By Tekedia Editors September 4, 2011 1 Comment

You think there is no job; No, there are many openings but for a unique set of people. Companies need them because a new market has been created for their services. They are the data scientists and they are very hard to  find:

 

While the concept of data science has been around for decades, the notion of a data scientist has become an in-demand career leading to a rise of a new generation of data scientists.

Proliferation of sensors, mobile and social trends provide explosive growth of new types of data. Data scientists are creating the tools that can be used to interpret and help translate the streams of information into innovative new products. Social media platforms such as Facebook depend on data science to create innovative, interactive features that encourage users to get interested and stay that way

 

We call these guys WANT (think of Quant or quantitative analyst in Wall Street). From Wikipedia, a quantitative analyst is a person who works in finance using numerical or quantitative techniques. Similar work is done in most other modern industries, but the work is not always called quantitative analysis. In the investment industry, people who perform quantitative analysis are frequently called quants.

 

Quants or WANT are exceptional bright guys that have strong mathematical intelligence, statistical purity and can crunch massive data and help you make seen of seemingly useful piece of bits. They are in hot demand all over the world. In short, in Stanford University, the course, Data Science,  is so hot that students cannot find space for it.

 

Nowadays, thanks largely to all of the newer tools and techniques available for handling ever-larger sets of data, we often start with the data, build models around the data, run the models, and see what happens.(from the rise of data science)

 

These are the courses or areas that will help become one.

  • Learn about numerical analysis

Take the Computational Linear Algebra course (it is sometimes called Applied Linear Algebra or Matrix Computations or Numerical Analysis or Matrix Analysis and it can be either CS or Applied Math course).

  • Learn about optimization

This subject is essentially prerequisite to understanding many Machine Learning and Signal Processing algorithms, besides being important in its own right.

This is a classic topic and “data science” par excellence in my opinion. Some of these methods were used to guide the Apollo mission or detect enemy submarines and are still in active use in many fields.

  • Learn about distributed computing
  • Learn about information retrieval
  • Master algorithms and data structures

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