difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?

Question

Can someone tell me the difference between Data Analysis, Data Mining, Data Analytics, Data Science, Machine learning and big data.

These terms always confuse me, I just want a rough Idea about how they differ from each other.

Answer 1

I will brief you on it:

• Data Analytics- A technique of Data Analysis
• Data mining- Used to deal with a large amount of data
• Data Science- It is used for statistical Analysis, Probability models and complex math, it includes Algorithms and Data
• Machine Learning- It allows machine to learn data from a Database.
• Big Data- It deals with a very large complex data  easily.
• Data Analysis- Used to analyse large data.

Answer 2

Data Analytics: It is the application of a mechanical or algorithmic process in order to derive insights. In other words, it performs runs through various data set to find meaningful correlations.

Data Analysis: It is a heuristic activity where the analyst scans through all data to gain some insights.

Data Mining: It involves bringing all the data together to discover unknown trends and correlations.

Data Science: It is a multi-disciplinary field which uses scientific process and algorithms to extract useful knowledge and insights from both structured and unstructured data.

Machine learning: It is the ability of a system to automatically learn and improve or modify based on past experiences without being explicitly programmed.

Big Data: They are large data sets which are analyzed to reveal patterns, trends, and associations which are specially related to human behavior an interactions.

Hope this helps!

Answer 3

• Data Analytics: Data Analytics often refers to the techniques of Data Analysis. It includes Algorithms, the process of Data Mining methods, etc. Based on this techniques Data Scientist can figure it out which method gives more efficient/quick results with fewer calculations.

• Data Analysis: Assume that we have a huge number of data in our storage devices. But if we don’t interpret such data, it will be useless, but if we do/perform analysis on that data, We might can predict something that can help mankind. We can predict based on data and can increase the selling / can understand the need of people in particular areas and many more things can be done by Data Analysis.

• Data Mining: Now this is a very basic term when we are dealing with a large amount of data. This is a basic term, but important as hell. Data can be any form either Structured or Unstructured or Semi-Structured, Data Mining techniques convert data into one single form that can be easily retrieved from the server to any user and can be easy for Analysis.

• Data Science: All the above fields/terms are the child of Data science. D.S is the father of all the above listed. D.S mainly includes Data and Algorithms where, Predictive Analysis/Modelling, Statistical Analysis, Probability Models and involves very complex mathematics in D.S.

• Machine Learning: There is no co-relation between M.L and D.S, but M.L can help in D.S. Machine Learning is different from Data Science. Somewhere is crashed between prediction as both uses Data and Algorithms, but it is not similarity. Machine learning is making machines to learn from the data, wherein D.S we’re making decisions from data. M.L includes NLP (Natural language Processing, to be more specific Corpora data set) where Human understandable Language split into chunks and then by applying some M.L algorithms it can be translated into machine understandable language. M.L is a subfield of Artificial Intelligence.

• Big Data: As the term suggests, Big data, means giant data. As we know the number of videos is uploading on Youtube every day, Number of tweets on twitter in a single day, number of pictures on Instagram well this all data can be noted as Big data that needed to be handled as we have some limited amount of data servers. and also there’s a huge possibility that data gets repeated.

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