Python Sql Tableau Integrating Python Sql And Tableau

See the full picture: Learn how to combine the three most important tools in data science: Python, SQL, and Tableau

Last updated 2022-01-10 | 4.5

- How to use Python
- SQL
- and Tableau together
- Software integration
- Data preprocessing techniques

What you'll learn

How to use Python
SQL
and Tableau together
Software integration
Data preprocessing techniques
Apply machine learning
Create a module for later use of the ML model
Connect Python and SQL to transfer data from Jupyter to Workbench
Visualize data in Tableau
Analysis and interpretation of the exercise outputs in Jupyter and Tableau

* Requirements

* Basic coding skills in Python
* Basic knowledge of SQL
* Basic ability to use Tableau for data visualization

Description

Python, SQL, and Tableau are three of the most widely used tools in the world of data science.

Python is the leading programming language;

SQL is the most widely used means for communication with database systems;

Tableau is the preferred solution for data visualization;

To put it simply – SQL helps us store and manipulate the data we are working with, Python allows us to write code and perform calculations, and then Tableau enables beautiful data visualization. A well-thought-out integration stepping on these three pillars could save a business millions of dollars annually in terms of reporting personnel.

Therefore, it goes without saying that employers are looking for Python, SQL, and Tableau when posting Data Scientist and Business Intelligence Analyst job descriptions. Not only that, but they would want to find a candidate who knows how to use these three tools simultaneously. This is how recurring data analysis tasks can be automated.

So, in this course we will to teach you how to integrate Python, SQL, and Tableau. An essential skill that would give you an edge over other candidates. In fact, the best way to differentiate your job resume and get called for interviews is to acquire relevant skills other candidates lack. And because, we have prepared a topic that hasn’t been addressed elsewhere, you will be picking up a skill that truly has the potential to differentiate your profile.

Many people know how to write some code in Python.

Others use SQL and Tableau to a certain extent.

Very few, however, are able to see the full picture and integrate Python, SQL, and Tableau providing a holistic solution. In the near future, most businesses will automate their reporting and business analysis tasks by implementing the techniques you will see in this course. It would be invaluable for your future career at a corporation or as a consultant, if you end up being the person automating such tasks.

Our experience in one of the large global companies showed us that a consultant with these skills could charge a four-figure amount per hour. And the company was happy to pay that money because the end-product led to significant efficiencies in the long run.

The course starts off by introducing software integration as a concept. We will discuss some important terms such as servers, clients, requests, and responses. Moreover, you will learn about data connectivity, APIs, and endpoints.

Then, we will continue by introducing   the real-life example exercise the course is centered around – the ‘Absenteeism at Work’ dataset. The preprocessing part that follows will give you a taste of how BI and data science look like in real-life on the job situations. This is extremely important because a significant amount of a data scientist’s work consists in preprocessing, but many learning materials omit that

Then we would continue by applying some Machine Learning on our data. You will learn how to explore the problem at hand from a machine learning perspective, how to create targets, what kind of statistical preprocessing is necessary for this part of the exercise, how to train a Machine Learning model, and how to test it. A truly comprehensive ML exercise.

Connecting Python and SQL is not immediate. We have shown how that’s done in an entire section of the course. By the end of that section, you will be able to transfer data from Jupyter to Workbench.

And finally, as promised, Tableau will allow us to visualize the data we have been working with. We will prepare several insightful charts and will interpret the results together.

As you can see, this is a truly comprehensive data science exercise. There is no need to think twice. If you take this course now, you will acquire invaluable skills that will help you stand out from the rest of the candidates competing for a job.

Also, we are happy to offer a 30-day unconditional no-questions-asked-money-back-in-full guarantee that you will enjoy the course.

So, let’s do this! The only regret you will have is that you didn’t find this course sooner!

Who this course is for:

  • Intermediate and advanced students
  • Students eager to differentiate their resume
  • Individuals interested in a career in Business Intelligence and Data Science

Course content

10 sections • 99 lectures

What Does the Course Cover? Preview 03:55

Properties and Definitions: Data, Servers, Clients, Requests and Responses Preview 04:43

Properties and Definitions: Data, Servers, Clients, Requests and Responses

Which of the following is incorrect?

Properties and Definitions: Data Connectivity, APIs, and Endpoints Preview 07:05

Properties and Definitions: Data Connectivity, APIs, and Endpoints

Further Details on APIs Preview 08:05

Further Details on APIs

Text Files as Means of Communication Preview 04:20

Text Files as Means of Communication

Definitions and Applications Preview 05:25

Definitions and Applications

Setting Up the Environment - An Introduction (Do Not Skip, Please)! Preview 00:51

Why Python and why Jupyter? Preview 04:59

Why Python and why Jupyter?

Installing Anaconda Preview 06:49

The Jupyter Dashboard - Part 1 Preview 03:15

The Jupyter Dashboard - Part 2 Preview 06:15

Jupyter Shortcuts Preview 00:09

The Jupyter Dashboard

Installing sklearn Preview 01:16

Installing Packages - Exercise Preview 00:09

Installing Packages - Solution Preview 00:12

Up Ahead Preview 04:08

Real-Life Example: Absenteeism at Work Preview 02:48

Real-Life Example: The Dataset Preview 03:18

Real-Life Example: The Dataset

Important Notice Regarding Datasets Preview 00:37

What to Expect from the Next Couple of Sections Preview 01:39

Data Sets in Python Preview 03:23

Data at a Glance Preview 05:53

A Note on Our Usage of Terms with Multiple Meanings Preview 03:27

ARTICLE - A Brief Overview of Regression Analysis Preview 01:50

Picking the Appropriate Approach for the Task at Hand Preview 02:17

Removing Irrelevant Data Preview 06:27

EXERCISE - Removing Irrelevant Data Preview 00:25

SOLUTION - Removing Irrelevant Data Preview 00:01

Examining the Reasons for Absence Preview 05:04

Splitting a Column into Multiple Dummies Preview 08:37

EXERCISE - Splitting a Column into Multiple Dummies Preview 00:04

SOLUTION - Splitting a Column into Multiple Dummies Preview 00:00

ARTICLE - Dummy Variables: Reasoning Preview 01:32

Dummy Variables and Their Statistical Importance Preview 01:28

Grouping - Transforming Dummy Variables into Categorical Variables Preview 08:35

Concatenating Columns in Python Preview 04:35

EXERCISE - Concatenating Columns in Python Preview 00:04

SOLUTION - Concatenating Columns in Python Preview 00:01

Changing Column Order in Pandas DataFrame Preview 01:43

EXERCISE - Changing Column Order in Pandas DataFrame Preview 00:06

SOLUTION - Changing Column Order in Pandas DataFrame Preview 00:12

Implementing Checkpoints in Coding Preview 02:52

EXERCISE - Implementing Checkpoints in Coding Preview 00:04

SOLUTION - Implementing Checkpoint in Coding Preview 00:00

Exploring the Initial "Date" Column Preview 07:48

Using the "Date" Column to Extract the Appropriate Month Value Preview 07:00

Introducing "Day of the Week" Preview 03:36

EXERCISE - Removing Columns Preview 00:37

Further Analysis of the DataFrame: Next 5 Columns Preview 03:17

Further Analysis of the DaraFrame: "Education", "Children", "Pets" Preview 04:38

A Final Note on Preprocessing Preview 01:59

A Note on Exporting Your Data as a *.csv File Preview 00:26

Exploring the Problem from a Machine Learning Point of View Preview 03:20

Creating the Targets for the Logistic Regression Preview 06:32

Selecting the Inputs Preview 02:41

A Bit of Statistical Preprocessing Preview 03:26

Train-test Split of the Data Preview 06:12

Training the Model and Assessing its Accuracy Preview 05:39

Extracting the Intercept and Coefficients from a Logistic Regression Preview 05:16

Interpreting the Logistic Regression Coefficients Preview 06:14

Omitting the dummy variables from the Standardization Preview 04:12

Interpreting the Important Predictors Preview 05:10

Simplifying the Model (Backward Elimination) Preview 04:02

Testing the Machine Learning Model Preview 04:43

How to Save the Machine Learning Model and Prepare it for Future Deployment Preview 04:06

ARTICLE - More about 'pickling' Preview 01:13

EXERCISE - Saving the Model (and Scaler) Preview 00:13

Creating a Module for Later Use of the Model Preview 04:04

Installing MySQL Preview 09:56

Installing MySQL on macOS and Unix systems Preview 01:24

Setting Up a Connection Preview 02:34

Introduction to the MySQL Interface Preview 05:09

Are you sure you're all set? Preview 00:13

Implementing the 'absenteeism_module' - Part I Preview 03:50

Implementing the 'absenteeism_module' - Part II Preview 06:23

Creating a Database in MySQL Preview 06:37

Importing and Installing 'pymysql' Preview 02:44

Creating a Connection and Cursor Preview 02:54

EXERCISE - Create 'df_new_obs' Preview 00:10

Creating the 'predicted_outputs' table in MySQL Preview 04:52

Running an SQL SELECT Statement from Python Preview 03:04

Transferring Data from Jupyter to Workbench - Part I Preview 06:15

Transferring Data from Jupyter to Workbench - Part II Preview 06:35

Transferring Data from Jupyter to Workbench - Part III Preview 02:45

EXERCISE - Age vs Probability Preview 00:14

Analysis in Tableau: Age vs Probability Preview 08:49

EXERCISE - Reasons vs Probability Preview 00:14

Analysis in Tableau: Reasons vs Probability Preview 07:49

EXERCISE - Transportation Expense vs Probability Preview 00:22

Analysis in Tableau: Transportation Expense vs Probability Preview 06:00