Hello, my name's Mrs. Jones, and I'm really pleased you decided to join this lesson today.

In this lesson, we will look at the final stage of the PPDAC cycle and how to draw conclusions from the data through analysing visualisations and reporting the findings.

So let's get started.

Welcome to this lesson.

This lesson is called "Using Data to Make Recommendations" from the unit "Data Science." And by the end of this lesson, you'll be able to analyse visualisations to draw conclusions and report findings.

There are two key words to today's lesson.

Visualisation.

Visualisation is a variety of techniques used to illustrate a problem and/or its solution to make it easier to understand.

Conclusion.

Conclusion is a judgement or decision reached by reasoning or using data.

There are two sections to today's lesson.

The first is "analyse visualisations," and the second is "draw conclusions and report findings." So let's start with "analyse visualisations." A visualisation is used to illustrate a problem and/or its solution to make it easier to understand, and a visualisation can come in many different forms. You can see some examples here of different ways to illustrate the data gathered in different formats.

When you analyse a visualisation, you might spot patterns; notice trends; identify outliers, which is a value that is much higher or lower than the others; make conclusions based on what you see.

This is the school litter data visualisation.

What do you notice about the data in the visualisation? What does it make you wonder? We have the recyclable waste on the left, whether it is or not; so yes or no.

And along the bottom, we have the location: whether it is the art corridor, dining hall, English corridor, IT corridor, languages corridor, maths corridor, PE corridor, and technology corridor.

Just take a moment to look at that data.

And what does it make you wonder? What can we notice? Can you draw any conclusions from this visualisation? Does it warrant any further investigation? What other information would be useful? Izzy says, "I can't really make any conclusions from this data." I wonder if Sam and Alex have any questions.

Sam says, "I wonder why there is so much recyclable waste in the PE corridor?" Really good question.

Look at the data gathered there.

There is no recyclable waste, but there is five dots in the recyclable.

Alex says, "Why do the maths and technology corridors have less litter than the other corridors?" Again, comparing the data there, you can see that the maths and technology have less than the others.

Let's have a quick check.

What data has been added to this visualisation? Pause the video, look at that data, and see what data has been added.

And then we'll go through the answer.

Let's check your answer.

The visualisation now shows the distance between each piece of litter found and the nearest bin.

You can see there that now there is different shadings of green, which has a code key at the bottom that shows the lightest to the darkest.

Well done if you've got that correct.

What insights can you draw from this visualisation? Pause the video, look at the data, see what insights you can draw, and then we'll go through the answer.

Let's check your answer.

We can now see that although there is a lot of litter in the canteen, there are also a lot of bins, so adding more bins may not resolve the problem here.

In the PE and IT corridors, there were high amounts of litter, but these were the furthest away from bins, so perhaps more bins in these areas may resolve the problem.

Well done if you've got those correct.

Let's do an activity.

There are two parts.

The first, analyse your visualisations from your previous investigations.

However, if you don't have any data or visualisations, then you can use the visualisations provided in this lesson.

And the second part is what does the visualisation tell you about litter in your school? Pause the video, use your visualisations or the ones provided in this lesson, and then we'll go through the answers.

Let's check your answers.

So this visualisation is the one that we are looking at here in this example.

The visualisation shows me that in corridors where there is a bin close by, there is a lot less litter dropped.

In the IT, languages, and PE corridors, which are the furthest away from the bins, there is more litter.

The dining hall has the most amount of litter, but does have bins close by.

The high amount of recyclable waste in the dining hall may mean more recycling bins are needed.

Well done if you got that correct.

Let's move to the second part of today's lesson: draw conclusions and report findings.

Does your data tell you enough? Do you need any more data to help you answer your question or make any recommendations about litter in your school? If you need to collect more data or improve your visualisations, take some time to do that now.

Let's recap the steps to create a visualisation.

Go to oak.

link/codap-new, select "Launch CODAP," and then select "Open document." If you don't have a dataset, you can use the data linked to this lesson.

You can see on the screenshot there where it says "Open document," and that is what you're clicking on to open the dataset that you have or provided with this lesson.

The data should then show in CODAP, so you can start creating your visualisation.

And to create a graph, a visualisation, you click on graph to generate it.

To change the data you are looking at in visualisation, you can either click the column heading and drag it across to either the vertical axis, the Y-axis, or the horizontal axis, the X-axis.

This visualisation is showing the type of litter by location.

Let's have a quick check.

What is the final stage of the PPDAC cycle? Is it A, conclusions; B, test; C, create? Pause the video to consider your answer, and then we'll go through it.

Let's check your answer.

The answer was A, conclusions.

Well done if you got that correct.

Can you answer your question using your visualisation? What's the answer to your question? How does the data help prove the answer? Is the answer to your question reliable? Reliable data is data that you can trust because it is accurate.

What can you do with the results? Can you use this data to make a case for further action, or has it led to more questions that need to be answered? What recommendations could you make to your teacher or school leadership team about the conclusions you have drawn from your data? How could you present this data? Alex says, "I could suggest where extra bins should be placed." Sam says, "I could recommend the school buy more recycling bins." Izzy says, "I could present my findings in a report." These are all really good suggestions on how we could present our findings.

In data science, a report is a way to share your findings after you've collected, analysed, and visualised data.

A good report includes: what you investigated, and this was the question.

What's your question or your goal that you set out at the start of your investigation? The data you used.

Charts or graphs to show patterns and trends.

Conclusions based on the data.

And recommendations: what the data suggests you should do.

Let's have a quick check.

Which of the following is a feature of a good data report? Is it A, long paragraphs with no charts; B, opinions without any data; C, clear conclusions based on evidence? Pause the video to consider your answer, and then we'll check it.

Let's check your answer.

The answer was C, clear conclusions based on evidence.

Let's do an activity.

Write a couple of paragraphs to conclude and report your findings.

To help you write your conclusion, consider the following questions: What's the answer to your questions? How does the data help prove the answers? Are the answers reliable? What can we do with the results? Can we use this data to make a case for further action, or has it led to more questions that need to be answered? Pause the video to complete this activity, and then we'll go through an example answer.

Let's check your answer.

My original question was, "Are there enough bins in the school?" My data shows me that in corridors where there is a bin close by, there is a lot less litter dropped, whereas in corridors where bins are further away, there is more litter.

I recommend that more bins are placed in the IT, languages, and PE corridors, as these are the areas where a lot of litter was dropped.

The dining hall had the most amount of litter, but does have bins close by.

The high amount of recyclable waste in the dining hall may mean more recycling bins are needed.

I would recommend installing more recycling bins and then also promoting recycling to pupils, using things like posters.

Well done for completing that activity.

In summary, graphs and charts can be used to visualise data and make it easier to understand.

By looking at visualisations, patterns can be spotted, trends can be seen over time, and unusual data, outliers, can be identified and removed.

A report can be used to present conclusions and findings from data analysis.

Well done for completing this lesson: "Using Data to Make Recommendations.".