본문으로 바로가기

강의

Intermediate Network Analysis in Python

고급기술 수준

업데이트됨 2026. 4.

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

무료로 강의 시작

PythonProbability & Statistics

4시간

13 동영상

46 연습 문제

3,850 XP

14,004

성취 증명서

수천 개 기업의 학습자들이 사랑하는

팀을 교육하시나요?

비즈니스용으로 체험해 보세요

강의 설명

Have you taken DataCamp's Introduction to Network Analysis in Python 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!

선수 조건

Introduction to Network Analysis in Python

1

Bipartite graphs & product recommendation systems

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!

Definitions & basic recap

Exploratory data analysis

Plotting using nxviz

Bipartite graphs

The bipartite keyword

Degree centrality distribution of user nodes

Degree centrality distribution of project nodes

Bipartite graphs and recommendation systems

Shared nodes in other partition

User similarity metric

Find similar users

Recommend repositories

2

Graph projections

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!

Concept of projection

Reading graphs

Computing projection

Plot degree centrality on projection

Bipartite graphs as matrices

Properties of bipartite adjacency matrices.

Compute adjacency matrix

Find shared membership: Transposition

Representing network data with pandas

Make nodelist

Make edgelist

3

Comparing graphs & time-dynamic graphs

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.

Introduction to graph differences

List of graphs

Graph differences over time

Plot number of edge changes over time

Evolving graph statistics

Number of edges over time

Degree centrality over time

Zooming in & zooming out: Overall graph summary

Find nodes with top degree centralities

Visualizing connectivity

4

Tying it up!

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.

Introduction to the dataset

Create a graph from the pandas DataFrame

Visualize the degree centrality distribution of the students projection

Visualize the degree centrality distribution of the forums projection

Time based filtering

Time filter on edges

Visualize filtered graph using nxviz

Time series analysis

Plot number of posts being made over time

Extract the mean degree centrality day-by-day on the students partition

Find the most popular forums day-by-day: I

Find the most popular forums day-by-day: II

Congratulations!

Intermediate Network Analysis in Python

강의
완료

수료증 획득

LinkedIn 프로필, 이력서 또는 CV에 이 인증서를 추가하세요
소셜 미디어와 성과 평가에서 공유하세요지금 등록

19백만 명 이상의 학습자와 함께 Intermediate Network Analysis in Python을(를) 시작하세요!

DataCamp for Mobile을 통해 데이터 분석 능력을 향상시키세요.

모바일 강좌와 매일 5분 코딩 챌린지를 통해 이동 중에도 학습 효과를 높이세요.