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.Rengaswamy was a professor of Chemical Engineering and Co-Director of the Process Control and Optimization Consortium at Texas Tech University, Lubbock, USA. He was also a professor and associate professor at Clarkson University, USA and an assistant professor at IIT Bombay. His major research interests are in the areas of fault detection and diagnosis and development of data science algorithms for manufacturing industries.
13454
1646
1301
41
358
442
460
75
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed
<40 - No Certificate
NOTE : We have taken the average of assignments in a particular week.
Week 2-Avg(A2+A3)