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Solving the Traveling Salesperson Problem in Python

Learn about solving the Traveling Salesperson Problem using Python. Explore geospatial data, clustering, network graphs, and Docker to optimize routes and create dynamic, interactive visualizations.

45 Lessons

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LEARNING OBJECTIVES

An understanding of geospatial data manipulation, plotting, and their application to optimizing routes
Working knowledge of distance calculation techniques, their role in solving the traveling salesperson problem (TSP), and alternate methods to solve TSP
Hands-on experience clustering sales data and finding patterns
Ability to create interactive dashboards showcasing optimal routes and data mining insights

Learning Roadmap

45 Lessons1 Quiz9 Challenges

What the Traveling Salesperson Problem Is About

Get familiar with the TSP, its challenges, practical applications, and geospatial data intricacies.

Getting Started

Introduction to the Traveling Salesperson Problem

Background on the Traveling Salesperson Problem

Understand Geospatial Data

Quiz about the Traveling Salesperson Problem

Preprocessing of Traveling Salesperson Data

Unpack the core of data preprocessing, analysis, manipulation, and visualization for the Traveling Salesperson Problem.

Import Data

Inspect Data: Data Analysis

Inspect Data: Plotting

Test Your Data Inspection Knowledge

Manipulate Data

Test Your Data Manipulation Knowledge

Solving the Traveling Salesperson Problem

12 Lessons

Master the steps to comprehensively calculate and optimize routes for the Traveling Salesperson Problem using various distance metrics and algorithms.

Traveling Salesperson Data Mining

8 Lessons

Grasp the fundamentals of network graphs, data clustering, KPI enhancement, data enrichment, and interactive storytelling techniques.

Building the Traveling Salesperson Dashboard

2 Lessons

Solve problems in building interactive dashboards for visualizing TSP using Python’s Dash library.

Scalability

2 Lessons

Tackle large-scale TSP using heuristic methods and cloud platforms for enhanced efficiency.

Conclusion

Practice using skills in geospatial data, visualization, clustering, and application deployment.

Course Summary

Appendix

9 Lessons

Learn how to use Python tools and platforms for TSP development and deployment.

Use Simulated Annealing for the Traveling Salesman Problem

Project

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Certificate of Completion

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Developed by MAANG Engineers

ABOUT THIS COURSE

Solving complex computational problems is a resource-demanding task. The traveling salesperson problem (TSP) is one such problem, which is an NP-hard problem. In the era of data science, data-centric approaches have evolved to be a good choice to approximate the solutions. In this course, you’ll dive into the fascinating realm of geospatial data manipulation, distance calculation, clustering, network graphs, and Docker containerization, all tied together to optimize the challenging TSP. You’ll first grasp the intricacies of manipulating geospatial data and plotting it. Next, you’ll delve into clustering sales data, enhancing your ability to make data-driven decisions and visualize the data mining results in interactive dashboards. By the end of this course, you’ll be skilled in tackling TSP effectively and creating geospatial data visualizations. You’ll also be well-equipped to scale route optimization, data analysis, and cloud deployment.

ABOUT THE AUTHOR

Jesko Rehberg

Jesko loves working, teaching and learning data analysis using Python.

Learn more about Jesko

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