What is Data Science ?

Data science is a multidisciplinary blend of data inference, algorithms development and technology in order to solve analytically complex problems. In order to uncover useful intelligence for their organizations, data scientists must master in the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process. Data science is ultimately about using this data in creative ways to generate business value. 

Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics and machine learning.

Data science – discovery of data insight

Discovery of data insight all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, inferences and trends. It is about surfacing hidden insight that can help enable companies to make smarter business decisions. For example:

• Hotstar data mines movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Hotstar original series to produce.
• Flipkart identifies what are major customer segments within it's base and the unique shopping behaviors within those segments, which helps to guide messaging to different market audiences.

How do data scientists mine-out insights? This requires a big dose of analytical creativity and  quantitative technique in order to get a level deeper – e.g. inferential models, time series forecasting, segmentation analysis, synthetic control experiments.In this case, data scientists act as consultants, guiding business stakeholders on how to act on findings.

Data science – development of data product

A "data product" is a technical utility that utilizes data as input, and processes that data to return algorithmically generated results. The classic example of a data product is a recommendation engine or product, which ingests user data, and makes personalized recommendations based on that data. Here are some examples of data products:

• Flipkart's recommendation engines suggest items for you to buy, determined by their algorithms. 
• Mail spam filter is data product, an algorithm behind the scenes processes incoming mail and determines if a message is junk or not.

A data product is technical functionality that encapsulates an algorithm and is designed to integrate directly into core applications/Server. Respective examples of applications that incorporate data product behind the scenes: Flipkart's homepage, Mail inbox, and autonomous driving software. In this case, data scientists serve as technical developers, building assets that can be leveraged at wide scale.

Skills require to become Data Scientist:  

Mathematics Expertise

Mathematical statistics is important, it is not the only type of math utilized. First, there are two branches of mathematical statistics – classical statistics and Bayesian statistics. Many inferential techniques and machine learning algorithms and linear algebra. Overall, it is helpful for data scientists to have breadth and depth in their knowledge of mathematics and statistics.

Technology and Hacking

Hacking mean not breaking computer program, but creativity and ingenuity in using technical skills to build things and find clever solutions to problems. Data scientists utilize technology in order to get enormous data sets and work with complex algorithms and it requires tools far more sophisticated than Excel. 

Strong Business Acumen

Having this business acumen is just as important as having acumen for technology and algorithms. Data science projects and business goals should clearly aligned. Ultimately final value comes from leveraging all of the above to build valuable capabilities and have strong business influence.

In this blog I explain basic terms related with data science. I will try to explain more on this topic in my next blog.