• Introduction – RDBMS Overview
• Challenges of Conventional Systems
• Intelligent Data Analysis
• Nature of Data
• Analytic Processes and Tools
• Analysis vs Reporting
• Statistical Concepts:
• Sampling Distributions
• Re-Sampling
• Statistical Inference
• Prediction Error, Accuracy measures, Cutoff
• Oversampling and Asymmetric Costs
• Big Data - Volume, Velocity, Variety, Veracity
• Types and Sources of Big Data 
• OLAP and RTAP.

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• Introduction to Streams Concepts
• Stream Data Model and Architecture, 
• Stream Computing, 
• Sampling Data in a Stream: Stream sampling is the process of collecting a representative sample of the elements of a data stream. The sample is usually much smaller than the entire stream, but can be designed to retain many important characteristics of the stream, and can be used to estimate many important aggregates on the stream. Unlike sampling from a stored data set, stream sampling must be performed online, when the data arrives. Any element that is not stored within the sample is lost forever, and cannot be retrieved. 
• Filtering Streams, 
• Counting Distinct Elements in a Stream, 
• Case Study: Real Time Sentiment Analysis/Stock Market Prediction.