The differences between the job roles of a data scientist and a decision scientist is subtle. While a data scientist is only involved with finding meaning in the chaos of big data, a decision scientist looks at big data with a view to solve a business problem. Decision scientists are nurtured and valued in the … Continue reading What is the difference between a Data Scientist and a Decision Scientist?
This needs a considerable amount of thought. Presently almost all programs being offered by universities are post graduate (Master’s) level or certificate courses which presume prerequisites such as fundamentals of computer science, network engineering, programming, and mathematics. ‘Data Science’ is generally considered to be a combination of the following disciplines: 1. Computer Science 2. Statistics … Continue reading How can I become a data scientist?
Yael Garten, Director of Data Science at LinkedIn, shared the following list of behaviors and qualities that distinguishes exceptional data scientists from the average performers. 1. Request context and envision the answer before you start the work. Average data scientists do what they’re asked and begin building prematurely when often the real question to answer or data product to build is still not understood. … Continue reading Great Advice for Data Scientists by Director of Data Science at LinkedIn
This is an excerpt from the article published by Vincent Granville which can be found at http://www.datasciencecentral.com/profiles/blogs/six-categories-of-data-scientists We are now at 9 categories after a few updates. Just like there are a few categories of statisticians (biostatisticians, statisticians, econometricians, operations research specialists, actuaries) or business analysts (marketing-oriented, product-oriented, finance-oriented, etc.) we have different categories of data … Continue reading 9 Types of Data Scientists
Does Big Data mean Hadoop? Not really, however when one thinks of the term Big Data, the first thing that comes to mind is Hadoop along with heaps of unstructured data. An exceptional lure for data scientists having the opportunity to work with large amounts data to train their models and businesses getting knowledge previously … Continue reading Why not so Hadoop?
I was recently having an informal discussion with a colleague at work regarding predictive analytics and the kinds possibilities moving ahead. It was quite interesting as the discussion started moving towards the statistics as a science side of things. He said that it’s not really forecasting when you can never tell something a 100%. … Continue reading The fit for ‘Data Science’
My R journey started a few years back. Before R programming, I didn’t really enjoy creating software. I mean I had to do a lot of C++ during engineering at uni, besides learning many other languages, but it never really aroused a sense of fondness. I’d never create applications unless an assignment or later … Continue reading The fuss about R