Lesson video

Hello, thank you for joining me for your lesson today.

My name is Mrs. Conway, and I'll be guiding you through your learning today.

So our lesson today is on Data Analysis, and our outcome for today is I can analyse data to inform and justify my design decisions.

Our keywords then.

These are analyse, patterns, trends, and justification.

We'll go through all of these in more detail, as I talk you through them throughout the lesson.

So first of all, we're going to look at analysis of data.

So collecting data when designing is a really important part of the design process.

It enables designers to design in an informed manner, so that they have all the information, as much as possible, to be able to make decisions.

By doing this, they're gonna be able to make the best decisions for their design.

Once the data has been collected and presented, that data does then need to be analysed to make it useful.

To analyse something is to examine it in detail.

Let's just do a quick check for understanding on that.

Why do you need to examine your findings in detail when analysing data? Is it A, to quickly finalise the design without needing further input? Is it B, to make it useful for the designing process? Or is it C, to ensure the findings match the initial assumptions exactly? Pause the video here to take a moment to think about that if you'd like to.

Okay, and the answer was B.

So we analyse our findings and our data to make it useful as part of the design process, and it helps to make informed decisions for the designers.

Once you've actually collected your data in, you do then need to ask some questions about it, just to start the analysis.

The kind of questions that you need to ask are why did you actually do that research? What were you hoping to find out by carrying that out? And which of the results or data are actually relevant? Is it all still relevant, or actually is some of it, once you've gathered it in, you've looked at it again and thought, this isn't really going to help me with what I'm actually going to be doing, and that's fine.

And what will you now use the data for? And how are you going to interpret that data? After answering those questions, the next stage of analysing that data is then deciding really if it is still relevant to the project.

Just because you have collected research in it doesn't mean it still needs to be used in the project.

And this might be the typical time where you would look at it and go, you know what? I don't think I actually need this data anymore.

I thought I did originally, but now I've decided it isn't really relevant to what I'm going to be doing.

As I said before, that's absolutely fine.

That's part of the decisions in the design process that we make as we go along.

So here's Andeep, and he has explained, As part of my research into a product that would help visitors at a music festival, I found out how many people visit the most famous UK music festivals each year.

And I also discovered how much they paid for their ticket.

Half a little think, do you think the research that Andeep has carried out will be relevant to his possible product? Now, this research wouldn't necessarily help Andeep with his product.

He needs to know more about his user's experience of the festival rather than how many people visit other music festivals and how much a ticket typically costs.

Now, it may be useful, but at the moment I'm not quite seeing that as being the most useful information he could actually use and analyse.

Now when we're analysing the data, we should be looking for patterns and key points to identify in those findings.

When we're talking about patterns in data, patterns are repeating trends or connections that help us to understand the different information and how that different information is related to each other.

Now, trends are patterns in the data that change over a period of time.

If we just have a closer look at trends then, trends can be positive by increasing or negative by decreasing.

An example of a positive trend would be an increase in the number of people recycling over the past five years.

Now, remember what I said about trends.

Trends are things that are changing over a period of time.

And over this period, this is a past five years period of time.

A negative trend example here, a decrease in students' performance in Maths exams over the past three years.

That would be an example of a negative trend.

Again, you can see that this is over a period of time.

This has been an example of over three years.

It can be a period of time of just maybe weeks or a period of time, much smaller than that, such as hours.

It doesn't necessarily have to be years.

Right, let's just do a quick check for understanding on trends then.

Choose the example of the negative trend from below.

Those are A, the coffee shop sells 10 fewer cappuccinos each day, for one week? B, someone increases their step count by 100 each day, for a fortnight? Or C, the amount of cars using a car park stays the same each day, for a month.

Which one of those is an example of a negative trend? Pause the video here to take a moment to think about that if you'd like to.

Okay, the answer was A, a negative trend example is the coffee shop sells 10 fewer, so less so it's decreasing cappuccinos each day for one week.

Our one week is our period of time that that trend is showing over.

Right, trends and patterns can be found both in quantitative and also qualitative data.

So qualitative data uses words or language.

It's the kind of data that we can gather in, and it is usually gathered in the format of feedback, such as in words or language.

Quantitative data is usually our number kind of data.

So when we find out how many people visited the music festival, for example, that would be quantitative data because it uses numbers.

And this example just shows you and reminds you how to remember that.

So qualitative has got that L in it, which represents the word language.

And quantitative data has the word as the letter N it, that represents the word number.

So just giving you that easier way to remember which one is which kind of data.

So the easiest way to see patterns in quantitative data, remember quantitative data is the one with numbers, is by viewing the data in a visual format.

Visual formats to view quantitative data are things such as graphs and charts, just like the examples that I'm showing now below.

So you've got a bar chart there, which is showing the method of transportation to a music festival.

And I've also shown you there a pie chart also showing the method of transportation to a music festival.

Two different row ways to represent the same information, and both are visual formats and both are as just as useful as the other.

By actually viewing the data in this way, this enables us to see patterns and also kind of common themes much easier and much quicker.

Okay, let's just do a quick check for understanding on that.

What visual ways could you view quantitative data? A, by bar charts? B, pie charts? Or C, a flow diagram? If you'd like to take a moment to think about that, just pause the video here.

Okay, there were two possible answers there.

Bar charts or pie charts are great visual ways to view quantitative data to allow you to look for things such as patterns in the data.

It also helps you to analyse it that little bit easier by showing you those patterns.

So how do we actually analyse the data? So the first thing you need to do is look for patterns as we've already mentioned, and notice trends as we've discussed already, or relationships in the data.

So for example, do higher study hours match with higher test scores? Next, we're then gonna compare and contrast.

So we're gonna compare different parts of the data.

So for example, who studied the most? And who studied the least? And how did then their scores compare to that? Next we're gonna be able to calculate some simple metrics.

Now we can really only do this with quantitative data, but you can include things such as averages or percentages to give you more insight into that data and to help summarise it a little.

So for example, the average study time was three hours and the average score was 85%.

Then we're going to summarise our findings.

So basically, just bring it down to simple terms what that data shows us.

So for example, it looks like students who study more than four hours tend to score above 90%.

And then lastly, we're going to draw conclusions.

We're gonna connect the findings back to the original question you were actually trying to find out the answer for.

So in the example here.

So yes, studying longer does seem to lead to better grades.

Shocking.

Okay, so let's just do a quick check for understanding.

Which one of these is an example of calculate simple metrics? Which was one of the parts of the process of how we analyse data.

Is it A, the average study time was three hours and the average score was 85%? B, who studied the most and who studied the least? How did their scores compare? Or C, do higher study hours match with higher test scores? Pause the video here just to take a moment to think about that.

Okay, how did you get on? It was A, the average study time was three hours and the average score was 85%.

So just by including a nice little percentage there and also a little bit of information about the time, we just calculated some simple kind of metrics into it.

Okay, so this bar chart below shows which method of transportation to a music festival was the most popular.

And the data collector, sorry, the data collected can be analysed using the previous method we have just gone through.

So we're gonna look at patterns, compare and contrast, calculate simple metrics, summarise our findings, and then draw conclusions.

So let's just have a consideration of that, thinking about that bar chart.

So look for patterns.

When looking at the patterns for that bar chart, the three most popular methods of transport were all travelling by road.

Compare and contrast.

So considering car sharing would be cheaper than travelling in your own car, this is by far the least popular.

Compared with "own car", this was four times more popular, which is quite an interesting thing to pick out considering the cost consideration there.

Let's calculate some simple metrics.

The coach was the most popular method of transport with 36% of people asked choosing this particular method.

So then by summarising those findings, the most popular method of transport was by coach, which would also most likely be the cheapest way to travel.

Coaches actually can be very cheap to travel around on.

The least popular method was by car sharing.

And then let's draw some conclusions on that.

It seems that some people are led by the cost of the transport from the coach being the cheapest, but also the convenience of travelling in their own car or taxi.

Right, time for you guys to have a little bit of a go at this.

In the bar chart below, we've got some findings.

The music festival attendees were asked, what do they use to carry their belongings to the festival? The answers are shown in the bar chart and they're an example of quantitative data.

You are going to analyse that data by writing at least three bullet points of the key findings from the data, and you're going to use the method that we've gone through to help you.

Just to remind you, again, this is going to be to look for patterns, to compare and contrast, to calculate simple metrics, to summarise findings and to draw conclusions.

I'm asking for three bullet points, so you don't have to do all of those.

You can pick which ones will make the most sense and the ones that you find the easiest to do.

Or if you feel like challenging yourself, maybe pick the ones that you find the hardest to do.

Pause the video here to have a go at this.

Right, how did you get on analysing that data? So remember, you were writing three bullet points of the key findings from that data of how people carried their belongings to the music festival.

Here's an example for you.

So the most popular way to carry belongings to the festival was a rucksack, bit of calculating simple metrics here, 42% of people asked to use a rucksack.

Carrier bags were the least popular.

The second most popular way of carrying belongings was using a holdall which was 31% of people asked.

It looks as though people prefer methods that they can carry on their shoulders and are sturdy.

You may have some other points that you have pulled out.

These are just examples of some of the things you could have mentioned.

Okay, now let's look at actually justifying this evidence.

Data is often used to inform design decisions.

That's kind of the purpose of us gathering it.

This forms as part of the design process.

And designers gather data before designing to understand the needs, the preferences and behaviours of their target audience or target market.

They need to ensure that the design is functional, appealing, and effective in meeting its goals because they need to design something that the target audience are going to want to buy.

Lets just do a quick check for understanding on that.

Why do designers gather data as part of the design process? Is it A, to ensure the design is functional, appealing and effective in meeting its goals? B, to finalise the design before getting feedback from the target market? Or C, to understand the needs, preferences, and behaviours of their target audience or target market? Pause the video here just to take a moment to think about that.

Okay, and the answer is, it's both A and C.

So designers gather data to ensure the design is functional, appealing and effective in meeting its goals.

And to do that, they need to understand the needs and the preferences and behaviours of their target audience.

And collecting that data is perfect way to do that.

So once data is analysed, decisions then need to be made based upon this information.

But any decisions need to be justified and to justify something, you need to refer to the data and the evidence.

Justification is the reason or explanation for why something is done or believed to be right.

So you are basically explaining your decisions, but using the data and the evidence to back that decision up.

Let's just do a quick check for understanding.

Justification is A, a detail plan or method for completing a task? B, a summary of all the facts in a situation? Or C, a reason or explanation for why something is done or believed to be right? Pause the video here, just take a moment to think about that.

Okay, and the answer was, C, justification is a reason or explanation for why something is done or believed to be right.

So justification for your design decisions is done by referring to your evidence or your data, and it's done by breaking it down into design decision, the justification.

So you make your decision first and then you justify it.

Andeep can use the evidence he has gathered and presented in the following mind map to make a design decision for his designing, which will then be justified.

So this is the mind map.

The mind map has recorded qualitative data from an interview with a typical person from their target market.

A client is asked about how they carried their belongings to a music festival? So the client carried their belongings in carrier bags with no free hands for finding their ticket in their pocket.

The carrier bag eventually broke.

All of their belongings got wet, the carrier bags stuck into their hands and "The carrier bags were the only things I could find at home." So it included a couple of quotes from the client there as well.

So Andeep then now needs to make a design decision based on that, but he must also justify it as well.

And he has all of that information in front of him from his interview that he will use as evidence in making that decision and then justifying it.

So based on that man mind map, Andeep's design decision is my product must be waterproof to enable visitors to the festivals to carry their belongings without them getting wet.

His justification of that statement is, My client talked about their belongings getting wet when using a carrier bag.

So this is an important consideration to make a product that meets their need of keeping everything dry.

Now, it might seem like an obvious statement, but we must always use the evidence to make our decisions, and Andeep has that evidence and has used that to consider his justification.

So he's made a design decision followed by a justification which clearly comment upon or relates to the evidence that has been gathered.

Right, time for you to have it go at this.

So using both the interview data shown on the mind map and the survey data shown on the bar chart, I'd like you to identify some design decisions of your own and then justify these.

So to remind you, the mind map of the interview with the client included these considerations and points.

The bar chart included this particular information, so the bar chart considered how people actually their belongings and how many people chose the different methods.

So you're going to use those to make your own design decisions and justify them for a product considering how people can carry their belongings to a festival? Use the table below to make a design decision on the left hand side with a justification for that design decision on the right hand side.

Pause the video here to have a go at this task.

Okay, how did you get on? So I'm gonna give you some examples here, but you will have or may have different ones to me, that's absolutely fine.

Doesn't mean they are wrong, just means there are lots of different options you could have gone for.

So design decision here for me then.

My product will be carried using shoulder straps.

That is my design decision.

The justification for that, based on the data that we gathered from the mind map and also the bar chart was the rucksack was the most popular way of carrying their belongings.

Now that was data gathered from the bar chart.

The design decision next was my product will be waterproof.

The justification of that is my client struggled with their belongings getting wet.

Again, that information was found in the interview and the mind map.

My next design decision, my product will be strong enough to hold a weekend's worth of luggage.

Again, my justification for that came from the mind map with the client interview, which was my client struggled with their bag breaking.

And my last design decision then is my product will be able to be carried hands free.

And again, the justification for that once more came from the client interview.

My client said that they needed to be able to grab items such as their ticket.

Have little compare with yours compared to mine, and see if you think there's any others that you could have included as well.

Okay, so let's just summarise our lesson for today.

Once data has been collected and presented this data needs to be analysed to make it useful.

Just because the research has been collected, it doesn't mean it has to be used in the project.

Part of the first stage that you should do is just deciding whether or not that information is still relevant and still needed.

When analysing the data, you should be looking for patterns and key points that need to be identified in the findings.

Once data is analysed, decisions need to be made based upon this information.

And these are our design decisions.

And any design decisions that we make need to be justified by referring to the data and the evidence that we have collected.

Well done, you've done some tricky tasks today to work through this Data Analysis lesson.

Thank you for joining me, and I will see you soon.