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Difference between Data Science and Machine Learning

Last Updated on Jul 31, 2023

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Understanding Data Science
Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It involves the use of complex machine learning algorithms to build predictive models.
Industries such as E-commerce, Streaming Services, and Healthcare heavily rely on data science.

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Exploring Machine Learning
Machine learning, a subset of artificial intelligence, enables machines to learn from past experiences and make decisions based on that knowledge.

Comparing Data Science and Machine Learning

S.No. Data Science Machine Learning

1. Data science enables businesses to extract valuable insights from data to make strategic decisions. Machine learning, a subset of data science, enables machines to learn from past data and improve their performance.

2. Data science is used to extract insights from data. Machine learning is used to make predictions or decisions without being explicitly programmed.

3. Data scientists spend a significant amount of time managing, understanding, and cleaning data. Machine learning engineers focus on handling the complexities that arise during the implementation of algorithms.

4. Data Science is a field that deals with data extraction, cleaning, and interpretation. Machine learning leverages data science methods to make machines learn and adapt from data.

5. Data science involves data processing, along with algorithms and statistics. Machine learning primarily focuses on algorithms and statistics.

6. Examples of companies using data science include Zomato, Hotstar, etc. An example of machine learning in action is Google's search engine.

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Difference between Machine Learning and Deep Learning .

Frequently Asked Questions

What is Data Science?

Data science is a popular discipline that deals with the extensive volume of data using contemporary tools and strategies to discover meaningful data and information and make business decisions. Data science utilises complex machine learning algorithms to create models.

What is Machine Learning?

Machine learning is a well-known branch of artificial intelligence, which facilitates machines to understand and learn from happenings and knowledge.

What are the differences between Data Science and Machine Learning?

Data Science deals with learning and understanding insights from data and is mainly used to find insights. Machine Learning is a subfield of data science that allows machines to understand and learn from previous incidents automatically and is used to categorise results for new data points.

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S.No.	Data Science	Machine Learning
1.	Data science enables businesses to extract valuable insights from data to make strategic decisions.	Machine learning, a subset of data science, enables machines to learn from past data and improve their performance.
2.	Data science is used to extract insights from data.	Machine learning is used to make predictions or decisions without being explicitly programmed.
3.	Data scientists spend a significant amount of time managing, understanding, and cleaning data.	Machine learning engineers focus on handling the complexities that arise during the implementation of algorithms.
4.	Data Science is a field that deals with data extraction, cleaning, and interpretation.	Machine learning leverages data science methods to make machines learn and adapt from data.
5.	Data science involves data processing, along with algorithms and statistics.	Machine learning primarily focuses on algorithms and statistics.
6.	Examples of companies using data science include Zomato, Hotstar, etc.	An example of machine learning in action is Google's search engine.