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.
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. | Features | Data Science | Machine 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. | Scope | It 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 |
|
Algo -> learning -> future trends | |
| 5. | Hardware requirements | Scalable horizontal systems
High SSD High RAM |
GPU Are used | |
| 6. | Complexity | It is complex when need to handle raw data | Complexity occurs during mathematical problems | |
| 7. | Tools used | MATLAB
APACHE SPARK SAAS BIG ML |
IBM WATSON STUDIO
Microsoft azure Scikit Learn Amazon LEX |
|
| 8. | Efficiency | Data science methods are not so efficient | Machine 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.
