SQL + Tableau + Python

Name: SQL + Tableau + Python Course
Price: 36 USD

with Martin Ganchev

4.8/5

(408)

Master the integration of SQL, Python, and Tableau for effective data analysis

5 hours of content 7695 students

Start for free

What you get:

5 hours of content
10 Interactive exercises
32 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

SQL + Tableau + Python

A course by Martin Ganchev

Start for free

What you get:

5 hours of content
10 Interactive exercises
32 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Start for free

What you get:

5 hours of content
10 Interactive exercises
32 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

What You Learn

Master the integration of SQL, Python, and Tableau for effective data analysis
Use SQL, Tableau, and Python to respond to real-life business questions
Learn how to seamlessly transfer data from Python to SQL, creating the structure necessary for connecting two compatible software tools
Gain hands-on experience with Tableau to create compelling data visualizations and impactful analysis
Apply machine learning techniques to solve specific business problems
Enhance your resume with three essential tech stack components for aspiring data analysts

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

While Python is the leading programming language for data science, SQL is unmatched when it comes to relational database management. Tableau, on the other hand, is a leading business intelligence software, providing tools for quick computations and rich visualizations. This course will show you how to combine these software products to solve real-life business problems.

Learn for Free

1.1 Course Introduction

3 min

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

5 min

1.3 Properties and Definitions: Data Connectivity, APIs, and Endpoint

7 min

1.4 Further Details on APIs

8 min

1.5 Text Files as Means of Communication

4 min

1.6 Definitions and Applications

5 min

Interactive Exercises
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

Curriculum

1. Software Integration

6 Lessons 32 Min

We begin by introducing key terms, such as data, servers, clients, requests, responses, data connectivity, APIs, and endpoints. Understanding all of these terms and how they are used is crucial for grasping the concept of software integration.

Course Introduction
3 min
Properties and Definitions: Data, Servers, Clients, Requests, and Responses
5 min
Properties and Definitions: Data Connectivity, APIs, and Endpoint
7 min
Further Details on APIs
8 min
Text Files as Means of Communication
4 min
Definitions and Applications
5 min
2. What's Next in the Course?

3 Lessons 10 Min

In this short section, we introduce the business problem to be solved and outline the task we’ll need to solve in the lessons to come: predict the probability that an individual will be absent from work on a specific day.

Up Ahead
4 min
Real-Life Example: Absenteeism at Work
3 min
Real-Life Example: The Dataset
3 min
3. Preprocessing the 'Absenteeism_data'

23 Lessons 94 Min

In this section, we will focus on cleaning and preprocessing an entire dataset. If you already are a Python guru and cleaning data sets comes as a second nature, you may wish to skip this section. But if it is possible that you have gaps in your Python mastery, even if it’s here and there, it is essential that you go through every lecture. We are actually coding all the time in this section, so you’ll quite likely end up having a lot of fun.

What to Expect from the Next Couple of Sections Read now
3 min
importing the Dataset in Python
3 min
Eyeballing the Data
6 min
Introduction to Terms with Multiple Meanings
3 min
A Refresher on Regression Analysis Read now
3 min
An Analytical Approach to Solving the Task
2 min
Dropping the "ID" Column
6 min
Analysis of the "Reason for Absence" Column
5 min
Converting a Feature into Multiple Dummy Variables
9 min
Dropping a Dummy Variable Read now
3 min
Working with Dummy Variables from a Statistical Perspective
1 min
Grouping the Various Reasons for Absence
9 min
Concatenating Column Values
5 min
Reordering Columns
2 min
Creating Checkpoints in Jupyter
3 min
Working on the "Date" Column
8 min
Extracting the Month Value
7 min
Creating the "Day of the Week" Column
4 min
Dropping the "Date" Column Read now
1 min
Modifying "Education" and Discussing "Children" and "Pets"
5 min
Analyzing the Next 5 Columns in our DataFrame
3 min
Final Remarks on the Data Preprocessing Part of the Exercise
2 min
A Note on Exporting Your Data as a *.csv File Read now
1 min
4. Machine Learning

15 Lessons 67 Min

This section is at the core of this Absenteeism Exercise. Here, we discuss modern machine learning tools that can be used to solve problems like the one we’re looking at. Every step requires you to use Python, so stretch your coding fingers and let’s get to it!

Exploring the Problem from a Machine Learning Point of View
3 min
Creating the Targets for the Logistic Regression
7 min
Selecting the Inputs for the Regression
3 min
Standardizing the Dataset for Better Results
3 min
Train-test Split of the Data
6 min
Training and Evaluating the Model
6 min
Extracting the Intercept and Coefficients
5 min
Interpreting the Coefficients
6 min
Creating a Custom Scaler to Standardize Only Numerical Features
4 min
Interpreting the Important Predictors
5 min
Simplifying the Model (Backward Elimination)
4 min
Testing the Machine Learning Model
5 min
Saving theLogistic Regression Model
4 min
More about 'pickling' Read now
2 min
Creating a Module for Later Use of the Model
4 min
5. Connecting Python and SQL

11 Lessons 47 Min

In this section, you see software integration applied in practice. You will not only be given the chance to experience how data can be transferred from Python to SQL firsthand, but you will also learn about the structure necessary for connecting two compatible software tools. Finally, we will export the dataset in the form of a *.csv file that’s ready to be used in Tableau.

Downloading the Section Resources Read now
1 min
Loading the "abssenteeism_module"
4 min
Working with the "absenteeism_module"
6 min
Creating a Database Structure in MySQL
6 min
Installing and Importing 'pymysql'
3 min
Setting up a Connection and Creating a Cursor
3 min
Creating the 'predicted_outputs' table in MySQL
5 min
Executing and SQL Query from Python
3 min
Moving Data from Python to SQL - Part I
6 min
Moving Data from Python to SQL - Part II
7 min
Moving Data from Python to SQL - Part III
3 min
6. Analyzing the Obtained Data in Tableau

4 Lessons 24 Min

In the last section of this course, we focus on the analytical part of the absenteeism task. We will load, analyze, and visualize in Tableau the data obtained in the previous sections.

Tableau Analysis: Age vs Probability - Homework and Dataset Read now
1 min
Tableau Analysis: Age vs Probability
9 min
Tableau Analysis: Reasons vs Probability
8 min
Tableau Analysis: Transportation Expense vs Probability
6 min

Topics

Pythondata analysisTableauProgrammingData processingmachine learningTheorydata visualizationSQL

Tools & Technologies

Course Requirements

A solid understanding of relational database theory and basic SQL is required. It is highly recommended to start with the SQL course first
An introduction to Tableau is strongly recommended
An introduction to Machine Learning in Python is strongly recommended
You will need to install MySQL Workbench
You will need to install Tableau Public

Who Should Take This Course?

Level of difficulty: Advanced

Aspiring data analysts, data scientists, data engineers who want to improve their job prospects
Existing data analysts, data scientists, data engineers who want to become proficient in SQL, Tableau, and Python

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Martin Ganchev

Worked at

15 Courses

34025 Reviews

508655 Students

Martin began working with 365 in 2016 as the company’s second employee. Martin’s resilience, hard-working attitude, attention to detail, and excellent teaching style played an instrumental role in 365’s early days. He authored some of the firm’s most successful courses. And besides teaching, Martin dreams about becoming an actor. In September 2021, he enrolled in an acting school in Paris, France.