Predictive Analytics Course | PGPBA

predictive analytics

Recently a lot of buzz has been created with “Analytics” entering every nook and cranny and getting a lot of attention by businesses all across the world. One such term that has been buzzing around is “Predictive Analytics”. Let us understand what is Predictive Analytics?
The definition goes like:
“Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data”.
It is basically understanding the data in hand to analyze trends, patterns and predict the future. This prediction occurs using predictive analysis techniques and predictive analytics algorithms. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.
But is it really recent? … well, the answer is no. Predictive analytics has been around for decades, the only thing that has changed is it has found a lot of momentum recently with valuable assists from many organisations. More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage.
But the question still lingers… Why and Why Now? Well, there are a few reasons behind this drastic change:

  • Growing volumes and types of data, and more interest in using data to produce valuable insights
  • Faster, cheaper computers
  • Easy-to-use software
  • Tougher economic conditions with a need for competitive advantage and point of differentiation

Predictive analytics is an enabler of big data: Businesses collect vast amounts of real-time customer data and predictive analytics uses this historical data, combined with customer insight, to predict future events. Predictive analytics enable organizations to use big data (both stored and real-time) to move from a historical view to a forward-looking perspective of the customer.

Why Predictive Analytics Course is Important?

A lot of organizations are turning to Predictive Analytics to increase their bottom line and competitive advantage. Industries like Banking & Financial Services, Retail, Oil & Gas, Health Insurance, Manufacturing, Government and Public Sector had dwelled into this arena of Predictive Analytics.
Some common uses include:

  • Detecting fraud  –  Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. As cybersecurity becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.
  • Optimizing marketing campaigns –  Predictive analytics is used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.
  • Improving operations –  Many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.
  • Reducing risk – Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s credit worthiness. Other risk-related uses include insurance claims and collections.
  • Predictive Analytics Tools –  A lot of tools are available to perform Predictive Analytics like IBM SPSS Modeler, MATLAB, Alteryx, R, SAS, Apache Mahout, Minitab etc.

We at SOIL as a part of BLP Analytics have been taught R, SAS and IBM SPSS. We provide Predictive Analytics Courses as part of our Business Analytics Course (PGPBA). We have followed a case study based approach wherein we have worked on various Real-Life case studies through SAS and R. We have worked on Live Projects along with various organizations wherein we have applied our knowledge on R, SAS and IBM SPSS to understand the problem and deliver the best solution.

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