Decision Science in Action

Data science is one of the  most promising field for bright future.  Various Gartner reports state that most of the organisations are going to invest more strategically to use the power of analytics to drive their business decisions.  Top banks and retailers have been using the data analytics for acquiring new customers, promotional initiatives, product pricing  etc  by setting up centre of excellence in analytics in India. In last 5 years analytics profession has caught lot of attractions in the job market.  Number of institutions in India and outside India have launched academic programs including online and on campus certificate programs. Question is which program is best? I do not know but I can definitely argue that M Tech in IME at IIT Kanpur is an excellent launchpad for career in data science or analytics.There is also another M Tech program at IITB which is among top programs for data science. These are two top programs which are producing tal...

The objective of this article is to propose a design of a personalized recommendations application for restaurants owners which can be used by the customers of the given credit/debit card issuer. The developed recommendation application can be installed as mobile application in the mobile phone of the customers. The recommendation engine will use machine learning algorithms and predictive analytics techniques to derive information from  the credit transaction data, mobile location data, social media data (Facebook, Twitter), public data (Yelp.com/Bundle.com etc.), mobile browser data and data of the restaurants*. The recommendation engine will have two modules. First module will use the clustering algorithm to derive actionable information from credit card data and restaurants data. It will also use the data mining techniques to integrate the useful information available in the web space with restaurant data (explained later). This module will execute in server of the bank...

Multicollinearity is defined as the linear relationship between two or more independent variables while performing regression analysis between a dependent variables and set of independent variables. Multicollinearity presents a severe problem during regression modelling. Inclusion of independent variables having linear relationship with each other leads to parameter estimation with higher standard error. This in turn leads to inaccurate parameter estimation. Furthermore, due to inaccurate parameters regression model becomes unstable. The unstable models performs badly on the validation and test samples. When model is unstable, its performance deteriorate very fast compared to stable model over the period, though model is scored on the data of sample of same population. In such a situation an analyst must investigate for the multicollinearity, before finalizing the model. Next question is how to investigate and which variable should be kept if some variables are found to ...

In the statistics, we use data to derive the information. It helps in business, research and governance.  We also develop to predict the value or behaviour of any dependent variable based on the historical data. We have following categories of the model based on the type of the variable When the dependent variable is continuous variable: 1.OLS  Linear Regression Model :  When the dependent variable is continuous variable and independent variables is/are continuous variable(s) 2.ANOVA:  When the dependent variable is continuous variable and independent variables is/are categorical variable(s) 3.ANCOVA :  When the dependent variable is continuous variable and as independent variables, we have   continuous  as well as categorical variables as independent variable. When the dependent variable is categorical variable: 1. Maximum Likelihood Logistic Regression Model :  When the dependent variable is categorical variable and independent ...