In this tutorial we will cover the Data Science Workflow and…
We will use Python and pandas with our initial Data Science problem.
Did you know you check your phone 58 times per day?
Let’s say you are awake 16 hours – that is, you check your phone every 17 minutes during all your waking hours.
Estimates approximate that 66% of all smartphone users are addicted to their phones.
Does that surprise you?
How do we know that?
We live in a world where you know that the above statements are possibly not wild guesses, there is data to confirm them.
This tutorial is not about helping your phone addiction – it is about Data Science.
With a world full of data you can learn just about anything, make your own analysis and understand the aspects better. You can help make data driven decisions, to avoid blind guesses.
This is one reason to love Data Science.
The key to success in Data Science is understanding the problem. Get the right question.
What is the problem we try to solve? This will form the Data Science Problem.
Part of understanding the problem included to asses the situation – this will help you understand your context, your problem better.
In the end, it is all about defining the object of your Data Science research. What is the success criteria?
The key to a successful Data Science project is to understand the object and success criteria, this will guide you in your search to understand the research better.
Most get Data Science wrong!
At least, at first.
The assume – not to blame them – that Data Science is about knowing the most tools to solve the problem.
This series of tutorials will teach you something different.
The key to a successful Data Scientist is to understand the Data Science Workflow.
Looking at the above flow – you will realize, that most beginners only focus on a narrow aspect of it.
That is a big mistake – the real value is in step 5, where you use the insight to make measurable goals from data driven insights.
Let’s take an example of how a simple Data Science Workflow could be.
Now, while this looks straight forward – the can be many iterations back into a previous step. Even at step 5, you can consult the client and realize you need more data and start another iteration from step 1, to enrich the process again.
To get started with a simple project, we will explore the Portuguese high school student dataset from Kaggle.
It consists of features and targets.
The features are column data for each student. That is, each studen as a row in the dataset, and each row has data for each of the features.
The the target is what we want to predict from student data.
That is, given a row of features, can we predict the targets.
Here we will look at a smaller problem.
Yes – we need to explore the data and get ideas on how to help the students to get higher grades.
Now, let’s explore our Data Science Workflow.
We need to understand a bit about the context.
We have an idea about these things, not exact figures, but we have an idea about the age (high school students). This tells us what kind of activities we should propose. If it were kids in age 8-10 years, we should propose something different.
What is possible – well, your imagination must guide you with your rational mind. Also, what is the budget – we cannot propose ideas which are too expensive for a normal high school budget.
Let’s get started with some code, to get acquainted with the data.
import pandas as pd data = pd.read_csv('https://raw.githubusercontent.com/LearnPythonWithRune/DataScienceWithPython/main/files/student-mat.csv') print(len(data))
We will see it has 395 students in the dataset.
This will show the first 5 lines of the dataset as well as the columns. The columns contains the feature and targets.
This step is also about understand if data quality is as expected. We will learn a lot more about this later.
For now explore the data types of the columns.
This will print out the data types. We see some are integers (int64) others are objects (that is strings/text in this case).
school object sex object age int64 address object famsize object Pstatus object Medu int64 Fedu int64 Mjob object Fjob object reason object guardian object traveltime int64 studytime int64 failures int64 schoolsup object famsup object paid object activities object nursery object higher object internet object romantic object famrel int64 freetime int64 goout int64 Dalc int64 Walc int64 health int64 absences int64 G1 int64 G2 int64 G3 int64 dtype: object
And if there are any missing values.
The output below tells us (all the False values) that there is no missing data.
school False sex False age False address False famsize False Pstatus False Medu False Fedu False Mjob False Fjob False reason False guardian False traveltime False studytime False failures False schoolsup False famsup False paid False activities False nursery False higher False internet False romantic False famrel False freetime False goout False Dalc False Walc False health False absences False G1 False G2 False G3 False dtype: bool
We are interested to see what has impact on end grades (G3). We can use correlation for that.
For now, correlation is just a number saying if something is correlated or not.
A correlation number is between (including both) -1 and 1. If close to -1 or 1 (that is not close to 0), then it is correlated.
age -0.161579 Medu 0.217147 Fedu 0.152457 traveltime -0.117142 studytime 0.097820 failures -0.360415 famrel 0.051363 freetime 0.011307 goout -0.132791 Dalc -0.054660 Walc -0.051939 health -0.061335 absences 0.034247 G1 0.801468 G2 0.904868 G3 1.000000 Name: G3, dtype: float64
This shows us to learnings.
First of all, the grades G1, G2, and G3 are highly correlated, while almost non of the others are.
Second, it only considers the numeric features.
But how can we use non-numeric features you might ask.
Let’s consider the feature higher (wants to take higher education (binary: yes or no)).
higher no 6.800 yes 10.608 Name: G3, dtype: float64
This shows that this is a good indicator of whether a student gets good or bad grades. That is, if we assume the questions were asked in the beginning at high school, you can say that students answering no will get 6.8, while students answering yes till get 10.6 on average (grades are in range 0 – 20).
That is a big indicator.
But how many are in each group?
You can get that by.
higher no 20 yes 375 Name: G3, dtype: int64
Now, that is not many. But maybe this is good enough. Finding 20 students which we really can help improve grades.
Later we will learn more about standard deviation, but for now we leave our analysis at this.
This is about how to present our results. We have learned nothing visual yet, so we will keep it simple.
We cannot do much more than present the findings.
higher mean grades
I am sure you can make a nicer power point presentation than this.
Now this is where we need to find ideas. We have identified 20 students, now we need to find activities that the high school can use to improve.
This is where I will let it be your ideas.
How can you measure?
Well, one way is to collect the same data each year and see if the activities have impact.
Now, you can probably do better than I did. Hence, I encourage you to play around with the dataset and find better indicators to get ideas to awesome activities.
Want to learn more about Data Science to become a successful Data Scientist?
This is one lesson of a 15 part Expert Data Science Blueprint course with the following resources.
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