Linear Programming for Data Science

Enhance your optimization techniques by learning Linear Programming for Data Science.

Instructor:

Dr. Abhinanda Sarkar
4.61
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Beginner

Level

4.5 Hrs

Learning hours

11.4K+
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Skills you’ll Learn

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. And 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.

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Course Outline

Introduction to Linear Programming

Linear programming is a mathematical modeling technique in which a linear function is maximized or minimized when subjected to various constraints. In this module, you will be introduced to linear programming. 

Graphical method
Sensitivity analysis
Assumptions in Linear Programming
Case study - Investment Problem
Case study - Portfolio optimization
Case study - Recruitment planning

Our course instructor

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Dr. Abhinanda Sarkar

Faculty Director, Great Learning

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503.7K+ Learners
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17 Courses
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 technology and business professionals. He is a recipient of the ISI Alumni Association Medal, IBM Invention Achievement Awards, and the Radhakrishan Mentor Award from GE India

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Linear Programming for Data Science

4.5 Learning Hours . Beginner

Why upskill with us?

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