All language subtitles for 1. A Practical Example What You Will Learn in This Course

Hello and a very warm welcome to the data scientist course too 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 shops 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 dataset 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 Kamins algorithm

we will identify for clusters.

The code which we'll 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 are 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 DeNiro Graham 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.