In the Dear Data Science course we covered analytics, visualization and modelling for relational (tabular) data. In this course we will approach network data as well as text mining.
watch The joy of stats by Hans Roslingwatch The power of networks by Manuel LimalistenNotes on linear algebra and matrix theorylearn Notes on graph theory [1 / 2]datacamp Network Science in R - A Tidy Approachwatch A visual history of human knowledgewatch Is time a network?glance Gallery: Gorgeous networks that help us understand the worldglance Visual complexityglance Networkismlearn Classes of networks
learn Technological networkslearn Social networkslearn Information networkslearn Biological networkslearn The 3 usual suspects: Degree, Closeness and Betweennesslearn Recursive centrality: Eigenvector, Katz, PageRank, and HITSdig PageRank: Standing on the shoulders of giantsdig Current-flow centralitieslearn A measure of power in networksdig A theory on power in networksdig Bargaining and power in networks. Chapter 12 in book Networks, crowds and marketslearn Similaritylearn Heterogeneitylearn Modularitylearn Spectral comunity detectionlearn Hierarchical clusteringlearn Other methodslearn Network modelslearn Components and resiliancemake Components and resilience in R [html / Rmd]watch The science of six degrees of separationread Chains, by Frigyes Karinthyread Erdös numberwatch The strength of weak ties learn Small-world networksmake Small-world networks in R [html / Rmd]learn Degree distributionmake Degree distribution in R [html, Rmd]read Power-law distributionlearn Transitivity and reciprocitylearn Assortative mixinglearn Strings with stringrdig Regular expressions and automata (Chapters 3 and 4)glanceCheatsheet. Regular expressionslearn The tidy text formatlearn Sentiment analysislearn tf-idflearn n-grams and correlationslearn Converting to and from non-tidy formatslearn Topic modellingmake Mining financial articlesmake The great library heistwatch What is a blockchain?learn Building a blockchain in Rdig Blockchain leashedglance InterPlanetary File Systemwatch IPFS Simply Explainedwatch IPFS and the Permanent WebYou will go through different tasks: learn, make, use, watch, glance, read, dig, listen. A legend is below:
learn: I teach, you listen (and hopefully learn).make: I give you an assignment, you make it during the class. We discuss the solutions during the next class.use: you use a software: download, install and run it for the first time. I give you a brief practical introduction to it.watch: We watch a video together. By and large, the video acts as a teaser, introducing the next topic in an informal and attractive way.glance: You give a brief and fast look at something, generally an informative website. I steer you towards the most important sections.read: You read a story, typically at home. We discuss it together during the following class.dig: You read a theoretical deepening of the current topic, normally at home. We talk about it during one of the next classes.listen: The class is given by an invited speaker, an expert in the field.As an academic professional, I can sign my class up for an entire semester for free via DataCamp for the Classroom. This has some benefits:
In a data story (or data challenge) you tell a story with data. Find a dataset, pose questions, and try to solve them using an analysis notebook in R. Follow your curiosity and be creative.
The exam consists of a written exam and a project with oral presentation.
The final mark will be a weighted average of the written and project parts of the exam. The weight of the project is \(\varphi^{-1}\), where \(\varphi\) is the golden ratio. Passable marks are between 18 and 30. Excellent projects will be awarded with a praise bonus from 1 to 3 points to be summed to the result of the weighted average. The final mark is rounded to the closest integer.