Informações sobre o curso: This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
Para quem é direcionado este curso: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course in the specialization contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Ministrado por: Daniel Romero, Assistant Professor
School of Information
| Informações básicas | Curso 5 de 5 no Applied Data Science with Python Specialization |
| Nível | Intermediate |
| Idioma | English |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
- Reading: Course Syllabus
- Reading: Help us learn more about you!
- Vídeo: Networks: Definition and Why We Study Them
- Vídeo: Network Definition and Vocabulary
- Vídeo: Node and Edge Attributes
- Vídeo: Bipartite Graphs
- Reading: Notice for Auditing Learners: Assignment Submission
- Notebook: Loading Graphs in NetworkX
- Vídeo: TA Demonstration: Loading Graphs in NetworkX
- Notebook: Assignment 1
- Vídeo: Clustering Coefficient
- Vídeo: Distance Measures
- Vídeo: Connected Components
- Vídeo: Network Robustness
- Notebook: Simple Network Visualizations in NetworkX
- Vídeo: TA Demonstration: Simple Network Visualizations in NetworkX
- Notebook: Assignment 2
- Vídeo: Degree and Closeness Centrality
- Vídeo: Betweenness Centrality
- Vídeo: Basic Page Rank
- Vídeo: Scaled Page Rank
- Vídeo: Hubs and Authorities
- Vídeo: Centrality Examples
- Discussion Prompt: PageRank and Centrality in a real-life network
- Notebook: Assignment 3
- Vídeo: Preferential Attachment Model
- Reading: Power Laws and Rich-Get-Richer Phenomena (Optional)
- Vídeo: Small World Networks
- Vídeo: Link Prediction
- Notebook: Extracting Features from Graphs
- Reading: The Small-World Phenomenon (Optional)
- Notebook: Assignment 4
- Reading: Post-Course Survey
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| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
Really enjoyed the mathematical component of this course. It was fun to see how you could connect the graph theoretical components to the machine learning concepts from earlier courses.
I learned many interesting new concepts in social network analysis and a bunch of new graph algorithms, which are rarely taught in the "traditional" algorithm course. Now I know how companies like Cambridge Analytics can use the Facebook's social network data to derive useful information. (It's actually quite easy.) A class like this is more important than ever. I just wish we could have more time to explore a few topics more deeply.
Very clear explanation.
JW
Well put together. Quizzes test on material covered and assignments expand on it. There is still challenge and rigor, but it comes from understanding the concepts, not ambiguity and lack of instruction. This is one of the best online courses I've taken.