A Lot of Study and Hard work need to do before bring Big Data analytics tools to help deliver that value and bring that data to life. To extracting and transforming data into a usable format, but once that’s done, Analysed data can provide greater insights into their owners, business, customers, and industry.

In a discussion of the history of analytics, highlights a number of communities from which learning analytics draws techniques, including:

1) Statistics: :

Statistics is Essentials for Analytics, knowledge in Statistics is mandatory for Analytical tools, Stats nothing but study of the collection, analysis, interpretation, presentation, and organization of data.
The major topics cover under Statistics as a profile for Bigdata Developer or Analyst :
In applying statistics to, e.g., a scientific, industrial, or societal problem, it is conventional to begin with a statistical population or a statistical model process to be studied.
1.Exploratory Analysis
2.Few terminologies on Statistics
3.Variance
4.Standard Deviation
5.Inquartile Range
Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments
The topics related to Statistics & Probability have extensively been covered in our course ‘Statistics Essentials for Analytics’.

2) Business Intelligence

The wide range of the Business Intelligence is nothing but Big data Analysis, which has similarities with Dash boards, MIS Reports, Decisions making analytics, Process flow optimization etc., are new form of the Business Intelligence as Big data Analytics. Although it has historically been targeted at making the production of reports more efficient through enabling data access and summarizing performance indicators.

3) Web Analytics

Tools such as Facebook Pixel, Google Analytics, Digital Marketing Analysis, Online Survey etc are the most popular web analytics. Report on web page visits and references to websites, brands and other key terms across the internet.

4) Operational research

Every Big data project needs some research and build the model based on the requirements. Analyst or Data Scientist will do special Research about design optimisation for maximising objectives through the use of mathematical models and statistical methods.

5) Artificial Intelligence and Data Mining

machine learning techniques built on data mining and AI methods are capable of detecting patterns in data. In learning analytics such techniques can be used for intelligent tutoring systems, classification of students in more dynamic ways than simple demographic factors, and resources such as “suggested course” systems modeled on collaborative filtering techniques.

6) Social Network Analysis (SNA)

The Big data’s 80 % of huge data is generated by the most visited domains on the Internet related social media networks like Facebook, Twitter, and Google+, Instagram, Youtube etc. They contain huge data about the users and the relationships among them. By using analyse and mine , special graph-based mining tools are required that can easily model the structure of the social networks and dig useful information from these huge social network data. By choosing appropriate analysis tools for a particular task is difficult to decide few of the most popular tools use for Social media analysis example Gephi, Pajek, IGraph. We need to select the proper tools based on platform, execution time, Graph types, algorithms complexity, input file format and graph features. A number of such analysis tools are available with their own features and benefits.

7) Information visualization

The most important final Step in many Analytics, is Data Visualization or Information Visualization, around the data provided, and is used across most techniques to Provide best expectations form Analyst by Data owners (including those above)