Big Data Analytics

Imagine the flow of information which is being generated from various sources, be it social media, search engines, banking sectors, government departments etc.  In the world of advanced Information Technology, analyzing data has become crucial. This is the reason Big Data Analytics is being adopted throughout the world in order to gain new insights and benefits pertaining to humungous data being produced.  It is important to know that on a daily basis, about 2.5 quintillions of data is being created. We should know the fact that data is coming from various sources which can be characterised by 5 V’S which are Volume, Veracity, Velocity, Variety & Value. In this blog, we walk through the various domains of big data analytics and related components. 

Why Big Data Analytics? 

Big Data Analytics is a blend of Machine Learning, Business Acumen, Algorithms, Specialized tools and Mathematics. These components help to find or arrange hidden patterns in the raw data. These patterns are further used by organizations to build a better marketing strategy and products. Syllabus of data science or big data analytics stress over the following skills: 

• Knowledge of R
• Python Coding 
• Hadoop Platform 
• MS Excel 
• SOL DataBase
• Mathematical Expertise 
• Working with Unstructured Data 
• Business Acumen

Big Data Analytics: Steps

• Identifying a problem
• Design a data requirement 
• Pre-processing the data
• Performing analytics over data
• Data visualization

Types of Big Data Analytics

• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics
• Diagnostics analytics

Tools

• Hadoop
• Hive
• Splunk
• Kafka
• Spark
• Apache Hbase
• Talend

Formats and Characteristics of Data

There are three main characteristics of big data which are called as 3 V’s, i.e. Volume, Variety and Velocity. Volume is a huge generation of data being produced from various sources. Variety elucidates upon the formation of data. On the other hand, Velocity is termed as the rate at which the data is being generated. Other than these three, there is also corresponds to the entire collection of Big Data from which we derive meaningful information. It is also used to refer to the inconsistencies as well as uncertainties that are present in the data. 

It is imperative to know that Big Data Analytics has generally three different formats, i.e. Structured, Semi-Structured and Unstructured. 

• Structured Data: In the form of tables based on columns. 

• Unstructured Data: In the form of audio files, video files, images etc. 

• Semi-structured Data: The kind of data which lacks a proper rigid scheme and doesn’t conform to a data model.

Why is Data Analytics important?

Since the flow of information is humungous, there is an increased demand for big data analytics experts in various organisations to handle data efficiently. Let us see why:

• It is important for helping in smarter as well as more efficient data handling in organizations. 
• It has helped in stopping the crime-related activities as police departments in Ameria and other developed countries are efficiently utilizing data patterns, scientific analysis and technological tools in order to tackle the instantly criminal activities and control the crime hotspots. 
• It has tremendously helped in business optimisation in order to analyse consumer behaviour. For example, Amazon is efficiently analyzing customer behaviour and understanding pertaining to product purchases. How a customer selects a product, checks its details, navigates through related suggestions etc. helps in mapping the website navigation behaviour. It also indirectly helps companies to know customer behaviour and enhance their products and services.
• It has reduced healthcare costs significantly as medical professionals are now readily available on many health apps to provide people with the required guidance on taking care of different health concerns.
• It is certainly a next-generation product. For example, Google self-driving cars are taking independent decisions while driving on the road without the intervention of humans. Netflix is also a great example of the application of Analytics. 

Big Data Analytics: Popular Courses & Universities

Here is a list of popular universities and courses that are well recognized for their prospective courses offered in the specialised domain of Big Data Analytics: 

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Universities	Courses
Imperial College London	MSc Health Data Analytics & Machine Learning
University College London	MSc Energy Systems & Data Analytics MSc Spatio-temporal Analytics & Big Data Mining
Boston University	Graduate Certificate in Data Analytics MS in Environmental Health Data Analytics
Western University Ontario	Master of Data Analytics
University of Michigan	MS in Business Analytics
University of Virginia	Master of Science in Data Science (MSDS)
University of Rochester	MS in Business Analytics

Certainly, the field of Artificial Intelligence has revolutionized the entire world and has created smarter devices to carry out our daily activities. If you are aiming to study Big Data Analytics in detail, sign up for an e-meeting with our Leverage Edu experts and we will assist you in finding a suitable course and university that can equip you with the requisite knowledge and skills to build a successful career in this field!