Importance of Data Science for Fresher and Experienced

Data science career is growing more day by day and most importantly it’s not new. It has been going on for several years technically but before that? Yes, it was there. People use to gather data manually, of course, it was not that much advanced like now, however, it is going on from many many thousands of years back. 

Now in this article, I am going to discuss career opportunities in Data Science.

First of all, we need to understand briefly what is Data Science?

Data science is basically a multidisciplinary blend of data inference, algorithm development, and technology in order to solve complex problems analytically.

We can divide this Data Warehouse into 2 segments. Both of them are equivalently valuable.

1.Discovery of Data Insight

Numeric data analysis to help steer strategic business decisions.

2.Development of Data Product

Algorithm solutions in production, operating at scale (e.g. recommendation engines)

Data science – the discovery of data insight

This perspective of data science is all about revealing findings from data. Diving in at a granular level to mine and understand difficult behaviors, trends, and conclusions. It’s about surfacing unknown insight that can help enable organizations to make smarter business decisions. For example:

  • Netflix mines movie viewing data patterns to know what drives user interest, and applies that to make decisions on which Netflix original series to produce.
  • Target identifies what are significant customer segments within its base and the unique purchasing behaviors within those sections, which helps to manage to message to different market audiences.
  • Proctor & Gamble uses time series models to more precisely understand future demand, which supports the plan for production levels more optimally.

How do data scientists mine out insights? It begins with data exploration. When given a challenging question, data scientists are kind of detectives. They examine leads and try to understand the patterns or characteristics within the data. This needs a big dose of analytical creativity.

Then as necessitated, data scientists may apply the quantitative technique to get a level deeper – e.g. presumed models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The purpose is to scientifically piece together a forensic view of what the data is saying.

This data-driven insight is necessary to providing strategic guidance. In this sense, data scientists act as advisers, guiding business stakeholders on how to act on findings.

Data science – development of data product

A “data product” is a technical asset that: (1) uses data as input, and (2) prepares that data to return algorithmically-generated decisions. The perfect example of a data product is a recommendation engine, which ingests user data, and makes personalized suggestions based on that data. Here are some examples of data products:

  • Amazon’s recommendation engines recommend items for you to buy, prepared by their algorithms. Netflix suggests movies to you. Spotify advises music to you.
  • Gmail’s spam filter is a data product – an algorithm behind the displays processes incoming mail and decides if a message is junk or not.
  • Computer vision used for self-driving vehicles is also data product – machine learning algorithms can recognize traffic lights, other cars on the road, walkers, etc.

This is separate from the “data insights” section above, where the consequence to that is to perhaps provide advice to an executive to make a more intelligent business decision. In contrast, a data product is a technical functionality that encapsulates an algorithm and is assigned to integrate directly into core applications. Several examples of applications that incorporate data product behind the scenes: Amazon’s homepage, Gmail’s inbox, and autonomous driving software.

Data scientists play a primary role in developing data products. This involves developing out algorithms, as well as testing, refinement, and technical deployment into production systems. In this sense, data scientists work as technical developers, building assets that can be leveraged at a wide scale

Getting interesting? Yes, it is interesting. Well, this is the discussion in depth so that you can understand. Whether you are a professional or a fresher, if this can steal your interest then I must say you should gain more knowledge in depth. This field is full of opportunities. You should consider this subject to grow more and give your career a boost.

There are few things you should have to be a data scientist-

  1. Mathematics expertise
  2. Technology: Hacking Skills
  3. Business or strategy acumen. 

Today  I am going to finish it off here, will be back soon with different industry and career opportunities. Till then, Good Luck!!