Curriculum

9 Sections
50 Lessons
Lifetime

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What is Data?
This course gives a short introduction in the world of data. If you are starting your journey into this wonderful land, this course is for you. The course covers basic concepts, different types of data and gives an introduction to machine readable data.
6
Finding Data
Don't know where to start looking for data? This section gives an introduction to data portals and different data sources. Finally we will walk you through how to use one sample data portal.
5
Sort and Filter: The basics of spreadsheets
So you found and downloaded your dataset? What now? Sort and Filter introduces a powerful tool to manage data: Spreadsheets. Based on Google Spreadsheets, this Course introduces the basic functions as well how to sort and filter data to find what you might be interested in…
8
Taming the Fierce Beast – The Math you need to start
Worried about all those numbers? This module will help you to refresh the basic math (don't be afraid it's mainly counting and adding).
10
'But what does it mean?': Analyzing data (spreadsheets continued)
Wonder how to make sense of the data? Basic Analysis will help you to understand what your data might mean. It also introduces spreadsheet formulas and helps you calculate more values out of what you already have.
4
From Data to Diagrams: An introduction to plots and charts
A picture says more than a thousand words – yeah but how do we turn a thousand words into a picture? This section will help you to understand basic data visualization and create them using Google spreadsheets. Visualizing data will help you to better understand the data you are handling.
7
Look Out!: Common Misconceptions and how to avoid them
Not everything is easily understood in dataland. In this course we give a short overview over common pitfalls when talking about data and how to avoid them. It can help you avoid the mistakes made as well as read other peoples claims more carefully. If you want to learn what to watch out for: join in!
9
Tell me a story: Working out what’s interesting in your data
Data alone is meaningless and often boring. To understand it better you will need context. In this section we will talk about how to publish the data we worked on throughout the basic track. We will talk about how to identify key points of your data to help you use data to make your voice heard.
6
Data provenance
To make a credible and sustainable data project, you need to document your steps right from the beginning. This course contains tips and tricks and tools for doing so.
3

Catching the thief – sensitivity and large numbers

Imagine, you are a shop owner and you just installed and electronic theft detection system. The system has a 99% accuracy of detecting theft. The alarm goes off, how likely is it, that the person who just passed is a thief?

It’s tempting to answer that there is a 99% chance that this person stole something. But actually, that isn’t necessarily the case.

In your store you’ll have honest customers and shoplifters. However, the honest customers outnumber the thiefs:: there are 10,000 honest customers and just 1 thief. If all of them pass in front of your alarm, the alarm will sound 101 times. 1% of the time, it will mistakenly identify a honest customer as a thief – so it will sound 100 times. 99% of the time, it will correctly recognise that a shoplifter is a shoplifter. So it will probably sound once when your thief does walk past. But of the 101 times it sounds, only 1 time will there actually be a shoplifter in your store. So the chance that a person is actually a thief when it sounds is just below 1% (0.99%, if you want to be picky).

Overestimating the probability if something is reported positive in such a scenario is called the base rate fallacy. This explains why airport searches and other methods of mass screening always will turn up lots of false positives.

Percent versus Percentage points change

Data Fundamentals

Curriculum

Catching the thief – sensitivity and large numbers

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