Python + SQL + Tableau: Integrating Python, SQL, and Tableau

Python + SQL + Tableau: Integrating Python, SQL, and Tableau

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 91 lectures (5h 14m) | 1.79 GB

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

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.

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
Table of Contents

Introduction
What Does the Course Cover

What is software integration
Properties and Definitions Data Servers Clients Requests and Responses
Properties and Definitions Data Connectivity APIs and Endpoints
Properties and Definitions Data Servers Clients Requests and Responses
Further Details on APIs
Properties and Definitions Data Connectivity APIs and Endpoints
Further Details on APIs
Text Files as Means of Communication
Definitions and Applications
Text Files as Means of Communication
Definitions and Applications

Setting up the working environment
Why Python and why Jupyter
Setting Up the Environment
The Jupyter Dashboard
Why Python and why Jupyter
Installing Anaconda
The Jupyter Dashboard
The Jupyter Dashboard
Jupyter Shortcuts
ShortcutsforJupyter
Installing sklearn
Installing Packages Exercise
Installing Packages Solution

Whats next in the course
RealLife Example The Dataset
Up Ahead
Real
Real
Important Notice Regarding Datasets

Preprocessing
What to Expect from the Next Couple of Sections
Data Sets in Python
Data at a Glance
A Note on Our Usage of Terms with Multiple Meanings
ARTICLE A Brief Overview of Regression Analysis
Picking the Appropriate Approach for the Task at Hand
Removing Irrelevant Data
EXERCISE Removing Irrelevant Data
SOLUTION Removing Irrelevant Data
Examining the Reasons for Absence
Splitting a Column into Multiple Dummies
EXERCISE Splitting a Column into Multiple Dummies
SOLUTION Splitting a Column into Multiple Dummies
ARTICLE Dummy Variables Reasoning
Dummy Variables and Their Statistical Importance
Grouping
Concatenating Columns in Python
EXERCISE Concatenating Columns in Python
SOLUTION Concatenating Columns in Python
Changing Column Order in Pandas DataFrame
EXERCISE Changing Column Order in Pandas DataFrame
SOLUTION Changing Column Order in Pandas DataFrame
Implementing Checkpoints in Coding
EXERCISE Implementing Checkpoints in Coding
SOLUTION Implementing Checkpoint in Coding
Exploring the Initial Date Column
Using the Date Column to Extract the Appropriate Month Value
Introducing Day of the Week
EXERCISE Removing Columns
Further Analysis of the DataFrame Next 5 Columns
Further Analysis of the DaraFrame Education Children Pets
A Final Note on Preprocessing
A Note on Exporting Your Data as a csv File

Machine Learning
Exploring the Problem from a Machine Learning Point of View
Creating the Targets for the Logistic Regression
Selecting the Inputs
A Bit of Statistical Preprocessing
Train
Training the Model and Assessing its Accuracy
Extracting the Intercept and Coefficients from a Logistic Regression
Interpreting the Logistic Regression Coefficients
Omitting the dummy variables from the Standardization
Interpreting the Important Predictors
Simplifying the Model Backward Elimination
Testing the Machine Learning Model
How to Save the Machine Learning Model and Prepare it for Future Deployment
ARTICLE More about pickling
EXERCISE Saving the Model and Scaler
Creating a Module for Later Use of the Model

Installing MySQL and Getting Acquainted with the Interface
Installing MySQL
Installing MySQL on macOS and Unix systems
Setting Up a Connection
Introduction to the MySQL Interface

Connecting Python and SQL
Are you sure youre all set
Implementing the absenteeismmodule
Implementing the absenteeismmodule
Creating a Database in MySQL
Importing and Installing pymysql
Creating a Connection and Cursor
EXERCISE Create dfnewobs
Creating the predictedoutputs table in MySQL
Running an SQL SELECT Statement from Python
Transferring Data from Jupyter to Workbench
Transferring Data from Jupyter to Workbench
Transferring Data from Jupyter to Workbench

Analyzing the Obtained data in Tableau
EXERCISE Age vs Probability
Analysis in Tableau Age vs Probability
EXERCISE Reasons vs Probability
Analysis in Tableau Reasons vs Probability
EXERCISE Transportation Expense vs Probability
Analysis in Tableau Transportation Expense vs Probability

Bonus lecture
Bonus Lecture Next Steps

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