Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.
By continuing you accept the Terms of Use and Privacy Policy, that your data will be stored outside of the EU, and that you are 16 years or older.
Have you taken DataCamp's Network Analysis in Python (Part 1) course and are yearning to learn more sophisticated techniques to analyze your networks, whether they be social, transportation, or biological? Then this is the course for you! Herein, you'll build on your knowledge and skills to tackle more advanced problems in network analytics! You'll gain the conceptual and practical skills to analyze evolving time series of networks, learn about bipartite graphs, and how to use bipartite graphs in product recommendation systems. You'll also learn about graph projections, why they're so useful in Data Science, and figure out the best ways to store and load graph data from files. You'll consolidate all of this knowledge in a final chapter case study, in which you'll analyze a forum dataset and come out of this course a Pythonista Network Analyst ninja!
Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.
In this chapter, you will learn about bipartite graphs and how they are used in recommendation systems. You will explore the GitHub dataset from the previous course, this time analyzing the underlying bipartite graph that was used to create the graph that you used earlier. Finally, you will get a chance to build the basic components of a recommendation system using the GitHub data!
In this chapter, you will delve into the fundamental ways that you can analyze graphs that change over time. You will explore a dataset describing messaging frequency between students, and learn how to visualize important evolving graph statistics.
In this chapter, you will use a famous American Revolution dataset to dive deeper into exploration of bipartite graphs. Here, you will learn how to create the unipartite projection of a bipartite graph, a very useful method for simplifying a complex network for further analysis. Additionally, you will learn how to use matrices to manipulate and analyze graphs - with many computing routines optimized for matrices, you'll be able to analyze many large graphs quickly and efficiently!
In this chapter, you will apply everything you've learned in the previous three chapters to a forum posting dataset. You will analyze the temporal changes in forum user connectivity patterns, and make visualizations of evolving graph statistics over time.
In this chapter, you will learn about bipartite graphs and how they are used in recommendation systems. You will explore the GitHub dataset from the previous course, this time analyzing the underlying bipartite graph that was used to create the graph that you used earlier. Finally, you will get a chance to build the basic components of a recommendation system using the GitHub data!
In this chapter, you will use a famous American Revolution dataset to dive deeper into exploration of bipartite graphs. Here, you will learn how to create the unipartite projection of a bipartite graph, a very useful method for simplifying a complex network for further analysis. Additionally, you will learn how to use matrices to manipulate and analyze graphs - with many computing routines optimized for matrices, you'll be able to analyze many large graphs quickly and efficiently!
In this chapter, you will delve into the fundamental ways that you can analyze graphs that change over time. You will explore a dataset describing messaging frequency between students, and learn how to visualize important evolving graph statistics.
In this chapter, you will apply everything you've learned in the previous three chapters to a forum posting dataset. You will analyze the temporal changes in forum user connectivity patterns, and make visualizations of evolving graph statistics over time.
Join over 3,210,000 others learning to leverage the power of data with DataCamp!
Start Course For Free