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Applied Social Network Analysis in Python

This course is a part of Applied Data Science with Python, a 5-course Specialization series from 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. 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.
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University of Michigan

Rating 4.4 based on 94 ratings
Length 5 weeks
Starts Apr 22 (4 days ago)
Cost $79
From University of Michigan via Coursera
Instructors Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero, V. G. Vinod Vydiswaran
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

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What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

social network analysis in 7 reviews

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.

The problem is that the course is not called "Applied Graph Analysis in Python" but "Applied Social Network Analysis in Python".

very clear logic, and will always wrap up at the end of the class Very comprehensive course for introduction of social network analysis.

4) More time should have been spent on prediction and other advanced topics, at least another week to bring the "Applied" into "Applied Social Network Analysis.

very good introductory course for social network analysis using Python.

The instructor was very clear in what he presented, and gave a good overview of Social Network Analysis.

老师讲解的非常好 , 逻辑清楚,条理明晰。建议编程作业稍微有点难度。所以扣掉一颗星。 希望越来越好。 Nice overview of general graph theory, and some useful exercises on how it can be applied for social network analysis.

machine learning in 6 reviews

Weekly quizzes check your understanding of the concepts and the assignments let you apply the material on practical examples, from basic network properties to link prediction using machine learning.

It was fun to see how you could connect the graph theoretical components to the machine learning concepts from earlier courses.

Regarding the assignments, they are divided into two groups: trivial tasks that are solved with a single line of code extracted from the NetworkX manual and more complex tasks related to Machine Learning that do not involve putting into practice the concepts of this course but those of the third course of the specialization.

The last assignment required machine learning, which was not taught in this course.

Thanks!The cherry on top was to apply machine learning techniques to predict how the net evolves.

Final programming assignment was very easy, you can re-use the code written in the final assignment of Machine Learning course in this specialisation (but that does not mean it's a bad thing).

very clear in 6 reviews

Very clear explanation.

Very clear concepts.

Very clear explanations and materials.

The lecturer managed to explain difficult concepts very clearly through its excellent slides and words.

other courses in 5 reviews

Great lecturer, comprehensive material and unlike other courses in this specialisation, actually prepares you well for the assignments and quizzes.

I think my appreciation for this course is intensified by the irritation with other courses.

The assignments were not as difficult as in other courses of the specialization, and very helpful to understand the contents.

This course was very interesting and well taught, finally after all other courses I have managed to complete the assignments for this one in the recommended amount of time.

data science in 4 reviews

Well taught and in a field which is not covered by many other data science curricula The lectures are good.

I know what is expected and can focus on doing data science.

As a stand alone course I would give it four stars, but it gets three because it's required for the data science specialization.

Going into this course, I was really disappointed that I had to take this course for a Data Science Specialization because at a skin-deep level it seemed very irrelevant, and frankly I was at that state of mind until week 4 of this course.

programming assignment in 4 reviews

The programming assignments are actually fun to solve - the instructions are clear and well-formulated.

Given that, the various skills I learned in the other courses did come together in the final programming assignment.

However, there were several issues with the AutoGrader that did not get fixed until late in the course and the PowerPoint slides for the lectures were also very late in getting posted (they were not available for most of the programming assignments).

One of the most interesting topics was a very quick overview of plotting for network diagrams, but this was never followed up with a programming assignment or other aspects to give us practice using the techniques described.

This course would benefit from 2-4 additional weeks of material and more programming assignments, IMO.

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Analysis Coordinator $65k

discharge analysis $68k

Operation Analysis $73k

Security Analysis $84k

Data Scientist (Social Network Analysis) $84k

Process Analysis $84k

IT Analysis $95k

Analysis $95k

Analysis Engineer 1 $103k

Business Analysis/Business Systems Analysis $120k

Senior Network Analysis Manager $148k

Vice Assistant President Network Analysis $190k

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Coursera

&

University of Michigan

Rating 4.4 based on 94 ratings
Length 5 weeks
Starts Apr 22 (4 days ago)
Cost $79
From University of Michigan via Coursera
Instructors Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero, V. G. Vinod Vydiswaran
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

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