This blog will talk
about the how data scientist came into a picture.With a tsunami like amount of
information being generated by firms on a daily basis; it becomes difficult for
them to make sense of it. This is a sneak peak
to the future that is there with “the sexiest job of 21st century”.

Essentially, a
data scientist is someone who gathers and analyzes with the goal of reaching a
conclusion. They do this through many different techniques. They may present
the data in a visual context, which is often called “visualizing the data,”
allowing a user to look for clear patterns that wouldn’t be noticeable if the
information was presented in hard numbers on a spreadsheet.

Essentially, a data scientist is
someone who gathers and analyzes with the goal of reaching a conclusion. They
do this through many different techniques. They may present the data in a
visual context, which is often called “visualizing the data,” allowing a user
to look for clear patterns that wouldn’t be noticeable if the information was
presented in hard numbers on a spreadsheet.

They
often create highly advanced algorithms that are used to determine patterns and
take the data from a jumble of numbers and stats to something that can be
useful for a business or organization. At its core, data science is the
practice of looking for meaning in mass amounts of data.

The world of data science is an
always-changing area, so people working in this field need to constantly update
their skills. They are continually training to stay at the leading edge of information
and technology.

Data science
educational qualification (Best choice):

A
Data Scientist should have a strong mathematical background to be able to test
and run the results from the data. A solid understanding of statistics,
probability, calculus, and linear algebra is required. It helps in analyzing,
applying data analysis techniques and implements it in a better way. 

Data
scientists come from a wide range of educational backgrounds, but the majority
of them will have technical schooling of some kind. Data science degrees
include a wide range of computer-related majors, but it could also include
areas of math and statistics. 

1.  
Engineer

2.  
Mathematicians or any degree in mathematics

3.  
Statisticians or any degree in stat

4.  
Economist

5.  
Actuaries

6.  
MBA

7.  
Commerce Degree

8.  
IIT/IIM/Ph.D

This is what you should follow first in order to become a
Data Scientist:

·        
First, learn the languages used for
programming – To be a data scientist, having hands-on experience on
programming languages such as R and
Python is a must. Python offers plenty of benefits – it is now the Bank’s
tool of choice for crunching financial data. Moreover, Python is easier to use.
It is a powerful tool with many capabilities and gives you the opportunity to
solve data problems end-to-end.

·        
statistics and probability –
if you are pursuing
data science, you need to have basic understanding of statistics. In short, you
need to be able to describe a few basic algorithms. You would need to run algorithms using R libraries. However,
being able to understand the basics of statistical analysis gives your teams a
better approach.

·        
Having knowledge of SQL – SQL,
or Structured Query Language,
is a powerful programming language that adds, deletes, extracts, or operates on
information within a relational database. Most
data scientists use SQL because it opens
the door to data science.

·        
Machine Learning – Machine learning is a set of
algorithms that train on a data set to make predictions or take actions in
order to optimize some systems. To
learn machine learning you need to have knowledge in mathematics. Machine
learning and statistics is a part of data science.

·        
Deep learning – is one of a kind of machine
learning that involves particular kind of mathematical model.

Communication:

Communication plays
a very important role. Because conveying or explaining any problem and ability
to share data analytics to people is an important job for a data scientist.

Presentation or
visualization:

After collecting
data the next exclusive step is to how we extract that data in an attractive
and meaningful manner. The tools that
can be used for presentation are excel, Tableau, Qlik View, etc…
Whatever the data we have, the destination is how we are presenting them.