Comparison Between Data Science and Machine Learning

Data Science, AI and ML
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The below table describes the basic differences between Data Science and ML:

Data Science Machine Learning
It deals with understanding and finding hidden patterns or useful insights from the data, which helps to take smarter business decisions. It is a subfield of data science that enables the machine to learn from the past data and experiences automatically.
It is used for discovering insights from the data. It is used for making predictions and classifying the result for new data points.
It is a broad term that includes various steps to create a model for a given problem and deploy the model. It is used in the data modeling step of the data science as a complete process.
A data scientist needs to have skills to use big data tools like Hadoop, Hive and Pig, statistics, programming in Python, R, or Scala. Machine Learning Engineer needs to have skills such as computer science fundamentals, programming skills in Python or R, statistics and probability concepts, etc.
It can work with raw, structured, and unstructured data. It mostly requires structured data to work on.
Data scientists spent lots of time in handling the data, cleansing the data, and understanding its patterns. ML engineers spend a lot of time for managing the complexities that occur during the implementation of algorithms and mathematical concepts behind that.
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Data Science Machine Learning
Data Science is the study of methods and systems for extracting information from structured and semi-structured data. Machine learning is a branch of computer science that enables computers to learn without being explicitly programmed.
It requires the complete analytics universe. Combination of data science and machine.
It is that Branch which deals with data. To learn about the data, machines use data science approaches.
Data in Data Science could have come from a machine or a mechanical process. It employs a number of methods, including regression and supervised clustering.
Data Science, as a larger phrase, encompasses not only algorithms and statistics, but also data processing. However, it focuses solely on algorithm statistics.
It is a broad phrase that encompasses a variety of fields. It’s a data science-related topic.
Many data science procedures, such as data collection, data cleaning, data manipulation, and so on. Unsupervised learning, Reinforcement learning, and Supervised learning are the three types of learning.
Netflix, for example, makes use of data science technology. Facebook, for example, employs Machine Learning.
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Machine learning focuses on tools and strategies for developing models that can learn on their own using data, whereas data science investigates data and how to extract meaning from it.

A data scientist is often a researcher who uses their talents to develop a study approach and works with algorithm theory.

A model is created by a machine learning engineer by selecting the most relevant algorithm for a given problem and using data to undertake trials to ensure that the findings are repeatable.

Data Science

  • Goal: Perform operations on a variety of data sources in order to prove or reject a theory.

  • Tools: It entails working with organized and unstructured data using machine learning methods.

  • Scope: It entails data collection, data cleansing, data analysis, and so on.

  • Output: Based on important data, a report is generated.

Machine learning

  • Goal: Create software that can learn on its own by deducing meaning from data.

  • Tools: It entails the use of machine learning algorithms and analytical models.

  • Scope: Learning can be supervised, unsupervised, or semi-supervised.

  • Output: ML Model