Course - 5; Specialization: Applied Data Science with Python; University Of Michigan

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.

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

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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.

Convolutional Neural Networks (CNNs) are a special category of deep neural networks particularly suited to the analysis of visual imagery – and commonly applied in facial recognition, image classification, and medical imaging. In this session, we’ll demonstrate how to use CNNs to detect a person’s mood and emotions based on their facial expressions. We’ll use TensorFlow in Colab to build, train, and test our model & show you how to refine the process for best results. Prerequisites: —Basic knowledge of Python and machine learning concepts —Google Colab (https://colab.research.google.com/) GitHub Link: ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Social media: —Instagram: http://instagram.com/makesmartthings —Facebook: http://fb.com/makesmartthings —Twitter: http://twitter.com/makesmartthings #DataScience #NeuralNetworks #DeepLearning

🌐This repository contains solutions to the course Applied Social Network Analysis in Python by University of Michigan.

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.

Social media such as Facebook, Twitter, LinkedIn, and Instagram led us to realize the importance of social network analysis. Social network analysis has its applications in social media networks, friendship (trust) network, disease transmission, and sexual relationships. In business, social network analysis is applied to fraud detection, viral marketing, terrorism and crime prevention, promising technology identification, and key player identification. For example, world leading companies seek for key scientific leaders and factors using social network analysis. The number of its applications is getting larger. Social network analysis is a special case of network analysis. The first step for network analysis is to define what objects you want to deal with and what relationships between the objects you want to focus on. How you define the objects and the relationships between them will decide the definition of your network. An object becomes a node in a network. The centrality of the node is determined by its relations to the other nodes. Analyzing different types of centrality will give you an idea on the position of the node among the others. The weight of an edge between two nodes represents the strength of their relation. The communities clustered by a machine learning algorithm may give you some hint to the abstractions and the notions of the whole network. This course will help students think a system including a social system, knowledge system, and communication system in relational and structural way that may lead the students to come up with insights. Bibliometrics (i.e., scholarly communication) and informetrics will be covered. Besides, technology and market will be examined by network analysis. Sample codes for network analysis will be given to the students in Java, Python, and R. Sample dataset for exercising the codes will be also given.

Repo containing my course notes and tests.

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Introduction to Network Analysis

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Analysis of W2 topic on Applied social network in 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.

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

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Michigan Course On Coursera

This is the fifth course of 5 course specialisation in Applied Data Science in python.

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

Applied Data Science with Python - University of Michigan

By University of Michigan

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

Analyze networks using NetworkX library

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Applied Social Network Analysis in Python Coursera

Applied Social Network Analysis in Python course from Coursera

Material para la clase de análisis de redes aplicado del Club de Ciencia de Datos del Tecnológico de Monterrey. Este material está basado en el curso de la universidad de Michigan: "Applied Social Network Analysis in Python"