See the major difference between the sub-categories of data science, machine learning, and big data.
It is a process in which large sets of data (Big Data) are collected, organized and analyzed to discover useful patterns/findings, uncover hidden patterns, market trends, and customers preferences. These patterns provide useful information that can help a company to produce future decisions. Data analytics are techniques of data analysis (discussed below). These techniques include algorithms and data mining methods to give results with fewer calculations.
Data analysis is a process to inspect, clean and transform data to extract the useful information that is required using analytical and logical reasoning. There are many methods to analyze data. These methods include data mining (discussed below), text analytics, business intelligence, etc.
Data mining is a process by which companies extract useful information from raw data (data may be in any form i.e. structured, unstructured or semi-structured). By using one or more software, from huge sets of data, patterns are discovered that help to learn about customers and develop effective marketing strategies. Data mining techniques help to convert data in one form so that the data can be retrieved easily from the server. Another name for data mining is KDD (Knowledge Discovery in Data).
Data Science is exploratory and useful in getting to know the data. Automated methods are used to analyze a massive amount of data. You can say, data science is the father of above all (data analytics, data analysis, and data mining). Data science includes very tough and complex Mathematics in it. It consists of tools, methods, processes, algorithms, and systems to have insights into data.
There is no correlation between data science and machine learning but machine learning can be used for making machines to learn (improve performance on specific tasks) from data. It is a field of Computer Science that gives computers the ability to learn using different statistical techniques. From huge data sets, machine learning can apply knowledge to excel at speech recognition, facial recognition or many other tasks. Machine learning provides systems with the opportunity of recognizing patterns and making predictions.
This term explains a really huge amount of data like data generated at banks or data of transportation services. This data might be present in both forms structured and unstructured. To maintain Big Data databases, you have to write programs in Java, Scala, Python, C++, R, etc. Companies analyzed this big amount of data for better decision making. Basically volume of the data is not important but the main question is What you do with it? By analyzing the data properly, many questions can be answered that can lead to business benefits like cost reduction, time management, new product development, decision making, etc.