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Interactive modules.

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What is data?

We encounter data everyday in our lives. Data is a collection of facts, such as collection of numbers, words or description of things.

Aaron is 8 years old.

For example, the above sentence contains a fact about someone called Aaron, that he is 8 years old. This fact is a piece of data about Aaron.

What types of data are there?

Not all data is numeric. There are many types of data that explains facts differently.

Some pieces of data are descriptive, which are known as qualitative data.

Other pieces of data are numeric, which are known as quantitative data.

What is Qualitative data?

Qualitative data is descriptive information about something or someone. It is not numerical.

Aaron is a tall boy.

or

Aaron can run fast.

The above sentences give us two pieces of information about Aaron. They tell us that Aaron is tall and can run fast. These are facts about Aaron are not numerical. These are examples of qualitative data. This is descriptive data and can be expressed without numbers.

How can we use Qualitative data?

Qualitative data can be used to classify or categorise things.

Clothes come in many sizes, but mostly in - small, medium and large sizes.

We use the size to categorise clothing into groups. In the example above, it was into three groups - small, medium and large.

Here's another example of qualitative data to classify things.

Apple is a fruit. Broccoli is a vegetable.

In the example above, we have used fruit and vegetables to classify the apple and broccoli into two groups.

What is Quantitative data?

Quantitative data refers to numbers. It is numeric information about something or someone.

Aaron is 8 years old.

In the sentence above, Aaron's age is 8, this is a numeric information about Aaron. This is called quantitative data.

There are two types of quantitative data:

Discrete data
Continuous data

What is Discrete data?

Discrete data is countable, it involves whole numbers.

There are 11 football (soccer) players in each team, and 5,000 spectators in the stadium.

In the above example, the number of players and the number of people in the stadium is discrete data. Spectators and players are countable and can be presented in whole numbers. You cannot have 10.5 players per team or 4,555.5 spectators.

What is Continuous data?

Continuous data refers to the measure of something and can be any numbers in a certain range.

Aaron drank 1.5 litres of water today.

In the above example, the amount Aaron drank (1.5 litres) is continuous data. It is a measure of how much he has drank. Someone's or something's height, weight can also be a continuous data.

Why is data important?

Data is important because it helps us answer questions. It helps us find patterns and work out how some facts are related and affect things in our life.

Aaron is tall and can run fast.

The above example shows us how being tall is related to running fast.

Aaron's height is 1 meter and 20 centimeters.

The above data helps us answer the question - "What is Aaron's height?".

We have now seen how we use data and how it helps us answer questions. It is important that we understand the different types of data as not only do they help us answer questions, they also help us ask the right questions so we get the answers we need.

What have we learnt so far?

Well done! You have now completed your first module and on your way with your data literacy journey.
Here is a quick recap of what we have learnt so far:

What is data?

Different types of data.

Importance of data and how we use data.

Please press "Back to menu" below to go back and select the next module.

How to work with data

In the last section we learnt about data and how to spot different types of data. We looked at how:

data allows us to answer questions

helps us ask questions

Now we will look at how to collect data and how to work with data.

How can we collect data?

We create data every time we do something. Data can be collected in many ways. Not all data is useful to track, so it is important to understand what questions we are trying to answer.

For example, if we would like to answer the following:

What time does Aaron leave home to go to school?

..we can make notes of the time when he leaves home in the morning. The process of making notes to keep track of this information is a type of data gathering. We can use that data at a later date to answer that question.

Data can be collected in many ways. We can gather data about someone or something just by observation, this is the simplest way to collect data.

We can also collect data by conducting surveys and asking questions.

What are census and sample method?

Census and sample are methods of collecting data. Let's look at the following scenario:

We have been asked to gather information to find out how many student completed their homework at Aaron's school .

Using the census method, we will have to ask every student in the school to get our answer. A census gives us accurate answers, but it is hard to do as we have to ask everyone related to the question.

Using the sampling method, we will just select a group of students and ask them the questions to gather our data. Sampling results in a less accurate answer, but it is easier to do as we can ask a smaller group of students. Sampling is a quick way to understand some trends.

How can we explore data?

Data that we gather from surveys and observations are sometimes difficult to explore. To find the answers to our questions we need to be able to explore our collected data. There are many ways we can organise our data to help us explore and answer our questions. We can use charts to organise and visualise our data. Let's look at some of the common types of charts.

What is personal data?

When collecting data, we need to be aware of whether the data is personal and what it means.

For example:
What time does Aaron leave home?
or
How long does Aaron spend on his console each day?

Information about an individual person is called personal information. We should always be careful about collecting personal information and others collecting information about us.

Another example:
When we visit a website or play a game, information is gathered about us. This information is sometimes used by the game or website to provide us with a better service. However, be cautious of sharing personal data, whether it is someone who is collecting data about us or if we are collecting data about another individual.

What have we learnt so far?

Well done! You have now completed another module on your data literacy journey.
Here is a quick recap of what we have learnt in this module:

How to collect data

Different data collection methods

Charts that can help us explore and summarise data

Collection of personal data

Please press "Back to menu" below to go back and select the next module.

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Game Id	Player username	Player name	Played date & time	Score	Score	Game duration
{{Results.gameId}}	{{Results.playerUserName}}	{{Results.playerName}}	{{dayjs(Results.playedDateTime).format('YYYY-MM-DD HH:mm:ss')}}	{{Results.playerScore}}	{{secondsToTime(Results.gameDuration)}}

What is a table?

In the last section, we discussed how to explore data. In this section, we will learn about tables and how we can use them to explore the data you generated playing Space Invaders.

A table holds information in cells arranged in rows and columns. It is the simplest and most common way of looking at data. Throughout this journey we will explore data from your game.

The above table is a simple list of all the data available from your game, including the game id, date and time played, player name, game duration and game score.

What is a column?

A column is a series of cells or data arranged vertically on top of each other.

12
10
44

The above example is a column of numbers. In a table, columns are a range of cells that are stacked on top of each other.

In the table above, the highlighted section represents a column. Each column has a header, which tells us what data it holds. In this example, the highlighted column holds the names of the players.

What is a row?

A row is a horizontal line of data.

12, 10, 44, 23, 67

The example above is a row of numbers.

In the table above, the highlighted section represents a row. In this example, we have highlighted the game id {{sortedGameResults[1].gameId}} and all of the data related to that game including the player's name {{sortedGameResults[1].playerName}}, each row in this table holds data for a single game.

How to organise a table?

Tables are great for showing line by line detail of our collective data. It is hard to learn anything from the table in this view, as it is a list of words and numbers.

However, tables have some features which helps us to organise this data.

For example, we cannot identify the top score from this current view of our table but we can sort this data to help us.

Exercise: Can you click on the highlighted "Score" column header on the table above? Observe how the score is sorted from lowest to highest and then highest to lowest as you click multiple times.

Sorting is a great way to organise the data in a table. This allows us to find out the top score easily. We can sort a table by any column. Can you try and find out the longest and the shortest game from the table above using sorting?

How to search or filter data?

When we have lots of data in a table, it is hard to find something specific. We can use other methods to help us find what we are looking for. In this example we will use search and filtering to help us.

Exercise: Can you search the game data above using the search box, and search for your name. Observe how the data is filtering to show us only the rows that matches your name.

This is an example of how we can search or filter data in a table. You can combine search and sorting features to get to an insight quickly.

Exercise: Using the table above, can you find your highest and lowest score?

Hint: try using search and sorting combined to find the answer.

What have we learnt?

Well done! You have now completed another module on your data literacy journey.
Here is a quick recap of what we have learnt in this module:

What is a table?

What are rows and columns?

How to sort a table.

How to use search and sort to find an answer.

Please press "Back to menu" below to go back and select the next module.

Opps, it looks like you have not played enough games! Please play 5 or more times before you can start this interactive learning.

Highest to lowest score Lowest to highest score By game played

Bar charts

In the last section we learnt about tables and how they help us understand data.

We will now learn about how data can be presented graphically, where the data is transformed into different shapes and sizes to help us understand and interpret it quicker.

We will begin this journey with the bar charts!

Bar charts present data in graphical form, by displaying bars of different heights. The larger the value, the large the bar, thus making it easier to identify and compare the values quicker.

What makes up a bar chart?

Bar charts are typically made up of two axes and a series of rectangular bars of different heights. Click on the below button to start building a bar chart using your data!

For this exercise, we will only look at the first five games that you have played.

Using colours in bar charts

Sometimes bar charts are difficult to interpret, but we can make some tweaks to help us. For example, we can make each bar a different colour, therefore helping us identify the independent value displayed.

In our example above, the X axis is the game number, so now each game is a different colour helping you identify the different games played.

More about colours

We can also use colours to help us identify key characteristics within our data.

In our example above, we have used different colours to highlight the highest and lowest scores, with the red bar highlighting the lowest score, while the light green bar highlights the highest score.

Organising a bar chart

We can use the characteristics of our data to help us organise a bar chart.

Similar to what we learnt in the table module, we can sort the contents of a bar chart to help us interpret and understand the data better.

In our example above, you can sort the bar chart by game number, or from characteristics of the values, i.e. from highest to lowest or from lowest to highest.

Exercise: Try the sort options and see which one you find most useful with this dataset.

Hint: see the explanation when you try different options.

Horizontal bar chart

So far we have only see bar charts with vertical bars. These are also referred to as column charts.

Bar charts can also be used to display the bars horizontally, where you swap the X and Y axes and the bar are typically displayed from left to right. The value are still represented by different sized bars but they are presented horizontally rather than vertically.

Exercise: Exercise: play with the sorting option above and see whether you prefer vertical or horizontal bar charts.

Hint: see the explanation when you try different options.

What have we learnt?

Well done! You have now completed another module on your data literacy journey.
Here is a quick recap of what we have learnt in this module:

What a bar chart is

What the ingredients are that make a bar chart

How colours can help understand a bar chart

How sorting can help you organise a bar chart

How bar charts can be horizontal and vertical

Please press "Back to menu" below to go back and select the next module.

Opps, it looks like you have not played enough games! Please play 5 or more times before you can start this interactive learning.

Line charts

Line charts present data in graphical form, by displaying dots or markers connected by lines over a series, usually over intervals of time, allowing us to easily spot the changes that takes place over time and spot the trends in our data.

What makes up a line chart?

Line charts are typically made up of two axes and series of dots/markers connected by a straight line. Click on the below button to start building a line chart using your data!

For this exercise, we will only look at the first five games you have played.

More than one series of data

You can have more than one series of data or lines in your line chart. Adding more series of data/lines to the chart allows us to compare the trends between them.

We can add additional player as a series of lines in our line chart, showing and comparing their trends.

In our example above, we added a second series for another player named "Super Mario" to compare Mario's scores with your own.

Adding colours

When you have more than one series of data in your line chart, it becomes hard to tell the difference between the lines. We can use colours to help us separate them.

In our example above, we have used different colours to highlight the two series of lines.

We have used black to highlight Mario's scores and green for your scores.

Show legends

Now that we have multiple coloured lines in our line chart, we need to somehow show which lines belong to which series.

We can use legends to show additional information relating to the contents of the chart.

In our example above, you can see the legend for the line chart is shown at the bottom of the chart and it's showing us which colour line belongs to which player.

Exercise: Sometimes you can use the legend to interact with a line chart.

Hint: why not click on the legends to see what happens to the chart?

What have we learnt?

Well done! You have now completed another module on your data literacy journey.
Here is a quick recap of what we have learnt in this module:

What a line chart is

What the ingredients are that make a line chart

How connected lines can help understand trends

How colours can help us organise a line chart with more than one series of data

How a legend helps understand the content of a chart

Please press "Back to menu" below to go back and select the next module.

Welcome to the Kids in Data workshop

The next few screens will quickly take you through some of the key features of the workshop before you start. Please press "Next" to start your tour.

You can create a new user or {{demoWorkshopFlag == 1 ? "use one of our existing demo users":"search and select an existing user (if you have participated in the workshop before)"}} to join the workshop. Just select an option and follow the on screen instructions, it's that easy!

Once you have picked/created your user, you will gain access to the workshop. In workshop space, you can -

See trophies and achievements
Play games
View the league table
Learn using the data literacy modules
Create your own data stories using our Story Studio

Please note: Some of the interactive learning modules require you to play minimum of 5 games, this is to ensure we have enough data for the modules.

When you register and activate your workshop, you gain access to the workshop admin panel. From there you can access:

Your workshop details, including REST API details.
View or download your workshop data.
Track user progress such as module completion and views.
Remove inactive/unwanted users.
Reset your workshop, allows you to reuse the workshop as many times as you prefer.

You are all ready to start your data literacy journey.

Have fun!

What is your story going to be about?

Space invaders

How would you like to tell your story?

Play more games!

Sorry, it looks like you need to play more games! You have played total of {{totalGamesPlayed}} {{totalGamesPlayed>1 ? 'games':'game'}}. You will need to play {{5-totalGamesPlayed}} more {{5-totalGamesPlayed>1 ? 'games':'game'}} before you can create your first data story.

Happy gaming!

What is the name of your story?

Preparing a new canvas for your story...

Edit my story..

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What is a table?

What is a column?

What is a row?

How to organise a table?

How to search or filter data?

What have we learnt?

Bar charts

What makes up a bar chart?

Using colours in bar charts

More about colours

Organising a bar chart

Horizontal bar chart

What have we learnt?

Line charts

What makes up a line chart?

More than one series of data

Adding colours

Show legends

What have we learnt?