Basics of Analytics

Hello Everyone,

In this article we are going to discuss about basics of Analytics

As we all know Analytics is the hottest and most demanding skill in current world of employment. But most of us are not aware that analytics have been in the core business for more than a decade now

In this blog i am going to explain about the types of analytics. Understanding this helps to visualize the basic structure of analytics.

Overview:

• Data analytics is a multidisciplinary field.
• There is extensive use of computer skills, mathematics and statistics, the use of various tools and techniques to gain valuable knowledge from data.
• The insights from data are used to recommend action or to guide decision making rooted in business context.

Necessity:

• Growing amount of data warrants the industry to innovate and advance more rapidly in the way we manage data
• Study on Data will open a host of opportunities for new businesses

Potential:

• On average, Google now processes more than 40,000 searches EVERY second (3.5 billion searches per day)
• 1.5 billion people are active on Facebook daily
• More than 300 million photos get uploaded per day
• Every minute there are 103,447,520 spam emails sent

Analytics can be broadly divided into 3 types:

1. Descriptive Analytics
2. Predictive Analytics
3. Prescriptive Analytics

Descriptive Analytics:

• It use data aggregation and data mining to provide insight into the past and answer: “What has happened?” 
• Over 80% of the business analytics are descriptive analytics. They compute descriptive statistics (i.e. counts, sums, averages, percentages, min, max and simple arithmetic: + − × ÷) that summarizes certain groupings or filtered version of the data, which are typically simple counts of some events
• Example: Analytics provided by Blogger and Google analytics. It basically collects the data from the viewers and provides the slices and dices of data in various forms

Predictive Analytics:

• Its uses statistical models and forecasts techniques to understand the future and answer: “What could happen?”
• The purpose of predictive analytics is NOT to tell you what will happen in the future. It cannot do that. In fact, no analytics can do that. Predictive analytics can only forecast what might happen in the future, because all predictive analytics are probabilistic in nature.
• Example: This method was adopted by Cambridge Analytica for US Presidential election 2016. It took the Facebook users data with the permission of Facebook to curate the audience to whom advertisements favoring the Democratic candidate should be displayed. This privacy concerns has resulted in a 120$ loss of market capital for Facebook. You can see more details of the Facebook scandal in various other pages.

Prescriptive Analytics:

• It uses optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?”
• A prescriptive model can be viewed as a combination of multiple predictive models running in parallel, one for each possible input action. Since a prescriptive model is able to predict the possible consequences based on different choice of action, it can also recommend the best course of action for any pre-specified outcome.
• Example: Autonomous cars. We all were unknowing training the OCR engine of futuristic Autonomous vehicles for years. These vehicles scans the surroundings every seconds and assesses the objects moving around. It then computes all data of each objects such as 
• whether it is a car or a sign board or a signal, 
• whether it is moving object or not, 
• if it is moving then at what speed it is moving, 
• it goes one step ahead and tags the object if there could be potential threat to the vehicle. 
• By doing so the vehicles will be moving in a very specific speed and distance.