All language subtitles for 001 A Practical Example What You Will Learn in This Course_en

Hello, and a very warm welcome to the data scientist course to many people, data science and

machine learning feel like unknown territory, something too abstract and complex to show you.

That isn't the case.

I would like to start by giving you a hands on example of what to expect from this course and

what you will be able to do on your own only a few hours into our training.

There are three key words that will come up a lot during the lecture data algorithm and insight.

Here we have data collected from the customers of a shop, 30 observations in total, each observation

represents a client who shared their customer satisfaction and brand loyalty.

Let's suppose the owners of the shop hired our consultancy firm to analyze customer behavior.

Dividing the shop's customer base into groups of individuals with similar traits is a great way to reduce complexity

and come up with ideas on how to serve these customer groups better.

And, of course, when their business in the long run.

To do that, we will have to apply machine learning.

Ready?

Here we go.

The data set that we've got is already loaded in the variable data.

A good preliminary step of most analyses is to visualize the data and examine it.

One of the better tools to do that is a scatterplot.

How many groups of points can you see here?

There are two groups standing out in data science, we would normally call these groups clusters

so two clusters can be identified instantly with no machine learning whatsoever.

One represents people with low loyalty and low satisfaction and the other one containing all the rest.

Our preliminary visual examination shows us that there are some insights we can draw for sure, but

let's take a more scientific approach.

Most of the times in data science, you would want to standardize your data.

Next, we will perform some unsupervised machine learning, more specifically cluster analysis using

the popular K means algorithm, we will identify four clusters.

The code, which we will examine in detail later on in the course, looks like the following.

And we are done.

I can now plot the data using the predicted clusters as colors of the new scatterplot.

We've got the same scatterplot, but with four clusters, our customers have been segmented from here,

we can distinguish four types of customers and actually name them.

The ones with the low satisfaction and low loyalty will be called alienated, those with high satisfaction

and high loyalty.

Our fans, those with low satisfaction and high loyalty are supporters and the last ones that are neutral

or disloyal but have a high satisfaction.

These are roamers.

Using just a few lines of code, we've reached a remarkable result we have segmented our customers in

four different groups.

We've applied an algorithm on our data to reach an insight.

Naturally, we must analyze what we see data science is about storytelling and making sense of numbers.

We have four groups, but only one of them is favorable.

The fans cluster analysis indicates the problem.

Some customers are dissatisfied, others are disloyal.

However, we must figure out how to solve the problem ourselves.

What are some ideas a data scientist and management will come up with?

It makes sense to focus our efforts to turn supporters into fans by improving their shopping experience.

Normally, we would have to dig deeper to find the drivers of dissatisfaction for these customers.

Maybe it is long queues or unfriendly staff or perhaps high prices.

Whatever the reason, we must take actionable steps to fix the issue and make our supporters happier.

Simultaneously, we can do something else.

We can turn the roamers into fans by increasing their brand loyalty.

Loyalty cards, gifts, personalized discounts, vouchers and raffles are different strategies used

to make such clients loyal in the long run.

Great.

Please bear in mind that in this exercise, we missed a few steps along the way, typing code step by

step, creating a program, analyzing a heat map and finding the optimal number of clusters.

However, these are all topics we will address later on in the course.

So let's begin acquiring the knowledge needed step by step until we are ready to gain insights from

larger data sets with various algorithms so we can turn all types of data into actionable insights.