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When & Where
Date: 
Fri, March 20, 2020 - 1:00 PM to 4:00 PM
Location: 
Join via Zoom link below
Description
Type: 

Network Analysis Fundamentals, 3 part workshop series: 

  • Part 1: We will cover the Theory & Methods of Network Analysis, and introduce basic network building blocks using Python notebooks and Gephi (www.gephi.org). Topics covered will include introduction to data types for nodes and edges, and general network properties: connected components, clustering coefficients, degree centrality measurements and eigenvalues.
  • Part 2: We will be a deep dive into building networks using Python notebooks and Gephi for visualization. We will walk through two notebooks for building networks. The first will be for lists of entities in a spreadsheet / .CSV, and the second will build networks that model relationships across documents. I've developed these for discourse analysis and content analysis of corpuses of unstructured data / raw .TXT files (in .CSV). So bring your texts and play along!
  • Part 3: We will survey different types of networks (from UC Berkeley professor to Twitter to Marvel comics and even historical networks), which you can use interactively in a network sandbox. We will also include a group consultation session, so bring your own topics, data sets, and network ideas, or just hang out for a few hours.
Primary Tool: 
Python
Details
Training Host: 
D-lab Facilitator: 
Evan Muzzall
Format Detail: 
Remote, hands-on, interactive
Participant Technology Requirement: 
Laptop, Internet Connection, Zoom account

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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