• Premanand S


Updated: Jul 12, 2020

Thanks for the support and motivation for the past two posts, guys! Keep supporting me, encourage me and correct me if i am wrong in any post!

My next topic is on the most trending topic ‘DATA SCIENCE’. We come across many fancy terms like Artificial Intelligence, Machine Learning and Deep Learning, before getting into these terms, first we must understand the term DATA SCIENCE (DS), and what actually DS means? What's the difference between Data Scientist vs Data Engineer vs Data Analyst?

Data Scientist: The Sexiest Job of the 21st Century - Harvard Business Review

Importance of Data + Science

The word DATA which means some useful information or signal (it may be anything like photo, music, video, text, even message or anything useful). There is always data and everywhere data in this internet era world, which means daily we are playing with data by internet, through that we will either browsing of social media like Facebook, Twitter, LinkedIn or even WhatsApp (either uploading or sharing of photos / videos), money transaction through Gpay, PhonePe or through some bank app like SBI, HDFC etc., or by e-commerce websites like Flipkart, Amazon for purchasing, watching movies or dramas through Netflix, Amazon Prime or through sensors we will be collecting some data for academic or for personal use, like these the data can be utilized in many forms. We will be thinking it won't be useful but this plays major role in Data Science in and around the world.


DATA - An Unexplored Art

There are many forms of data we will be using or used in our whole life with or without knowing its importance or character, these data are classified as Big data, Structured, Unstructured and Semi-structured data, Time Stamped data, Machine data, Spatiotemporal data, Open data, Dark data (not DARK series), Real time data, Genomics data, Operational data, High dimensional data, Unverified outdated data and Translytic data. (Is this types necessary if you asked means, Yes! before analyzing any data we need to know about it! For further process)



Data is the foundation of data science; it is the material on which all the analyses are based. The definition for Data science before 25 years ago was, gathering and cleaning data sets then applying statistical methods (mathematical operations) to that data.

But from 2018, data science has grown to a field that involves data analysis (technique of collecting raw data from various sources, analyzing it and transforming it into information that can be used to reach a specific application), data mining (a process used to extract usable data from a larger set of any raw data.), predictive analytics (gathering the historical and current data and then using it for the future, it helps organizations to increase efficiency, save costs and to reduce uncertainty.), business intelligence (a technology driven process that helps business to convert the available data into knowledge that is delivered to the stakeholders to help them analyze and take appropriate decision at the right time), and machine learning (next topic).


Data Science - Layman Explanation

In a layman term, Data Science is any insight we learn from the data. For example, if you study the data from a grocery store, you may find a pattern that customer buys butter if he / she buys a bread. Finding any useful insight from a huge data can be termed as the data science. To be more precise many big shops failed because they are not aware of customers mind setup and how to attract them, added what to purchase, so here in the layman term the shopkeepers are not going to do some mathematics and programming, they are analyzing from customers aspect, in this we can correlate to technical scenario of DS is DATA here implies about what kind of goods needed to buy for the shop, BIG DATA implies about the quantity based on situation / environment / or maybe we call seasonal too, MACHINE LEARNING which implies here not the programming skills but the shopkeepers thinking / analyzing about the customers point of view and the goods, STATISTICAL & PROBABILITY which means not all the time all goods can be purchased for the shop in bulk, it depends on various factors about the customer as well as situation, PROGRAMMING LANGUAGE here which deals with advertisement mode. So any successful business with professional techniques we can term as data science (for understanding).

Application of DATA SCIENCE

The above diagram which explains about the usage and purpose of data science and some more applications are,

  1. Fraud and Risk detection (Bank / Credit cards)

  2. Healthcare (Medical Image / Signal Analysis, Genetics and Genomics, Drug analysis, Virtual assistance of patients / tele communications)

  3. Internet Search (Helps in word search)

  4. Targeted advertisement (Frequent explored search comes in frequently used apps or web links)

  5. Website Recommendations (Amazon, Flipkart)

  6. Advanced Image recognition (Tag name in Facebook while uploading photos)

  7. Speech Recognition (Siri, Cortana)

  8. Airline Route Planning (Flight delay, Bookings)

  9. Gaming (EA sports like FIFA, Cricket)

  10. Augmented Reality (Pokémon Go)

Note: Artificial Intelligence is used in the field of Data Science for its application. Data Science transforms the Data, which can be used for visualization and analysis. Various operations on data are performed using the Data Science algorithms implemented in languages like Python, SQL and R. Key decisions today are taken based on the Data that is processed by Data scientists. Thus, Data science has to play a vital role in any organization.

If you want to start for the job for Data Science follow the suggestion from the scratch from the infographic,

Data Scientist vs Data Engineer vs Data Analyst

Ok w.r.t job aspect, data scientist is not the only job related to Data Science.

Have you ever wondered what differentiates data scientist from a data analyst and a data engineer? What is the differentiating factor that helps them to analyze the data from a different point of view?

The area of study which involves extracting knowledge from data is called as Data Science and people practising in this field are called as Data Scientists.

The process of the extraction of information from a given pool of data is called data analytics.

A Data Engineer is a person who specializes in preparing data for analytical usage.

Some of the skills required for Data Scientist, Data Engineer and Data Analyst

Data Science – Explained



Some courses for Data Science through online,

  1. Data Science Specialization (Coursera)

  2. Python for Data Science and Machine Learning Bootcamp (Udemy)

  3. Applied Data Science with Python Specialization (Coursera)

  4. Introduction to Data Science using Python (Udemy) - Rakesh Gopalakrishnan

  5. Data Science (Harward) http://cs109.github.io/2015/pages/videos.html

  6. The Data Science Course 2020: Complete Data Science Bootcamp (Udemy)

Business Intelligence

Business Intelligence for Dummies

Big Data & Business Intelligence (Udemy)

Business Analytics Fundamentals (edx)

Introduction to the Basic Business Intelligence Concepts (https://www.datapine.com/blog/business-intelligence-concepts-and-bi-basics/)

Statistics and Probability

Statistics and Probability (Khan Academy)


Python for Everybody Specialization (Coursera)

Python & Introduction to Data Science (Udemy)


Machine Learning:

Machine Learning (Coursera)

Machine Learning A-Z™: Hands-On Python & R In Data Science (Udemy)

Learning from data is virtually universally useful. Master it and you will be welcomed anywhere

I hope from the above discussion, brings clarity about some questions like,

  1. Why Data Science?

  2. What are the different classification of Data's?

  3. What is Data Science?

  4. What is the layman understanding of Data Science?

  5. Some trending applications of Data Science?

  6. Difference between Data Scientist, Data Analyst and Data Engineer?

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