What's the difference between Data Science, Data Analysis, Big Data, Data Analytics, Data Mining and Machine Learning?

Here's a great infographic that shows the variations of what Data 360 does. Check out these key definitions:

Data Mining

Uses the predictive force of machine learning by applying various machine learning algorithms to Big Data


Data Analytics

Automated insights into a dataset and supposes the usage of queries and data aggregation procedures. Can represent various dependencies between input variables, but also can use Data Mining techniques and tools to discover hidden patterns in the dataset under analysis.


Data Analysis

Human activities aimed at gaining some insight on a dataset analysis. Analysts can use some Data Analytics tools to obtain desired results, but in principle. Data Analysis can be performed without a special data processing.


Data Science

Deals with structured and unstructured data. Everything that relates to data cleansing, preparation and analysis.


Big Data

Huge data volumes that cannot be processed effectively with traditional applications. Begins with raw data that is not aggregated and it is often impossible to store such data in the memory of a single computer.


Machine Learning

Artificial intelligence technique that is broadly used in Data Mining. Uses a training dataset to build a model that can predict values of target variables.


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