Machine learning and Data Science are two renowned terms of the IT domain. 
Data Science is a method of extracting useful information and deriving at significant knowledge from a theoretical method. Many data science tools are used for the purpose.
While, Machine Learning comes under a set of data science techniques where computers get trained and learn from the data available. 
Both of these are trendy buzzwords and are often taken interchangeably but there is a significant amount of difference between both.
These two buzzwords, along with artificial intelligence and deep learning, are all fairly perplexing terms, so it's crucial to know how they vary. We will only learn about the differences between Data Science and Machine Learning in this topic, as well as how they relate to one another.

Data Science Machine Learning
It extracts meaningful information from structured and semi-structured data It makes computers to learn through data, without the need to be programmed
They are used to gain insights. They are used to make predictions and classify the result
It contains the entire universe of data. It utilizes data science techniques to learn about the data.
Algorithm statistics and data processing are taken care of. It is focused on algorithm statistics.
Data Science is undoubtedly a broad discipline. Machine learning is like a subset of data science.
Skills required for Data Science:

  • Statistics
  • Data visualization
  • Data mining and cleaning
  • Understanding programming languages, like Python
  • Data Science tools
Skills required for Machine Learning:

  • Statistical modelling
  • Data evaluation
  • Fundamentals of Data Science
  • Data architecture design
  • Techniques of text representation
A lot of time is spent by data scientists in handling, cleaning, and understanding the data. Engineers who work in machine learning spend a lot of time dealing with the complexity that arise during the implementation of algorithms and the mathematical ideas that underpin them.


Where is Machine Learning used in Data Science?


Its use can be best understood by the way of the developing steps of data science.
1. Data Collection: In this step, data is collected in order to answer the problem. We can gather the user's ratings for different products, comments, purchase history, and other information for the recommendation system.

2. Data Processing: In this stage, the raw data collected in the previous step is processed into a format that can be used by the subsequent steps.

3. Data Exploration: This is the process in which we try to analyze the patterns in the data and extract relevant insights from it.

4. Data modeling: It is a process in which machine learning methods are employed. As a result, this step encompasses the entire machine learning process. Import is a part of the machine learning process. Importing data, cleaning data, developing a model, training the model, testing the model, and improving the model's efficiency are all part of the machine learning process.

5. Deployment & Optimization: This is the final step, in which the model is put on a real-world project and its performance is evaluated.

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