About This Course
Analytics is emerging as a competitive strategy across many sectors of business that sets apart high performing companies. Of the three major categories of analytics: descriptive, predictive and prescriptive, anecdotal evidence suggests that predictive analytics is the most frequently used analytics across several industries.
Decision makers often struggle with questions such as: Which product a customer will buy in their next purchase? What should be the right price for a soccer/Baseball/cricket player? Which products should be recommended to an existing customer? Finding right answers to these questions can be challenging yet rewarding.
Predictive analytics aims to predict the probability of occurrence of a future event such as customer churn, loan defaults, and stock market fluctuations – leading to effective business management.
Models such as multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks are frequently used in solving predictive analytics problems. Regression models help us understand the relationships among these variables and how their relationships can be exploited to make decisions.
The primary objective of this course is to help you understand how to use the predictive analytics tools to analyze real-life business problems such as prediction, classification, and discrete choice problems. We will focus on case-based practical problem solving using predictive analytics techniques to interpret model outputs. You will be exposed to software tools such as MS Excel, R, SPSS, and SAS; and taught how to use these software tools to perform regression, logistic regression, and forecasting.
Did you know that Amazon earns 35% of its revenue through its powerful recommender system? If you are in the quest for the right competitive strategy to make companies successful, then join us to master the tools of predictive analytics.
