Linear Programming for Data Science
- Offered byGreat Learning
Linear Programming for Data Science at Great Learning Overview
Linear Programming for Data Science
at Great Learning
Duration | 3 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course |
Credential | Certificate |
Linear Programming for Data Science at Great Learning Highlights
Linear Programming for Data Science
at Great Learning
- Earn a certificate of completion
Linear Programming for Data Science at Great Learning Course details
Linear Programming for Data Science
at Great Learning
What are the course deliverables?
- Linear Programming
More about this course
- Linear programming is an optimization technique to identify the optimal solution in a mathematical or business model for a system of linear constraints and a linear objective function
- Linear Regression in Data Science is one of the hot topics today
- It is a technique that identifies a linear relationship between dependent and independent variables
- In this course, you will get introduced to Linear Programming, its Graphical method, sensitivity analysis, and assumptions in Linear Programming, and some hands-on exercise
Linear Programming for Data Science at Great Learning Curriculum
Linear Programming for Data Science
at Great Learning
Introduction to Linear Programming
Graphical method
Sensitivity analysis
Assumptions in Linear Programming
Case study - Investment Problem
Case study - Portfolio optimization
Case study - Recruitment planning
Linear Programming for Data Science at Great Learning Faculty details
Linear Programming for Data Science
at Great Learning
Dr. Abhinanda Sarkar
Designation : Faculty Director, Great Learning
Description : Dr. Abhinanda Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. Dr. Sarkar received his B.Stat. and M.Stat. degrees from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. He has taught applied mathematics at the Massachusetts Institute of Technology (MIT); been on the research staff at IBM; led Quality, Engineering Development, and Analytics functions at General Electric (GE); served as Associate Dean at the MYRA School of Business; and co-founded OmiX Labs.
Dr. Sarkar’s publications, patents, and technical leadership have been in applying probabilistic models, statistical data analysis, and machine learning to diverse areas such as experimental physics, computer vision, text mining, wireless networks, e-commerce, credit risk, retail finance, engineering reliability, renewable energy, and infectious diseases, His teaching has mostly been on statistical theory, methods, and algorithms; together with application topics such as financial modeling, quality management, and data mining.
Dr. Sarkar is a certified Master Black Belt in Lean Six Sigma and Design for Six Sigma. He has been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc). Over the years, he has designed and conducted numerous corporate training sessions for
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