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The world of Data Science

by anshuman@thinkcerti.com
data science

These days, Big Data, Data Science, Artificial Intelligence, Machine Learning are the words read and heard every where. Every organization on this planet wants to leverage Data Science to grow their business. And so, it is no surprise that Data Science is considered to be the coolest job of this century.

As per the LinkedIn’s 2018 Emerging Jobs Report, out of 15 top jobs, 6 are related to AI in some form.

So why is everybody interested in Data Science?

There are 2 primary reasons

  1. The availability of Data
  2. The capability to process Data

To prove the point about the availability of data, have a look a the following specific stats. (Look at the Forbes article for more such exciting stats. Link is given at the end of this post.)

  1. 90% of world’s data has been generated in the last 2 years.
  2. There are more than 3.7 billion people using Internet.
  3. 1.5 billion people are using Facebook daily.

In short, we are revealing our selves to the world more and more through various ways and means. And the Businesses are interested in knowing us as much as possible. So as to provide us

  1. Personalized products and services
  2. And thus keep their Business thriving and growing.

And so, there is a high demand for people who have the skills to process the data and derive intelligence from it.

Thanks to the cocktail of

  1. Internet
  2. Cloud computing and
  3. Computing power of our devices

It is economically viable to process the huge data that is being generated.

Enter the world of Data Science

With this background, let us put the various buzzwords related to Data Science together. The aim of this post is to help you

  1. Understand the significance and purpose of the various technologies
  2. To make an informed decision about your Data Science learning goals
  3. Make a Career in field of Data Science

This post is a primer and so the certain words has been used interchangeably to make it an easy read.

Let us demystify the top 4 concepts in Data Science

  • What is Data Science

The discipline of processing data to generate insights can be called as Data Science. Big Data, Machine learning are part of this.

  • What is Big Data?

1.5 billion people are Facebook are uploading pictures, posting status updates, reacting to their friends updates. The data generated is huge and is unstructured. Hence, the conventional methods of managing and mining data are not useful. This kind of data is generally called as Big Data. To process Big Data, specialized tools and techniques are developed. Hadoop is one such technology. NoSQL Databases like MongoDB are becoming vital to handle Big Data.

  • What is Machine Learning?

The act of deriving business insight from Big Data is like finding a needle in Haystack. We need to use some kind of Artificial Intelligence to process the data, simply because of its size. This is where Machine Learning and Artificial Intelligence come handy. The typical job of a Data Scientist is to normalize the data, filter the data, Use various algorithms to build models to mimic real world scenarios and so on…

If you have to tag the data before feeding it to the algorithms, it is called as Supervised learning.

If the algorithm can understand data on its own, its called Unsupervised learning.

Python is one of the most widely used programming language for Data Science.

  • What is Neural networks?

The journey of processing data till the desired outcome is achieved often takes multiple stages. The various stages are connected to each other and behave like the neurons in our brain. The mechanism of connecting intelligence software modules to each other to get the desired outcome is called as Neural networks.

Looking to build a Career in Data Science, checkout these courses that can help you become a Data Scientist

  • External References



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1 comment

Top 5 courses for Data Science - Learn from Home - Thinkcerti Blog March 23, 2020 - 3:20 pm

[…] Data Science is here and its going to get into all the aspects of our lives, if we it has not already. […]


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