Difference between Data Science and Machine Learning

Data Science: It is the complex study of the large amounts of data in a company or organizations repository. This study includes where the data has originated from, the actual study of its content matter, and how this data can be useful for the growth of the company in the future.
The data related to an organization is always in two forms: Structured or unstructured. When we study this data, we get valuable information about business or market patterns which helps the business have an edge over the other competitors since they’ve increased their effectiveness by recognizing patterns in the data set.
Data scientists are specialists who excel in converting raw data into critical business matters. These scientists are skilled in algorithmic coding along with concepts like data mining, machine learning, and statistics.

Data science is used extensively by companies like Amazon, Netflix, the healthcare sector, in the fraud detection sector, internet search, airlines, etc.

Machine Learning:
Machine Learning is a field of study that gives computers the capability to learn without being explicitly programmed. Machine learning is applied using Algorithms to process the data and get trained for delivering future predictions without human intervention. The inputs for Machine Learning is the set of instructions or data or observations.

Machine Learning is used extensively by companies like Facebook, Google, etc.

Below is a table of differences between Data Science and Machine Learning:

S.No Data Science Machine Learning
1. Data Science is a field about processes and system to extract data from structured and semi-structured data. Machine Learning is a field of study that gives computers the capability to learn without being explicitly programmed.
2. Need the entire analytics universe. Combination of Machine and Data Science.
3. Branch that deals with data. Machines utilize data science techniques to learn about the data.
4. Data in Data Science maybe or maybe not evolved from a machine or mechanical process. It uses various techniques like regression and supervised clustering.
5. Data Science as a broader term not only focuses on algorithms statistics but also takes care of the data processing. But it is only focused on algorithms statistics.
6. It is a broad term for multiple disciplines. It fits within data science.
7. Many operation of data science that is, data gathering, data cleaning, data manipulation, etc. It is three types: Unsupervised learning, Reinforcement learning, Supervised learning.
8. Example: Facebook uses Machine Learning technology. Example: Netflix uses Data Science technology.

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