A simple definition of analytics is "the science of analysis". A practical definition, however, would be that analytics making the best decision possible by leveraging the right data and applying good critical thinking skills. Traditionally business managers may choose to make decisions based on past experiences/ thumb rules or there might be other qualitative aspects to decision making; but unless there are data involved in the process, it would not be considered analytics. Currently analytics is been leveraged and practiced by all types of industries irrespective of their size. Top-performing organizations use analytics five times more than lower performers. Organizational leaders want analytics to exploit their growing data and computational power to get smart, and get innovative, in ways they never could before. Senior executives now want businesses run on data-driven decisions. They want scenarios and simulations that provide immediate guidance on the best actions to take when disruptions occur – disruptions ranging from unexpected competitors or an earthquake in a supply zone to a customer signaling a desire to switch providers. Executives want to understand optimal solutions based on complex business parameters or new information, and they want to take action quickly. These expectations can be met – but with a caveat. For analytics-driven insights to be consumed – that is, to trigger new actions across the organization – they must be closely linked to business strategy, easy for end-users to understand and embedded into organizational processes so that action can be taken at the right time. That is no small task. It requires painstaking focus on the way insights are infused into everything from manufacturing and new product development to credit approvals and call center interactions. Analytics include complex statistical analysis, computational modeling and data mining. These is applied in predictive science, strategy science, campaign management, fraud analytics, credit risk analysis, marketing analytics, Inventory & supply chain and so on.

Course Contents:

• Introduction - Statistics & Applications • Descriptive statistics – Measure of central tendency & dispersion (Mean, Median, Mode, Variance, Standard Deviation, Quartiles, skweness & kurtosis etc)
• Univariate & Bivariate analysis - Includes Types of data, frequency tables & cross-tabulation, histograms etc
• Data preparation – Cleaning, Validation & Outlier detection
• Probability concepts
• Hypothesis Testing – T test, F test, Chi Square test
• ANOVA & Correlation
• Regression - Simple Linear & Multiple
• Logistic Regression
• Principle Component Analysis
• Factor Analysis
• Cluster Analysis
• Discriminant Analysis
• Domain specific modeling applications – Retail, Insurance, Banking, Travel etc with a real time examples