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be able to name the ethernet header fields
be able to explain the reason for the preamble
understand that the cable length has an influence to transfer rate
understand that speed of light is responsible for the connection between cable length and transfer rate
be able to calculate the maximum cable length for a given transfer rate
understand that the cable length is part of the Ethernet protocol
Understand that Ethernet is a non deterministic program
Be able to reconstruct a collision detection / resolve algorithm
Understand what happens if two computers send data at the same time 
get introduced to the concept of an IP-network
understand that networks can be interconnected
learn about the importance for decentralization as a design principle
realize that Local area networks can be fragmented via IP networks
understand that an IP network as an overlay network is an abstract thing that is not directly reflecting the hardware settings
understand the notion of an IPv4 address and its components like network and host part
understand why MAC addresses do not fulfill the requirements of IP addresses.
get introduced to the notion of an IP router / gateway
review the definition and concept of an IP network
understand that IP routing works on the level of IP networks
understand the concept of subnetting
review network classes and understand classless inter domain routing.
get a feeling for the IP header
get a better understanding of how the protocol works
understand which header fields are changed while routing
understand which problems of IP will be solved with the transmition control protocol
be aware of the limitations of the internet protocol and the internet architecture
get to know the end to end principle and in which only sender and receiver take care that communication works properly
understand the concept of a logical connection (virtual communication channel) between two computers on the internet
understand the importance of acknowledging received messages
be able to understand the process of establishing a tcp / connection
understand the concept of a socket in a TCP/IP package
understand that ports are part of the TCP header
be able to explain the difference between solicited and unsolicited TCP/IP traffic
understand how ports can be used for multiplexing internet connections
understand the concept of windowsize and sliding window
understand how flow control can prevent TCP connections to overload link layer protocols and slow networks
In this lesson you will learn some basics on the Question: Why Web Content needs structure and proper markup.
Understand the Domain Object Model and the DOM tree
Understand that HTML is just a special dialect of XML
Understand the relationship between HTML and XML
Be able to write simple HTML code having learned a few example elements of HTML (headings, paragraphs, lists, tables, links, anchors, emphasize, input fields; but also few dirty ones like italics, color,...)
See that HTML really is just another simple mark up and has nothing to do with programming
Be able to structure web Content using HTML and create pages following a specified structure.
Know about the style attribute and how to use it within HTML elements
Know already realize that there are some limits using the style attribute
be able to create websites that follow a certain style guide
See the problems with inline styles
Understand that a style sheet gives you freedom
being able to explain people why they should use style sheets
be able to name at least 2 important point why to use style sheets
know how the cascading process works
know the basic syntax of cascading stylesheets
know how to include a media file like a graphic to your webpage.
understand that images like jpg, gif and bitmaps are hard for machines to understand.
Know how to use a XML based format to create images that are easy to understand for machines and humans an can even make use of stylesheets.
Understand that metadata is necessary to communicate the semantics of content
See that using metadata for ranking in search results is a bad idea
get introduced to modern ways of publishing media data as RDFa
Understand the separation between content, structure, layout and meta data
Review HTML, CSS, XML, SVG and RDFa
Understand what makes a clean HTML markup ("separation of concerns") vs. unclean one ("mixing responsibilities"); and implications (better or worse maintenance, better or worse personalization, better or worse accessibility)
become aware of the possibilities to create dynamic content within a webserver
see that you don't have to implement a webserver to be able to serve dynamic content
understand some main issues like blocking I/O that one should keep in mind when doing server side programming
see how the web server is the entry point for web applications
whitelisting of input vs blacklisting and a method of preventing XSS
understand the basics of HTTP POST requests
become aware of security issues while transfering data to a web server
be able to create a simple web form in HTML
See how a POST request is handled in a Java Servlet
get to know the Request object
see how a data base query and more advanced technology can be included to a servlet
understand how javascript was supposed to support people to fill out web forms
understand the issues and disadvantages that arise with javascript
be aware of JavaScript APIs
know some of the standard JavaScript libraries
be able to understand the concept of Ajax requests.
The web as a software system.
The web as a collection of text documents.
The web as a graph of interlinked documents.
Even when choosing 1 point of view we have fundamentally different ways of modelling.
understand that only the model is described.
description of the model can be used for interpretation.
within the descriptive model one chooses measures to describe the object of study.
understand the notion of a modelling choice
be able to criticise a descriptive model and the modelling choices
Can be used to try to give a reason why something works.
need to be run more than once!
understand the notion of a modeling parameter
will be compared to the descriptive model of our object of study. 
Understand why we selected simple English Wikipedia as a toy example for modeling the web
Understand that a task already as simple as counting words includes modeling choices
Be familiar with the term “unique word token”
Know some basic tools to count words and documents
Be familiar with some basic statistical objects like Median, Mean, and Histograms
Should be able to relate a histogram to its cumulative distribution function
Understand the ongoing, cyclic process of research
Know what falsifiable means and why every research hypothesis needs to be falsifiable
Be able to formulate your own research hypothesis
Understand what a log-log plot is
Improve your skills in reading and interpreting diagrams
Know about the word rank / frequency plot
Should be able to transfer a histogram or curve into a cumulative distribution function
Get a feeling for interdisciplinary research
Know the Automated Readability Index
Have a strong sense of support for our research hypothesis
Be able to critically discuss the limits of our models
Be able to name some fundamental properties about how frequencies of words in texts are distributed
Be a little bit more cautious about visual impressions when looking at log-log plots
Know both formulations of Zipf’s law
Be able to do a coordinate transformation to change the scales of your plots
Understand in which scenario power functions appear as straight lines
Know in which scenarios exponential functions appear as straight lines
Be even more cautious about your visual impressions
Know the axioms for a distance measure and how they relate to norms.
Know at least two distance measures on functions spaces.
Understand why changing to the CDF makes sense when looking at distance between functions.
Understand the principle of the Kolomogorov-Smirnov test for fitting curves
Know how to transform a rank frequency diagram to a powerlaw plot.
Understand how powerlaw and pareto plots relate to each other.
Be able to explain why a pareto plot is just and inverted rank frequency diagram
Be able to transform the zipf coefficient to the powerlaw and pareto coefficient and vice versa.
Understand that building the CDF is basically like building the integral.
Know the properties of a similarity measure
Be able to relate similarity and distance measures
Know of two applications for modelling similarity
Understand how text documents can be modeled as sets
Know the Jaccard coefficient as a similarity measure on sets
Know a trick how to remember the formula
Be aware of the possible outcomes of the Jaccard index
As always be able to criticize your model
Be familiar with the the vector space model for text documents
Be aware of term frequency and (inverse) document frequency
Have reviewed the definitions of base and dimension
Realize that the angle between two vectors can be seen as a similarity measure
Be aware of a unigram Language Model
Know Laplacian (aka +1) smoothing
Know the query likelihood model
The Kullback Leibler Divergence
See how a similarity measure can be derived from Kullback Leibler Divergence
Understand that different modeling choices can produce very different results.
Have a feeling how you could statistically compare the differences of the models.
Know how you could extract keywords from documents with the tf-idf approach.
Try to argue which model you like best in a certain scenario.
Understand the principle methodology for building generative models
Remember why people are interested in generative models
Know why descriptive models are needed when evaluating a generative model
Be aware of one way to create a model for text generation
Understand how to sample values from an arbitrary probability distribution
Have seen yet another application of the cumulative distribution function
Understand that sampling from a distribution is just a coordinate transformation of the uniform distribution
See that it makes sense to compare statistics
Understand that comparing statistics is not a well defined task
Be aware of the fact that very different models could lead to the same statistics
See that one can always increase the model parameters
Know that increasing model parameters often yields a more accurate model
Be aware of the bigram and mixed models as examples for our generative processes
Be familiar with a set theoretic way of denoting a graph
Know at least 4 different types of graphs
Have practiced your abilities in reading and writing mathematical formulas
Be able to model web pages as a graph
Know that the authorship graph is bipartite
Know what kind of graph the graph of web pages is
(as always) be aware of the fact that modeling is done by making choices
Know terms like Size and (unique) volume
Be able to count the in and out degree of web pages
Have an idea what kind of law (in & out) degree distributions follow
Know that degree is not distributed in a fair way
Know that the Gini coefficient can be used to measure fairness
Understand the notion of a path in a (directed) graph
Know that shortest paths between nodes need not be unique
Understand the notion of a strongly connected component
Know about the diameter of a graph
Be aware of the bow tie structure of the Web
Be able to read and build an adjacency matrix of a graph
Know some basic matrix vector multiplications to generate some statistics out of the adjacency matrix
Understand what is encoded in the components of the k-th power of the Adjacency matrix of a graph
| Home | Part1: Foundations of the web | Part2: Emerging Web properties | Part3: Behavioral Models | Part4: Web & society | Participate | About the Web Science MOOC |
Course elements
- PART1: Week1: Ethernet · Internet Protocol · Week2: Transmission Control Protocol · Domain Name System · Week3: Internet vs world wide web · HTTP · Week4: Web Content · Dynamic Web Content
- PART2: Week5: How big is the Web? · Descriptive Web Models · Week6: Advanced Statistic Models · Modelling Similarity · Week7: Generative Modelling of the Web · Graph theoretic Web Modelling
- PART3: Week8 : Investigating Meme Spreading · Herding Behaviour · Week9: Online Advertising · User Modelling
- PART4: Week10 : Copyright · Net neutrality · Week11: Internet governance · Privacy
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Introduction (0th week)
New here[edit]follow this link to learn
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Web Science/Part1: Foundations of the web
Lessons
- understand the basic problems when communicating over a shared medium
- understand the origins of ethernet
[[Web_Science/Part1:_Foundations_of_the_web/Ethernet/|]]
no learning goals defined
Discussion
Web Science/Part2: Emerging Web Properties
Lessons
- The question will remain unanswered during the lesson and the entire course.
- question of size is underspecified because a measure is needed.
- measure depends heavily on the choice of how we model the web.
- We have not yet defined what we mean when we say World Wide Web.
