Challenge Your Intelligence

brain scan

I watched an Olivier Roland’s video  and I learned good stuff :

Have you ever heard that : « If you’re smart and you’re always surrounded by idiots, you’ll always be right ».

Sincerely, there is a deep truth in it. This corresponds to this quote « If you’re the smartest person in the room, you’re in the wrong room ». We are human beings and it’s easy to follow your ego. It’s easy to stay in an environment where we’re always right because subconsciously we want to be around people who adulate us or put us on a pedestal.

When we look around, we see people who are successful and who stay in a bubble where they’re surrounded themselves with people who will always agree with them. It’s in our human nature not to like the contradiction of what we are and the values we defend. All of this is logical, we have success, we developed values and a life approach that we tested in the field and we don’t know others ways.

It doesn’t mean that in any case it works and that doesn’t mean that there are not other ways to succeed.

A group of intelligent people

 

mastermind group

When was the last time you were in a room where you felt that you weren’t the smartest person in the room and that you learned new things ?

What is interesting when yo participate in a mastermind group is that you’re surrounded by brilliant people who don’t hesitate to say when there are obvious things in you and you don’t see them. It allows you to change your point of view and forces you to face your own limiting beliefs.

Often during a mastermind group, there is a « hot seat ». A « hot seat » is when you share a specific problem and a specific question to the group. What is surprising is that we always see the same pattern that repeats itself. There is a person who shares a problem and a question and we see a lot of obvious things about the limiting beliefs of this person, how much that person has locked himself/herself into a part of the problem. From the outside, we see that there are other ways to attack the problem and we also see obvious solutions.

What’s interesting is that it seems extremely obvious for a person who is in the group to help find the solution but when you share your problem and your question, the group offers you things that you have never seen by yourself.

We all need a moment to take out our trash. We all need to share with a group what doesn’t work and what problems we have. Whenever we do that, we realize that we all have blind spots. Things that seem obvious to everyone but seem hard to see. It’s like seeing the flaws of another person but we don’t see ourselves our flaws.

Here’s an example, I’m ticklish. I can try to tickle myself, I can’t laugh but if someone else tickles me, I laugh immediately.

I can’t be at the window and see me walking in the street at the same time. So if you’re an entrepreneur and you feel isolated, break this situation. Go to events, try to meet other entrepreneurs because it will help you a lot.

You can’t know how many mistakes you make each day without realizing it. Only someone else can show you, your mistakes if you allow him/her to do so in a caring way. It’s not a matter of showing mistakes by accusing but in a kindly way to help you move forward. If you can participate in a mastermind group to be in a collective spirit of brilliant people, do it. It’s a great place to no longer be the smartest person in the room and learn new things to improve yourself.

Share this article if you think it can help someone you know. Thank you.

-Steph

A Pratical Tip To Validate Your Approach

data science tableau check

I have just enrolled in a Data Science course on Udemy  and I learned good stuff.

How was the A/B test « Number Of Product » ? Easy or difficult ?

Here is the result I found.

data science tableau check bar chart

I think you noticed there was something bizarre. There is an anomaly. We imagine that the more the client has products, the more the client is satisfied with the bank so this type of clients should stay in the bank.

In the first 2 bars we can see that a client who has 1 product is more likely to leave the bank than a client who has 2 products. But when a client has 3 or 4 products, we see a huge rate of clients leaving the bank.

Look, there is a little bizarre detail. In the 2nd bar, we can’t see the « Exited » label. This is because there is no place in the orange part to put the text. To make it simpler, we’ll remove the label « Exited ». Drag and drop on the « Exited » text label to the outside.

data science tableau check bar chart

data science tableau check bar chart

Perfect, we can read the percentages. On the 1st bar, we can see that among the client that have 1 products, 28% left the bank. On the 2nd bar, we can see that among clients who have 2 products, 8% left the bank. This show us that clients who have 1 products are more likely to leave the bank than clients with 2 products.

And for the next bars, we observe an anomaly. On the 3rd bar, we can see that among the clients who have 3 products, 83% left the bank. On the 4th bar, we can see that among clients who have 4 products, 100% left the bank. We clearly see that there is a problem and we need to do a deeper analysis to understand what is going on .

As a Data Scientist, we need to explain what happens in bars 3 and 4. Usually when a client has 3 or 4 banking products, that means he/she is satisfied and is loyal to the bank. But in our case, it’s the opposite because there is a high rate of client who left the bank. This is the time to do deeper analysis.

The first thing to analyze is the quality of the data. There is a very big anomaly and it may be because there is something insignificant in our data that disturbs the statistics. For example, it’s possible that when the bank selected these clients in this sample, there were very few clients with 4 products and all those clients with 4 products left the bank. Sometimes chance can create anomalies and you have to play attention to these effects of chance because they don’t seem important but they can create false interpretations.

To start, we will check the number of clients with 4 products.

In « Measure », move « Number Of Records » (which gives the number of observations) on « Label ».

data science tableau check bar chart

data science tableau check bar chart

We observe on the first 2 bars than many clients with 1 or 2 products selected for our sample. For clients with 3 or 4 products, we can see that there were fewer clients selected for our sample.

There are 220 clients with 3 products and 60 clients with 4 products. These small number of clients probably explain why we observe these anomalies.

In this sample of randomly selected clients, there are very few clients with 4 products and they all left the bank. In this situation, we can confirm that it’s a chance. When thing like that happen, you have to be very careful not to make conclusion too fast and make misinterpretations.

The conclusion is that a lot of clients have been selected for category 1 and 2. For category 3 and 4, there have been few clients selected so we can’t do accurate statistics. We need to do deeper analyze for these categories of clients with 3 and 4 products.

Now, let’s put the percentage back on the bar chart. Click on the « Back » button.

.

data science tableau check bar chart

Or do a click and drag of « SUM(Number of Record) » to outside.

data science tableau check bar chart

data science tableau check bar chart

We saw that there is an anomaly and what is interesting to do is to have a comment to remember to do a more in-depth analysis of columns 3 and 4.

Right-click between the bar chart’s title and the bars. Select « Annotate » then « Areas… ».

data science tableau check bar chart

A window appears. In this window, you write « Low observation in last 2 categories » and click on the « OK » button.

data science tableau check bar chart

data science tableau check bar chart

Click on the comment and move it on bars 3 and 4.

data science tableau check bar chart

data science tableau check bar chart

The next time you work on this bar chart, you will see this comment that will remind you to seriously analyze client who have 3 and 4 products.

Validate our approach

It’s time to show you how to validate an approach and how to validate the data. For this we will create a new A/B test.

Duplicate this worksheet with a right-click on the « NumberOfProducts » tab and select « Duplicate ».

data science tableau check bar chart

And rename the tab « Validation ».

data science tableau check bar chart

For this tab, we will erase the comment. Select the comment and press the « Delete » button on your keyboard.

data science tableau check bar chart

data science tableau check bar chart

Everything is ready, the idea is to find a variable that doesn’t affect our results. That is a variable that has no impact on a client’s decision to leave or stay in the bank.

Take for example, the variable « Customer Id ». Client’s identification number has no influence on the client’s decision to stay or leave the bank.

We’ll do an A/B test with the last digit of the « Customer Id » and we’ill check that there is the same clients proportion who leave the bank in the 10 categories of the last digit of the « Customer Id ». The 10 categories are the numbers 0,1,2,3,4,5,6,7,8,9.

Let’s g.To start, we will create the variable that contains the last digit of the « Customer Id ». To have this variable, we will create a « Calculated Field ».

Right-click on « Customer Id », select « Create » and click on « Calculated Field ».

data science tableau check bar chart

data science tableau check bar chart

Name the calculated field « LastDigitOfCustID ». In the text field, we use the « RIGHT » function with « Customer Id » in parenthesis to select the last character of the « Customer Id ». In our case, the last character of the « Customer Id » is the last digit.

Here is the code to write in the text field : Right ({Customer Id},1)

data science tableau check bar chart

data science tableau check bar chart

Oooops, you see there is a small mistake => The calculation contains errors.

There is an error in the formula because « Customer Id » is a number variable and the « RIGHT » function applies to a variable of type « STRING ».

To use the « RIGHT » function, we will convert « Customer Id » into a string. We will use the « STR » function with « Customer Id » in parenthesis.

Here is the code to write in the text field

And click on the « OK » button : Right (STR({Customer Id}),1).

data science tableau check bar chart

Now, you can see that our calculated field « LastDigitOfCustID » is in « Dimensions ».

Click on « LastDigitOfCustID » and move it on top of « NumOfProducts » in « Columns ».

data science tableau check bar chart

data science tableau check bar chart

Now we have a new bar chart and we see that for every last digit of the « Customer Id » there is about the same proportion of clients leaving the bank. All these proportions don’t correspond exactly to the average of 20% but these slight variations aren’t important.

Seeing this uniform distribution allows us to validate our data because these data are homogenous.

Conculsion

Here’s how you can check the homogeneity of your data. You take a variable that has no impact on the fact that a client leaves or stays in the bank. The example we did with the last digit of the « Customer Id » is excellent. We were able to verify that in each of the categories taken by this variable, if there was the same proportion of clients leaving the bank. As is the case, we can validate our data.

Imagine another result. When we do the test with the last digit of the « Customer Id », we observe that for one of the numbers, the rate of clients who left is really higher than the average. This shows us that there is a problem in our data because it indicates an anomaly.

You can find other ways to verify your data by using other « insignificant variables » to see if the distribution is homogeneous. But be careful when you select an « insignificant variable » because there may be traps.

Here is an example. If you create a variable that takes the first letter of the first name, the distribution will not be homogeneous. The reason is simple, there are many more people who have a name that starts with the letter « M » than with the letter « Y ».

Share this article if you think it can help someone you know. Thank you.

-Steph

Navigate In Tableau

front boat

I have just enrolled in a Data Science course on Udemy  and I learned good stuff.

We’ll explore Tableau’s tools

From the connection manager, we’ll go into the Tableau’s workspace.

Click on the « Sheet1 » tab at the bottom of the window.

data science tableau screenshot

Here is the Tableau’s workspace.

data science tableau screenshot

The 2 important elements of the workspace are « Data » on the left and the workspace on the right. It’s in the workspace that you’ll create tables and charts.

We’ll start with « Data » on the left.

data science tableau screenshot

« Data » divided into 2 zones : dimensions and measures.

The dimensions and measures are 2 different rules that will allow you to manipulate data.

Tableau sets the numerical values in « measures » and the categorical or quantitative variables in « dimension ». This is the Tableau’s settings by default.

There is also another way to explain « dimension » and « measures ». The « dimensions » are independent variables and the « measures » are dependent variables.

For exemple, « Units » is a measure, it’s the number of items sold per product. « Region » is a dimension, it’s the geographic region where the product sold. With 2 elements we can know how many items sold by region. This means that « Region » is an independent variable and « Units » is a dependent variable because it will be grouped by region.

But if you don’t like it, you can move the entities between dimension and measures and the opposite by click and drag.

In the menu bar, at the top, there is « File » where you can open and save file.

data science tableau screenshot

« Data » to connect to new source files.

data science tableau screenshot

« Worksheet » is the workspace to create analyzes

data science tableau screenshot

« Dashboard » is a combination of worksheet

data science tableau screenshot

« Story » is a combination of worksheet and dashboard

data science tableau screenshot

« Analysis » to specify how you want to do your analysis on your workspace

data science tableau screenshot

« Map » to add maps to the workspace

data science tableau screenshot

« Format » contains formatting options

data science tableau screenshot

Now, let’s study the workspace.

In the workspace, the main elements are « Columns » and « Rows ». This is where you decide which data goes in columns and rows in your worksheet.

You can also choose different format for these elements like colors, size, text level of detail and tooltips (useful tool optional).

data science tableau screenshot

Let’s do a test. Use data from « Region » (which is in « dimension »). Move « Region » with a click and drop to the center of your workspace. Now, « Region » is in the element « Rows ».

A table appears in your workspace.

data science tableau screenshot

You put a dimension in your workspace. Now put a measure in your workspace.

Uses the « Units » data. Move « Units » with a click and drop next to the « Region » column.

data science tableau screenshot

As you can see, Tableau automatically put « Region » in the « Rows » element and the « Units » data aggregated by region. In this way, you can tell how many items were sold by region.

Now, what you can do is to move « SUM(Units) » to the « Columns » element.

data science tableau screenshot data science tableau screenshot

And then, you have a « bar chart » to see how many items have been sold by region. You can enlarge the graphic with a click and drop.

Let’s look at the tools that are in « Show Me » zone.

data science tableau screenshot

Click on « Pie chart » to have this chart’s type.

data science tableau screenshot

Click on « Size » icon and drag from left to right you can increase the chart’s size.

data science tableau screenshot

In this chart, each region has a color and proportion of items sold by region.

You can also test the « bubble chart ». Tableau organizes the data automatically and everything and placed in the « Marks ».

data science tableau screenshot

You can test « Treemaps » chart. This is the same principle as « bubble chart » but it’s rectangles instead of circles.

data science tableau screenshot

As you can see in « Show Me », there are charts disabled. This is because you need some elelments in your data to be able to activate them.

For example for the « Area chart », you need « date »data to activate it.

Share this article if you think it can help someone you know. Thank you.

-Steph

Connect Tableau Public To A CSV File

tableau connect file csv data science

I have just enrolled in a Data Science course on Udemy  and I learned good stuff.

After downloading the file « OfficeSupplies.csv », you will open this file with Tableau. When you open Tableau, there is a « Connect » column :

tableau connect file csv data science

Click on « Text File » and open the file « OfficeSupplies.csv » :

tableau connect file csv data science

The connection manager appears for this source file :

tableau connect file csv data science

At the top left, there is « Connect » where the file is located and below it, there is « File » with 1 file. There is 1 file because you imported 1 file. I’ll show you later how to import several files of the same type.

tableau connect file csv data science

In the center, there is a window with the files you connected. And it’s possible to connect several files.

tableau connect file csv data science

For exemple, if you do a drag and drop here, Tableau will try to connect these 2 files.

tableau connect file csv data science

You can work with data comes from several differents files, different tables from different CSV files. We’ll see that later.

At the bottom, there is a preview of the file with columns and rows. Colums « Order Date », « Region », « Rep » and « Items » identified as data in text format. Columns « Units » et « Unit Price » identified as data in number format.

tableau connect file csv data science

Now you’re gonna go on the dashboard. To access it, click here.

tableau connect file csv data science

Here is the dashboard. 

tableau connect file csv data science

We will discuss the various function in more detail in the next section.

Now, I just want to show you that we have « Data » column with our source file.

tableau connect file csv data science

If you do a right-click and click on « View Data », you see the data as in the previous window.

tableau connect file csv data science

tableau connect file csv data science

You’ve seen, it’s simple to connect a source file to Tableau.

Wait, I’ll show you how you can connect more source files. Click here to return to the connection manager where you can connect several different files.

tableau connect file csv data science

Of if you want to stay on the dashboard, you can click on the top left on « Data » then « New Data Source ».

tableau connect file csv data science

Or click on this icon on the dashboard. Here are the files types you can connect to Tableau

tableau connect file csv data science

What is interesting with the « Statistical File » is that we can connect files type SAS, SPSS and R.

tableau connect file csv data science

We have access to different server with OData and others.

tableau connect file csv data science

It’s perfect, now you can connect a data source to Tableau and you’ll see later how to connect data faster.

Share this article if you think it can help someone you know. Thank you.

-Steph