+- Softwarez.Info - Software's World! (https://softwarez.info)
+-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone)
+--- Forum: Video Tutorials (https://softwarez.info/Forum-Video-Tutorials)
+--- Thread: Network Analytics and Visualization in Python (/Thread-Network-Analytics-and-Visualization-in-Python)
Network Analytics and Visualization in Python - BaDshaH - 02-05-2025
![[Image: 9c61cb3be1078a90307bb57839632996.jpg]](https://i124.fastpic.org/big/2025/0205/96/9c61cb3be1078a90307bb57839632996.jpg)
Published 2/2025
Created by Milan Janosov
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 17 Lectures ( 2h 59m ) | Size: 1.5 GB
From Graph Creation to Advanced Network Analytics and Visualization using Python
What you'll learn
Build and Analyze Networks in Python - Create, manipulate, and analyze real-world networks using Python
Master Key Network Metrics and Algorithms - Apply centrality measures, modularity, clustering, and other graph-based techniques to uncover characterize graphs
Develop Practical Visualization and Analysis Skills - Use Python to create interactive and informative network visualizations for data-driven decision
Design Network Analytical Pipelines - Learn the complete workflow of network analysis
Requirements
Functional, beginner level Python programing
Basic knowledge of network science concepts
Description
Course DescriptionWelcome to the world of network analytics and visualization in Python, where data connections turn into valuable insights! This course is your comprehensive guide to understanding and applying graph analytics and visualization techniques using Python. Whether you're a data scientist eager to enhance your expertise or a tech-savvy learner looking for hands-on experience, this course takes you from the fundamentals to network analytics applications with step-by-step guidance.What You'll LearnThe foundations of network analytics, including graph creation and visualization in Python.Key network concepts like centrality, modularity, and network statistics.Step-by-step techniques for building and analyzing graphs using Python (mainly NetworkX)Hands-on exercises with networks, including synthetic and real networks and their comparative analytics.How to combine Python with Gephi for advanced visualization and exploration.Why Take This Course?Network analytics is a powerful skill with applications in data science, social sciences, urban planning, biology, and beyond. This course balances theory with practical, hands-on skills to help you:Analyze and understand complex systems using network-based approaches.Master Python's network science ecosystem, including NetworkX and visualization tools.Gain a competitive edge by expanding your analytical skills into network-based data science.Who Is This Course For?This course is designed for
ata scientists and engineers who want to apply network analytics in Python.Software developers looking to incorporate graph-based solutions into their applications.Researchers and analysts exploring network-based insights in social, biological, or business domains.Python users with an introductory-level understanding of the language, eager to expand their skills.What Makes This Course Unique?Step-by-step learning: From simple graph creation to advanced analytics.Hands-on: Real-world examples, including synthetic networks and comparative analysis.Focus on both theory and practice: Recapping conceptual foundation combined with practical proficiency. A world-class instructor: Recognized for cutting-edge network visualizations featured in The New York Times, GQ, and Miami Art Week.Join us and unlock the power of network analytics in Python! By the end of this course, you'll be equipped with the tools, knowledge, and confidence to tackle real-world network data like a pro.Who this course is for
Data Scientists & Engineers - Professionals looking to expand their analytical toolkit with network-based approaches and Python-driven graph analytics.
Researchers & Analysts - Academics, social scientists, and business analysts seeking to apply network science techniques to understand complex systems and relationships.
Software Developers - Those interested in incorporating graph-based algorithms and network structures into their applications, from recommendation systems to social network analysis.
Python Enthusiasts & Learners - Individuals with a beginner-level understanding of Python who want to step into the world of network analytics and visualization.
Homepage