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We’ve all heard that data scientists analyze big data for a living. But what does that mean exactly?

Nowadays, data is to civilization what gold used to be to our forefathers: the promise of a better tomorrow. Data can be used to predict natural disasters, to track the habits of online costumers, and of course, to sell more stuff.

But what exactly needs to happen so this information ends up being useful? Well, big data is the answer. And data scientists analyze big data.

I can almost hear your next question. What is big data then? The term explains the process of storing, classifying, analyzing and sharing massive amounts of information. Anyone who produces and sells anything can use big data. However, the big issue is not how to acquire data, but what to do with it.

Thankfully, this is where data scientists come into the picture.

 

What is a Data Scientist and What Does He Do?

Their training is similar to that of data analysts, but what sets them apart is their strong business insights, plus their ability to communicate findings to business and IT leaders.

According to IBM, “a data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data across an organization’s leadership structure.”

 

Cool! Can I Become a Data Scientist?

Glassdoor recently reported that the average salary for a data scientist is $118,709 versus $64,537 for a programmer. That’s attractive! So… how do you become one?

Experts agree that there are three basic skills that a data scientist needs in order to be successful, however, this of course will depend on what are you working on specifically in your organization, so keep that in mind: 

  • Computer literacy
  • Strong math and statistics knowledge
  • Knowledge of a particular business domain

 

Being a fast learner and having a curious and gritty personality definitely scores you some extra points.

Some of the best programs that specialize on Data Science are in the following universities. Make sure to check their programs and schedule an information appointment if needed:

 

Once you get a degree and start dipping into the professional world it will be necessary to keep updated by taking boot camps and online-based courses to hone specific skills. In this field you will never stop learning!

see the original blog here.

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Samuel Noriega is Marketing Manager at 3blades.io.

 

 

Views: 3050

Tags: Data, Scientist

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Comment by E. Michael Huestis on November 11, 2015 at 3:02pm

I also read this post with a great deal of trepidation, however I put more weight on certain aspects of data science over garden variety analytics than Sione does.  As a practitioner since 1983, I have adopted a view that is in close agreement with many other long time practitioners, but without elevating the discipline to lofty heights.  So, here is what I think.

Definition of Data Science

Put in the most direct terms, data science is the practice of drawing actionable knowledge out of raw data.  That’s it.

How Data Science is Done
Data Scientists distill actionable knowledge from raw data through the application of scientific, business, experiential and many other possible filters and disciplines to correlate data points, vectors and relationships.

What Data Scientists Do

They look for correlations which may indicate trending, relationships and other links to numeric, natural and systemic behavior (including human behavior).  These correlations are called “signals”, which may represent newly-discovered facts or knowledge in a constellation of data sets, and which must be tested for veracity and consistency.  If the findings support it, they probably have a nugget of actionable (or sometimes called operable) knowledge.

This new knowledge is then applied to real issues which are in need of resolution, in whatever industry or field of study is being worked in.

Level-Setting

Notice that I did NOT say anything about: the size of the data sets; the number of the data sets; prediction; problem space; or even complexity.  Before I move on, let me say that contrary to assertions in the original post, data science has nothing to do with “Big Data”.  In turn, “Big Data” doesn’t ‘do’ anything for anybody, and ‘explains’ nothing to anyone.  Apart from being a very powerful marketing myth, big data is important because it is driving advances in languages, architecture, networked appliances and so on which allows the massive parallelization required to parse, reduce and process data of all types without leaving the analytic environment.

Additionally, data science is not new.  Contrary to current marketing hype, it is actually very old,.  In fact, I first learned about data science in 1969 when I read Aristotle’s Posterior Analytics (written about 350 BC).  By the way, it's a good read - I recommend it.

Examples of the Application of Data Science
A business example would be that fraudulent activity might be more discoverable and more easily mitigated because new knowledge about human behavior has been brought to bear (well beyond standard forensic accounting analysis).

A marketing example would be that the release of a new product might be more successful because the branding and target market are more focused, enabled by new knowledge about specific market drivers (developed by the analysis of the demographics of human migration patterns).

If this sounds like the kind of thing you do, then you may actually be a practitioner of Data Science.  How about THAT !!

If you have solid functional experience or knowledge in/of multiple disciplines such as geology, physics, accounting, or any number of other fields of study associated with the problem space in business or any other endeavor, (and you use these disciplines in your analysis) then you might be a better practitioner than someone who has less functional experience.

Likewise, the more broadly you can see connections/relationships in data and be able to associate widely disparate data to a given problem space, the better you may be compared to another practitioner.

You should recognize however, that even though your personal universe of knowledge, expertise and experience may differ in size and shape, you are both focused on doing the same thing – drawing actionable knowledge out of raw data. 

.... And this concludes my prepared remarks.  8-)

Comment by Sione Palu on November 7, 2015 at 11:47am

Quote :  "Their training is similar to that of data analysts, but what sets them apart is their strong business insights, plus their ability to communicate findings to business and IT leaders."

This is why real scientists usually sneer at us data-scientists from this kind of misinformed definition of what data science is & I don't blame them.  Data science is basically/fundamentally the art of analysing data,  full-stop.  Whether that data is from healthcare, NASA satellite images, Google web-links,  CERN's Higgs boson experimental data & so forth. The business part or whatever outcome is an outcome of that primary/fundamental goal of analysis. Scientific organizations such as CERN,  Fermi-Lab, Los Alamos, etc, don't have business insights to target/aim for because their main goal is to discover first & foremost. Commercialization may after that discovery & it may take years to commercialize those discoveries.

Data science is a relatively new term, but analysts have been doing analysis way before data science emerged. Data science therefore is simply analysing data, whether its small data, big data, dark data (as mentioned here on Data science before - I have no clue to what that is), yellow data & what have you. Everything else is simply there or exist to help the process of storing & analysing data.

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