By Rizal Raoul Reyes
FORGET about nursing, law and other non-technology courses. The future is in data engineering, analytics, virtualization and other related disciplines.
Two technology experts recently pointed out that the youth should pursue a career in analytics, virtualization and Big Data because this will be the next big wave around the world.
Philippine Long Distance Telephone Co. Chief Strategy Officer Winston Damarillo recently said the explosion of Big Data will require more scientists to analyze the information and data sourced from different devices.
Although the country is a new comer in the Big Data race, he said Filipinos are competent and savvy enough to maximize information that will be sourced from the billions of devices. Furthermore, he said, Filpinos can excel in Big Data because they have the innate ability to analyze things on a wider perspective. “Big data requires a combination of business and science.”
Hence, the company, according to Spokesman Ramon Isberto, would include Big Data in its program starting next year. “This is our response to the importance of Big Data in engineering education.”
In an e-mail interview, Wells Fargo Vice President of Enterprise Data and Analytics Adam Christensen stressed that there are many opportunities in data analytics and related disciplines for people who are interested in pursuing a career in information communications technology.
“It is pretty amazing. If you look at the trends in the ICT industry, whether it’s social or mobile, cloud, they all depend on data, data analytics and data visualization are safe career bets,” Christensen said.
With the growth of data, as well as the growth in demand of data analytics, he said, the industry has a long runway with all sorts of opportunities present. “This is just the beginning. There are lots of opportunities,” he said.
“If you are from the technology side, you could give special focus on database technology, data modeling and data mining because they are going to be very relevant in the scheme of things. You also have to place importance on Java code as it has an important role in analytics,” he added. For mathematically inclined individuals, Christensen suggested that key focus areas are data visualization, business intelligence tool and open-source technologies.
He pointed out that young people should get serious in analytics because it can provide tremendous opportunities to any aspiring professional in these fields.
Moreover, he said, Internet of Things will also pave the way to gather more data from the machines and gadgets, and, at the same time, encourage more people to explore opportunities in analytics.
1 comment
For the past 70 years SQL dominated search for electronic information. It’s external to data technology, which helps to distill patterns and statistics based on queries, from outside to data, externally. SQL technology emanates from External Relations theory of Analytic Philosophy: students of Moore, Russell and Wittgenstein established IBM and everybody followed their path.
However, there is Internal Relations theory, which is based on Bradley, Poincare and my ideas. In this theory patterns and statistics are found into structured data.
I discovered and patented how to structure any data: Language has its own Internal parsing, indexing and statistics. For instance, there are two sentences:
a) ‘Sam!’
b) ‘A loud ringing of one of the bells was followed by the appearance of a smart chambermaid in the upper sleeping gallery, who, after tapping at one of the doors, and receiving a request from within, called over the balustrades -‘Sam!’.’
Evidently, that the ‘Sam’ has different importance into both sentences, in regard to extra information in both. This distinction is reflected as the phrases, which contain ‘Sam’, weights: the first has 1, the second – 0.08; the greater weight signifies stronger emotional ‘acuteness’.
First you need to parse obtaining phrases from clauses, restoring omitted words, for sentences and paragraphs.
Next, you calculate Internal statistics, weights; where the weight refers to the frequency that a phrase occurs in relation to other phrases.
After that data is indexed by common dictionary, like Webster, and annotated by subtexts.
This is a small sample of the structured data:
speaking – done – once : 333112
speaking – was – both : 333109
place – is – in : 250000
To see the validity of technology – pick up any sentence.
Do you have a pencil?
All IT industry is obsolete – everybody uses SQL only.