Python + SQL + Tableau: Integrating Python, SQL, and Tableau
4.4 (1,828 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
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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
Bestseller
4.4 (1,828 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
19,706 students enrolled
Created by 365 Careers
Last updated 8/2020
English
English [Auto], French [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 5 hours on-demand video
  • 28 articles
  • 5 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Expand all 91 lectures 05:14:44
+ What is software integration?
5 lectures 29:38

Which of the following is incorrect?

Properties and Definitions: Data, Servers, Clients, Requests and Responses
2 questions
Properties and Definitions: Data Connectivity, APIs, and Endpoints
2 questions
Further Details on APIs
08:05
Further Details on APIs
2 questions
Text Files as Means of Communication
1 question
Definitions and Applications
05:25
Definitions and Applications
2 questions
+ Setting up the working environment
9 lectures 23:56
Setting Up the Environment - An Introduction (Do Not Skip, Please)!
00:51
Why Python and why Jupyter?
04:59
Why Python and why Jupyter?
2 questions
Installing Anaconda
06:49
The Jupyter Dashboard - Part 1
03:15
The Jupyter Dashboard - Part 2
06:15
Jupyter Shortcuts
00:09
The Jupyter Dashboard
3 questions
Installing sklearn
01:16
Installing Packages - Exercise
00:09
Installing Packages - Solution
00:12
+ What's next in the course?
4 lectures 10:51
Up Ahead
04:08
Real-Life Example: Absenteeism at Work
02:48
Real-Life Example: The Dataset
03:18
Real-Life Example: The Dataset
1 question
Important Notice Regarding Datasets
00:37
+ Preprocessing
33 lectures 01:29:45
What to Expect from the Next Couple of Sections
01:39
Data Sets in Python
03:23
Data at a Glance
05:53
A Note on Our Usage of Terms with Multiple Meanings
03:27
ARTICLE - A Brief Overview of Regression Analysis
01:50
Picking the Appropriate Approach for the Task at Hand
02:17
Removing Irrelevant Data
06:27
EXERCISE - Removing Irrelevant Data
00:25
SOLUTION - Removing Irrelevant Data
00:01
Examining the Reasons for Absence
05:04
Splitting a Column into Multiple Dummies
08:37
EXERCISE - Splitting a Column into Multiple Dummies
00:04
SOLUTION - Splitting a Column into Multiple Dummies
00:00
ARTICLE - Dummy Variables: Reasoning
01:32
Dummy Variables and Their Statistical Importance
01:28
Grouping - Transforming Dummy Variables into Categorical Variables
08:35
Concatenating Columns in Python
04:35
EXERCISE - Concatenating Columns in Python
00:04
SOLUTION - Concatenating Columns in Python
00:01
Changing Column Order in Pandas DataFrame
01:43
EXERCISE - Changing Column Order in Pandas DataFrame
00:06
SOLUTION - Changing Column Order in Pandas DataFrame
00:12
Implementing Checkpoints in Coding
02:52
EXERCISE - Implementing Checkpoints in Coding
00:04
SOLUTION - Implementing Checkpoint in Coding
00:00
Exploring the Initial "Date" Column
07:48
Using the "Date" Column to Extract the Appropriate Month Value
07:00
Introducing "Day of the Week"
03:36
EXERCISE - Removing Columns
00:37
Further Analysis of the DataFrame: Next 5 Columns
03:17
Further Analysis of the DaraFrame: "Education", "Children", "Pets"
04:38
A Final Note on Preprocessing
01:59
A Note on Exporting Your Data as a *.csv File
00:26
+ Machine Learning
16 lectures 01:07:03
Creating the Targets for the Logistic Regression
06:32
Selecting the Inputs
02:41
A Bit of Statistical Preprocessing
03:26
Train-test Split of the Data
06:12
Training the Model and Assessing its Accuracy
05:39
Extracting the Intercept and Coefficients from a Logistic Regression
05:16
Interpreting the Logistic Regression Coefficients
06:14
Omitting the dummy variables from the Standardization
04:12
Interpreting the Important Predictors
05:10
Simplifying the Model (Backward Elimination)
04:02
Testing the Machine Learning Model
04:43
How to Save the Machine Learning Model and Prepare it for Future Deployment
04:06
ARTICLE - More about 'pickling'
01:13
EXERCISE - Saving the Model (and Scaler)
00:13
Creating a Module for Later Use of the Model
04:04
+ Installing MySQL and Getting Acquainted with the Interface
4 lectures 19:03
Installing MySQL
09:56
Installing MySQL on macOS and Unix systems
01:24
Setting Up a Connection
02:34
Introduction to the MySQL Interface
05:09
+ Connecting Python and SQL
12 lectures 46:22
Are you sure you're all set?
00:13
Implementing the 'absenteeism_module' - Part I
03:50
Implementing the 'absenteeism_module' - Part II
06:23
Creating a Database in MySQL
06:37
Importing and Installing 'pymysql'
02:44
Creating a Connection and Cursor
02:54
EXERCISE - Create 'df_new_obs'
00:10
Creating the 'predicted_outputs' table in MySQL
04:52
Running an SQL SELECT Statement from Python
03:04
Transferring Data from Jupyter to Workbench - Part I
06:15
Transferring Data from Jupyter to Workbench - Part II
06:35
Transferring Data from Jupyter to Workbench - Part III
02:45
+ Analyzing the Obtained data in Tableau
6 lectures 23:29
EXERCISE - Age vs Probability
00:14
Analysis in Tableau: Age vs Probability
08:49
EXERCISE - Reasons vs Probability
00:14
Analysis in Tableau: Reasons vs Probability
07:49
EXERCISE - Transportation Expense vs Probability
00:22
Analysis in Tableau: Transportation Expense vs Probability
06:00
+ Bonus lecture
1 lecture 00:39
Bonus Lecture: Next Steps
00:39