big data staffing needs has risen exponentially over the past five years from about 400 advertised vacancies in 2007 to almost 4,000 in 2012. In addition, the demand for big data skills will continue to outperform the demand for standard IT skills, with big data vacancies forecast to increase by around 18% per annum in comparison with 2.5% for IT. Over the next five years this equates to a 92% rise in the demand for big data skills with around 132K new jobs being created in the UK alone in 2013.

Data dependency and the nature of a business activity in today’s world in the context of individual characteristics such as business size, will affect the potential for organizations to benefit from big data, however, organizations don’t have to be big to have big data opportunities. The problems and benefits are as true for many small enterprises as they are for big business which inevitably expands the demand for cloud and big data skills. Furthermore, even when security concerns prevent the use of external “public” clouds for certain types of data, organizations are need to find approaches to their own internal IT resources, using perhaps virtualization to create “private” clouds for data analysis.

Addressing these challenges requires expert specialists who can bridge the gap between design of scalable algorithms, and the underlying theory in the modelling of data analysis

Traditional undergraduate and postgraduate courses produce experts in one or the other of these areas, but not both. Advance degrees are required to produce multi-disciplinary experts in mathematics, statistics and computing science for extracting knowledge from big data. In addition, practical experience in developing this knowledge is a most in order to solve problems across a range of application domains.