Intermediate Network Analysis in Python

Starting at USD 39.00
4 hours duration

Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.

Are you looking to expand your knowledge and skills in network analysis after completing DataCamp's Introduction to Network Analysis in Python course? If so, this course is perfect for you! In this course, you will delve deeper into advanced techniques for analyzing various types of networks, including social, transportation, and biological networks. Throughout the course, you will build upon your existing knowledge and skills to tackle more complex problems in network analytics. You will acquire both conceptual and practical skills necessary to analyze evolving time series of networks. Additionally, you will explore the concept of bipartite graphs and learn how to utilize them in product recommendation systems. Furthermore, you will gain an understanding of graph projections and their significance in the field of Data Science. You will also discover the most effective methods for storing and loading graph data from files. To solidify your understanding, the course concludes with a comprehensive case study involving the analysis of a forum dataset. By completing this course, you will emerge as a proficient Pythonista Network Analyst, equipped with the expertise to handle intricate network analysis tasks.

About Provider

DataCamp
DataCamp
Online Education Provider ยท 410 courses
Description
DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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