Are you an Individual / Employed professional who looks upon Data Science as an Ocean with fear of taking a dip in? Herewith, in this short write, I have made an attempt to shallow the floor of the ocean for clear visibility and confidence.
Let me welcome you to the emerging Industrial Revolution 4.0 which is foreseen as the era of Data Science and Artificial Intelligence which will steer the entire global transition irrespective of the industry it touches from Household to Outer Space. Let’s now look a little deeper into Data Science alone.
Hope you would agree with me that all of us would have asked “What’s for Breakfast / Lunch / Dinner?” another at home during these Quarantine days. As soon as the question is raised the counter queries that come to us shall be “What would you like to have?” to the members for choices and “What’s in Store with us?” exploring the resources available for planning a suitable recipe.
Now if we look at an Organisation or any business entity it shall work on similar grounds with differing in functionality and solving real-world requirements about their situations. The amount of information in the form of DATA involved in this process is enormous thereby requiring additional support for a smooth decision-making process which requires involving SCIENCE to process and analyse these data. Hence, DATA SCIENCE is defined as Scientifically Analysing and Processing Available and Sourced Data to arrive at the desired outcome.
Data Science is a more than 3-4 decade-old concept that has evolved with the ever-changing world with extensive usage of the Internet and information Data that is shared through it. It is now been widely used and is predicted to see huge demand with High pay packages across the globe for the Data Scientist or person who can effectively and efficiently use data science for the purpose of effective engagement and outcome.
Data Science as a concept involves 4 Stages broadly may vary depending on the depth of usage, involvement and categorisation :
Problem Identification: The data Scientist identifies and defines a problem or a desired outcome along with a couple of Possibilities and Probable requirements shared by the Organisation.
e.g. > Say Increase in Sales, New Product launch, R & D Requirement, Advertisement Reach, Usage of Application, Spending pattern etc…
Data Preparation: The data scientist then sources the required Direct and Indirect data from various resources internally and via external resource pool sources. These accumulated data may be Structured or Unstructured for ease of process and Understanding. Hence, it is required to be processed as it is derived from a Huge Versatile ocean of data termed BIG DATA.
e.g. > Say Client Needs, Basic Buying Pattern, Competitors’ Product USP, Peak Sales or Revenue, consumption Pattern, Loan requirement, New Product expectation etc…
Analysis and Tools: The data once gathered are being churned with the usage of Statistical and Analytical Tools such as Python, R, Tableau, Hadoop, Machine Learning, Data Mining, Apache Spark, Qlikview and many more depending on the usage and availability.
e.g. > Say Info collected in Excel, Image, PDF, E-mail, Videos, Infographics, Charts, etc...
Outcome / Presentation: Once these data are Analysed and Processed with the help of Algorithms and Machine Learning tools and are made available as a meaningful data set the process of decision-making is been made more Optimised, Precise, Scalable and Effective.
e.g. > Launch of New Scheme, Promotional Offer for Festive Season, Interest Rate for FD/Loans/Investment instruments, New Showroom Location, Branding Timeline for Advertisement etc…
Individuals from the Non-Technical field are also using Data Science with the help of various tools that help them to Identify, Gather, Analyse and make successful decisions.
Now that you know the ways and means of Data Science.
Am I a Data Scientist? Yes, You can be one among them.
Digital Bud 
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