Differences between big data, machine learning, data science, and data analytics. Following is just a brief intro to what these fields are, otherwise, all these fields are cliffs on their own.
Big Data: As the name suggests it is a huge amount of data that is to be processed. there are different tools through which we can analyze the data like map reduce, hive, apache pig, and many more. recently big data is really hyped keyword because big data came due to its potential to store and process structured(DBMS), semi-structured (XML.etc), and unstructured data(text. etc).
Machine Learning: It is the study of pattern recognition from the given data set. Machine learning is the study and construction of algorithms that can learn from and make predictions on data. As Wikipedia defines “Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.” For example: what you see on youtube, Facebook, etc. homepages says “RECOMMENDED FOR YOU”. that means they have tracked your gestures like what you see, like, comment on, and search on them. so they give customized results for their personal liking, these are based on machine learning models.
Data Science: It involves a lot more than just machine learning and data analytics, it cannot be just achieved by doing a course. It comes through great experiences that one has gone through, you can be a data analyst by doing a course. It is more clear through this Venn diagram:
Data Analytics: Data analysis referred to as the analysis of data is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
DBMS: The software which is used to manage databases is called Database Management System (DBMS). For Example, MySQL, Oracle, etc. are popular commercial DBMS used in different applications.