DATA SCIENCE VS MACHINE LEARNING

The demand of the technology has taken its new wings Today’s the era of data science, machine learning and artificial intelligence, being interrelated every term has different functionalities. Data science and machine learning are equally important, without machine learning data science cannot execute so well and without data science machine learning algorithms are irrelevant.

What is Data Science?

The inter-disciplinary fields that deployed several methods like scientific methods and algorithms to find out knowledge from data which can be structured and un-structured is called Data Science.

What is Machine Learning?

Whereas the technical revision of algorithms and statistical models that computer systems utilise to execute a detailed task without using clear guidelines is called Machine Learning.

There are huge career paths after opting career either in machine learning or in data science. You can earn handsome amount of money by developing your career as data scientist, software engineer, machine learning analyst, business developer engineer and many more options. Salary packages in this domain are really good. Average annual income of a data scientist ranges between $95000 to $185000 approximately whereas the salary of machine learning engineer varies from $114120 to $146084 approximately and it depends on the expertise, company and region.

data science vs machine learning
data science vs machine learning

There are number of companies in India that are working so efficiently in these areas. Some of the companies that offer machine learning and data science are Talentica software Pvt. Ltd. This is Pune based company. Many other companies come under this list are Space-o technologies, softweb solutions, ThirdEye data, Cartesian consulting, GOJEK etc.

Following are some features that differentiate between data science and machine learning.

Data Science Vs Machine Learning:

S.No.FeaturesData ScienceMachine Learning
1.Definition

Data Science is the process of using multiple scientific algorithms to find out knowledge from hidden data.

Machine learning is the branch of AI which mainly focuses on modeling particular task  following some set of visible patterns

2.ScopeIt has wider scope

It has limited scope as it comes during modeling stage of data science

3.Skills

It includes various skills like R and Python, PIG/HIVE and data wrangling

It includes skills like mathematics, fundamentals of computers, probability, stats, data modeling and evaluation

4.Process
Collect -> clean-> prepare -> analyse
Algo -> learning -> future trends
5.Hardware requirementsScalable horizontal systems

High SSD

High RAM

GPU Are used
6.ComplexityIt is complex when need to handle raw dataComplexity occurs during mathematical problems
7.Tools usedMATLAB

APACHE SPARK

SAAS

BIG ML

IBM WATSON STUDIO

Microsoft azure

Scikit Learn

Amazon LEX

8.EfficiencyData science  methods are not so efficientMachine learning methods are more efficient
9.SQL Knowledge

It is necessary for executing various operations

It is not needed as programs are execute with the aid of Python and R.

10.Performance

Performance is not stable it changes time to time

Performance is stable as algorithm are used to depict trained results.

The motive of the differentiation between data science and machine learning is to explore multiple career opportunities in both of the domains. So, if you’ve been searching difference between data science and machine learning  you don’t find much difference because the origin of both is Artificial Intelligence. In this article we have tried to present relevant points of differentiation. Both have tremendous scope in the future. Both the terms are interrelated.

LEAVE A REPLY

Please enter your comment!
Please enter your name here

This site uses Akismet to reduce spam. Learn how your comment data is processed.