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

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1. Data analytics is the general term for several activities, including data exploration, data modeling, machine learning and more.

2. Data analysis aims to explore data with statistical techniques like mean or median; categorical variables are described using frequencies; scatterplots show pairs of numerical variables; graphical summary displays (like histograms) of quantitative variables can be used to summarize groups of numbers in a single display, which shows how the distribution of values changes with one or more explanatory values that concur with your question you’re trying to answer. 3. Data mining entails analyzing summary statistics on large databases of information by correlating various factors to each other in order to look for patterns where they exist – but without knowing ahead of time what factors might be related to each other.

4. Data mining results in models that summarize data for predictive purposes, where the relationships are unclear and need to be identified using statistical analysis by computing correlations between variables to reveal underlying clusters of values that form relationships which can then be used for predictive modeling

5. Data visualization is the graphical display of data with the goal of communicating information clearly, allowing you to see “the big picture” at a glance but often with minimal detail about individual cases or emphasizing only certain aspects of the data – rather than providing extensive amounts of raw information or summary statistics computed on an entire dataset.

6. Data science is the study of data, which is a large set of data that has been organized and structured in a specific way. Data science allows you to analyze and explore data in order to find patterns and relationships. With data science, you can also build models to predict future events.

7. Machine Learning is a subset of artificial intelligence in the field of computer science, concerned with giving computers the ability to learn without being explicitly programmed whereas data mining and machine learning are methods to discover patterns in large datasets which can be used for predictive modeling or for extracting information from unstructured or unorganized data by using statistical algorithms or artificial intelligence to cluster similar groups together based on predetermined rules that were established beforehand. Its goal is self-optimization through continuous training and refinement of its abilities over time given a particular task set.

8. Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, manage, and process the data within a tolerable elapsed time. In recent years, this term has become very fashionable in business and media, being used to refer to all kinds of datasets, including those that do not fit the above criteria for “Big Data”.