Hi.
Welcome to a lecture on data analysis and visualization from a more theoretical point of view.
Now, as you have seen through all the apps we have been building, we have been working with different
data sets for this app.
For example, we are working with weather data, temperature data, and so it becomes necessary for
you as a programmer to understand the data analysis and data visualization process, to know the steps
you have to take to be successful in completing a project which involves datasets.
So in this video, I want to talk about the four steps of the data analysis and visualization process.
So everything starts with the insight needs that That's the first step where our insight needs.
Well, let's take as an example the weather data application we have been working on in this app.
So we might want to get some insights from these data.
For example, we might want to see if there's been an increase in temperature during the last decades
for climate change purposes.
Or another example would be if a company and organization is at a loss, financial loss.
So they want to get the data themselves.
They have made the employee salaries and they want to get some insights.
So they want to understand why they are at a loss.
So they want to get some insights from this process.
So that's the first step of the data analysis process.
The next step is data acquisition.
So once you know what what you want to know from the data, you want to think about how you get the
data.
And there are two ways.
Either you collect the data from scratch so you can go to field to take surveys, you can measure,
etc. but most of the time you will find the data, right?
As we did in this app, I did find this data from an organization which have collected them before.
So these were existing temperature.
I didn't collect them by myself.
So that's a data acquisition process.
You want to make sure you get the best data possible for your needs.
The first step in a data analysis and visualization project is data analysis itself.
This consists of things like cleaning the data.
For example, we remove these minus nine, 999 values from our temperature data.
So before you present the data in graphs in the fourth step, you need to go through some steps in this
major step here, this analysis.
So that one was cleaning the data.
Secondly, we could do transformations of the data.
For example, we could have to convert the Celsius units to Fahrenheit if you intend to, to visualize
the data for an American audience.
For example, in this data analysis step, we might also do some statistical analysis, such as getting
the maximum of the values of a column or the minimum or the average value of a series of data, etc..
Plus, we might also look at temporal analysis.
For example, we looked at plotting the data over time, the temperature data in our weather temperature
project.
So these were the steps we went through during the the app that we have been building.
Now, you might ask, well, we did visualize some data there.
So why are you talking about data analysis when those steps involve some visualization?
Well, data analysis, it might also involve data visualization.
In fact, the data analysis phase is also referred to as the exploratory phase of the data, where you
as the data analyst, you get the data and you explore to find trends and relationships between different
columns in the data to to get the outliers.
So big data, which you don't need or incorrect data, those -999.
So this is an exploratory phase and might also involve data visualization.
Then when you go to the data visualization phase, this is also known as the explanatory phase where
you explain the results to your audience.
Your audience could be anyone on the Internet.
If you plan to publish this on the website.
Or it could be a scientific body which you might want to convince about something like climate change,
for example.
And so that's the fourth step.
So once you have understood the data in the data analysis phase, then you create the final graphs in
the data.
Visualization phase so that you can present as we spoke already, you can present these to the audience.
So these are the four steps of a data analysis and visualization project.
So I hope you found this lecture useful.
Now you know something more about this analysis, so thanks a lot for following this.
I'll see you later.