Applied Social Network Analysis in Python by University of Michigan on Coursera

This repository contains my work while completing the specialization created by University of Michigan on Coursera. The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

This repository contains notes, assignments, quizzes and code files from the "Applied Social Network Analysis in Python" course by University of Michigan, on Coursera.

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.

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Applied Social Network Analysis in Python Coursera

Applied Social Network Analysis in Python course from Coursera

Solutions of Applied Social Network Analysis in Python (Course on Coursera)

Applied Social Network Analysis in Python, This Project of Coursera and Future X course.

This repository has assignments of Coursera course, Applied Social Network Analysis in Python by University of Michigan.

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Random Graph Identification , Salary Prediction , New Connection Prediction Using Company Emails

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Applied Social Network Analysis in Python on Coursera

Coursera | Applied Social Network Analysis in Python (University of Michigan)

Work for Coursera's Applied Social Network Analysis in Python

Jupyter notebooks for Applied Social Network Analysis in Python (Coursera/UMich)

Applied Social Network Analysis in Python by University of Michigan Coursera

Assignments of Applied Social Network Analysis in Python University of Michigan, course 5 of Applied Social Network Analysis in Python Specialization at Coursera. Certificate at https://www.coursera.org/account/accomplishments/certificate/N4LK7TK8Y5QB

This repository contains the work done for the Coursera course: Applied Social Network Analysis in Python.

Repo for assignments and related work from the Applied Social Network Analysis in Python course on Coursera

Company Emails Network Analysis and new links prediction using SVMs and logistics regression models. Solution of the final assignment of Applied Social Network Analysis in Python, Coursera

This repository contains Ipython notebooks of assignments and the datasets used to complete them. Applied Social Network Analysis in Python is course 5 of 5 in the Applied Data Science with Python Specialization offered on coursera by the University of Michigan.

https://www.coursera.org/specializations/data-science-python The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.