## Case study: sensationalised data

Following on from the previous step, let’s look at a real example of where data has been sensationalised. A few years ago, new evidence (Gunter et al. 2017) suggested that …

## Bias and error in data collection

In a statistical sense, bias at the collection stage means that the data you have gathered is not representative of the group or activity you want to say something about. …

## BODMAS

In the previous step, we used mathematical expressions to transform raw data values. When looking at more complex mathematical expressions, we need to be crystal clear about exactly how the …

## Practise modifying code

The Python code we developed for comparing the values of two dice in the previous step can be modified to study a different kind of dice, called Grime Dice (invented …

## Learning a new language

What exactly is programming and how do we get started? Programming is a type of written language that allows us to send instructions to devices and computers to complete specific …

## Sense checking sensational statistics

Thinking about the ‘scientist’ part of the data scientist’s skill set, we might say that a good scientist is: objective, independent and curious. Scientists also, however, limit their confidence about …

## Avoiding ambiguity

Computers are not very clever, and they don’t do very clever things. They appear to be doing clever things, however, just by doing incredibly simple things very, very fast. How …

## A day in the life

What does it meant to be a data scientist? Watch the video to find out what being a data scientist involves – skills, tasks, knowledge and interests – and how …

## What if we need to store something other than a number?

A variable can store many types of different information, not just numbers. Knowing the different types of values, how we operate on them and how they interact with each other …

## Coming up with questions

We have seen the importance of asking questions in data science. Whether we have a particular question in mind or only a vague idea, questions need to be carefully formulated …

## Welcome back

Welcome to the second week in Data Science. Last week, we looked at the sorts of skills a data scientist uses, considered the importance of asking questions in data science, …

## How do we design good experiments?

While we may have access to lots of existing data, it does not necessarily mean that it will answer the questions we want to ask. Sometimes we will need to …