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Introduction to Optimization in Python

DataCamp, Online
Length
4 hours
Next course start
Start Anytime! See details
Course delivery
Self-Paced Online
Length
4 hours
Next course start
Start Anytime! See details
Course delivery
Self-Paced Online
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Course description

Learn to solve real-world optimization problems using Python's SciPy and PuLP, covering everything from basic to constrained and complex optimization.

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  • Self-Paced Online
  • Online
  • English

Training Course Content

Optimization problems are ubiquitous in engineering, sciences, and the social sciences. This course will take you from zero optimization knowledge to a hero optimizer. You will use mathematical modeling to translate real-world problems into mathematical ones and solve them in Python using the SciPy and PuLP packages.


Apply Calculus to Unconstrained Optimization Problems with SymPy


You will start by learning the definition of an optimization problem and its use cases. You will use SymPy to apply calculus to yield analytical solutions to unconstrained optimization. You will not have to calculate derivatives or solve equations; SymPy works seamlessly! Similarly, you will use SciPy to get numerical solutions.


Tackle Complex Problems Head-On


Next, you will learn to solve linear programming problems in SciPy and PuLP. To capture real-world complexity, you will see how to apply PuLP and SciPy to solve constrained convex optimization and mixed integer optimization.

By the end of this course, you will have solved real-world optimization problems, including manufacturing, profit and budgeting, resource allocation, and more.

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DataCamp
Data Science Central UK Limited, 25 Luke Street
EC2A 4EE London

DataCamp

DataCamp offers a comprehensive platform for learning data skills, specializing in training individuals and teams in data science, analytics, and AI. With a focus on interactive, hands-on learning, DataCamp provides courses across key programming languages such as Python, R, SQL,...

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