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

Introduction (0th week)

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follow this link to learn

What is a mooc?
What are open educational resources?
How to use this platform?
What will this course be all about?

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Web Science/Part1: Foundations of the web

1. Ethernet

Communication over a shared Medium

understand the basic problems when communicating over a shared medium
understand the origins of ethernet

Ethernet header

be able to name the ethernet header fields

be able to explain the reason for the preamble

Minimum package length vs maximum cable length

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

Collision detection

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

Summary, further reading, homework

no learning goals defined

2. Internet Protocol

Motivation for IP

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

Classful IPv4 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

IP forwarding algorithm

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.

IP header

get a feeling for the IP header

get a better understanding of how the protocol works

understand which header fields are changed while routing

Summary, further reading, homework

no learning goals defined

3. Transmission Control Protocol

End to end principle and connection oriented communication

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

Three way handshake

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

Port numbers

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

Sliding window and flow control

understand the concept of windowsize and sliding window

understand how flow control can prevent TCP connections to overload link layer protocols and slow networks

Summary, further reading, homework

no learning goals defined

4. Domain Name System

Tree structure

test the mooc system

Terminology

no learning goals defined

Zones

no learning goals defined

Review some facts about DNS

no learning goals defined

Delegating DNS queries

no learning goals defined

DNS address resolution

no learning goals defined

Summary, further reading, homework

no learning goals defined

5. Internet vs World Wide Web

Summary of the internet architecture

no learning goals defined

Motivation and requirements for the World Wide Web

no learning goals defined

Design principles of the web

no learning goals defined

Uniform Resource Identifier

no learning goals defined

Summary, further reading, homework

no learning goals defined

6. Hypertext Transfer Protocol

Making HTTP requests

no learning goals defined

A simple web client

no learning goals defined

A simple web server

no learning goals defined

HTTP header

no learning goals defined

Content negotiation

no learning goals defined

Summary, further reading, homework

no learning goals defined

7. Web content

Problem setting for web content formats

In this lesson you will learn some basics on the Question: Why Web Content needs structure and proper markup.

Working with XML

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

HTML for web document structures

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.

Layout elements in HTML

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

Motivating separation of content and layout

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

Cascading 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

Media content

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.

Metadata

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

Summary, further reading, homework

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)

8. Dynamic Web Content

Basics of server side web programming

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

Forms and HTTP post request

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

Handling a post request in a Java Servlet

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

Client side JavaScript

understand how javascript was supposed to support people to fill out web forms

understand the issues and disadvantages that arise with javascript

Ajax and the XMLHttpRequest class

be aware of JavaScript APIs

know some of the standard JavaScript libraries

be able to understand the concept of Ajax requests.

[[File:Web Science MOOC Exercises Week 4.pdf|frameless|300x170px|link=Web_Science/Part1:_Foundations_of_the_web/Dynamic_Web_Content/Summary,_further_reading,_homework]]

Summary, further reading, homework

no learning goals defined

9.

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Web Science/Part2: Emerging Web Properties

1. How big is the World Wide Web

Problems with the question about the size of the Web

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.

3 ways to study the Web

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.

A simplistic descriptive model

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

An unrealistic, simplistic generative model

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.

Summary, further reading, homework

no learning goals defined

2. Simple statistical descriptive Models for the Web

Counting Words And Documents

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

Typical length of a document

Be familiar with some basic statistical objects like Median, Mean, and Histograms

Should be able to relate a histogram to its cumulative distribution function

How to formulate a research hypothesis

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

Number of words needed to understand most of Wikipedia

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

Linguists way of checking simplicity of text

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

3. Advanced statistical descriptive models for the Web

The Zipf law for text

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

Visually straight lines on log log plots

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

Fitting a curve on a log log plot

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

Zipf law powerlaw or pareto law.webm

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.

4. Modelling Similarity of Text

Know the properties of a similarity measure

Be able to relate similarity and distance measures

Know of two applications for modelling similarity

Jaccard Similarity for Sets

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

Cosine Similarity For Vectorspaces

Be familiar with 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

Probabilistic Similarity Measures Kullback Leibler Divergence

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

Comparing Results of Similarity Merasures

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.

5. Generative Models for the Web

Introduction to generative modelling.webm

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

Sampling from a probability distribution

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

Evaluating a generative model

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

Pittfalls when increasing the number of model parameters

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

6. Modeling the Web as a graph

Reviewing terms from graph theory

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

The standard web graph model

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

Descriptive statistics of the web graph

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

Topology of the web graph

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

Modelling-graphs-with-linear-algebra

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

Web Science

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Web Science/Part1: Foundations of the web

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Web Science

New here[edit | edit source]

Web Science/Part1: Foundations of the web

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Discussion

Web Science/Part2: Emerging Web Properties

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