Learning Objectives : 1. Introduce R as a programming language 2. Introduce the mathematical foundations required for data science 3. Introduce the first level data science algorithms 4. Introduce a data analytics problem solving framework 5. Introduce a practical capstone case study Learning Outcomes: 1. Describe a flow process for data science problems (Remembering) 2. Classify data science problems into standard typology (Comprehension) 3. Develop R codes for data science solutions (Application) 4. Correlate results to the solution approach followed (Analysis) 5. Assess the solution approach (Evaluation) 6. Construct use cases to validate approach and identify modifications required (Creating)
Prof.Shankar Narasimhan is currently a professor in the department of Chemical Engineering at IIT Madras. His major research interests are in the areas of data mining, process design and optimization, fault detection and diagnosis and fault tolerant control. He has co-authored several important papers and a book titled Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data which has received critical appreciation in India and abroad.
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