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Linear Programming for Data Science
4.59
Beginner level
4.5 learning hrs
Enhance your optimization techniques by learning Linear Programming for Data Science.
Instructor:
Dr. Abhinanda Sarkar
Modules updated 07/2025
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
Get access to the complete curriculum once you enroll in the course
4.59
What our learners enjoyed the most
Skill & tools
62% of learners found all the desired skills & tools
Our course instructor
Dr. Abhinanda Sarkar
Senior Faculty & Director Academics, Great Learning
Data Science Expert
Frequently Asked Questions
Will I receive a certificate upon completing this free course?
Is this course free?
Will I get a certificate after completing this Linear Programming for Data Science free course?
Yes, you will get a certificate of completion for Linear Programming for Data Science after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.
How much does this Linear Programming for Data Science course cost?
It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Linear Programming for Data Science can get started with this course.
Is there any limit on how many times I can take this free course?
Once you enroll in the Linear Programming for Data Science course, you have lifetime access to it. So, you can log in anytime and learn it for free online.
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Linear Programming in Data Science
Linear programming for Data Science is a course designed to help learners understand and apply the principles of linear programming in the Data Science context. It provides an in-depth understanding of linear programming principles, assumptions, and applications and their practical applications in areas such as optimization and decision-making. The course includes a wide range of topics, including introduction to linear programming, graphical method, sensitivity analysis, and assumptions in linear programming.
This course on Linear Programming for Data Science is designed to provide learners with a comprehensive overview of the principles and techniques of linear programming. Learners will learn to apply linear programming to solve various optimization problems. The course will begin by discussing the basics of linear programming, definitions, and the different mathematical models.
The graphical method is used to represent linear programming problems graphically. This method is used to identify the optimal solution and analyze the solution's sensitivity to changes in the problem parameters. The graphical method is used to identify the optimal solution and also analyze the sensitivity of the solution to changes in the problem parameters.
Sensitivity analysis is also included in this course. This is an essential tool used in linear programming to examine the effects of changes in the parameters or the problem's constraints on the optimal solution. This method is used to identify the optimal solution and analyze the sensitivity of the solution to changes in the problem parameters.
Finally, the assumptions in linear programming are discussed. This includes the assumptions of linearity, homogeneity, additivity, convexity, and non-negativity. These assumptions help to simplify the optimization process, leading to faster and more accurate solutions.
This course on linear programming for data science provides learners with a comprehensive understanding of the principles, assumptions, and applications of linear programming. Learners will gain a strong understanding of the mathematical models and the graphical methods used to solve linear programming problems. Furthermore, they will gain an understanding of the assumptions of linear programming and the sensitivity analysis used to identify the optimal solution. This course is an excellent resource for learners interested in applying linear programming in the data science field.