SQL + Tableau + Python
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.
4 hours 
10 exercises 
1 assignments 
8 quizzes 
19 downloadables
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Section 1
Software Integration
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.
Introduction to Data, Servers, Clients, Requests, and Responses
Introduction to Data Connectivity, APIs, and Endpoints
More on APIs
Exchanging Information using Text Files
Software Integration: Python-SQL-Tableau
Section 2
What's Next in the Course?
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.
What's next in the course?
Defining the Task: Absenteeism at Work
The Data Set
Section 3
Preprocessing the 'Absenteeism_data'
In this section, we will focus on 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.
Importing and Eyeballing the Data Set in Python
Introduction to Terms with Multiple Meanings
An Analytical Approach to Solving the Task
Dropping the "ID" Column
Working on the "Reasons for Absence" Column
Grouping the Various Reasons for Absence
Creating Checkpoints in Jupyter
Extracting the Month and Day of the Week values from the "Date" Column
Analyzing the Next 5 Columns in our DataFrame
Final Remarks on the Data Preprocessing Part of the Exercise
Section 4
Applying Machine Learning to the Preprocessed Data
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
Creating the Inputs and the Targets for the Regression
Standardizing the Dataset for Better Results
Train-Test Split
Training and evaluating the model
Extraction and Interpretation of the Intercept and Coefficients
Creating a Custom Scaler to Standardize Only Numerical Features
Simplifying the Model (Backward Elimination)
Testing the Logistic Regression Model
Saving the Model and Preparing it for Deployment
Section 5
Connecting Python and SQL
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.
Loading the 'absenteeism_module'
Working with the 'absenteeism_module'
Creating a Database Structure in MySQL
Installing and Importing 'pymysql'
Setting up a Connection and Creating a Cursor
Creating the 'predicted_outputs' table in MySQL
Executing an SQL Query from Python
Moving Data from Python to SQL
Section 6
Analyzing the Obtained Data in Tableau
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
Tableau Analysis: Reasons vs Probability
Tableau Analysis: Transportation Expense vs Probability
Machine and Deep Learning
This course is part of Module 3 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. Expanding on your statistical and programming skills from Modules 1 and 2, Module 3 is designed to improve your programming skills and develop your advanced statistical thinking. You will learn how to build complete linear and logistic regression models, how to cluster data, and how to build deep learning models with TensorFlow 2.0.
See All Modules
Why Choose the 365 Data Science Program?
Practice
Real-life project and data. Solve them on your own computer as you would in the office.
Q&A Hub
Our expert instructors are happy to help. Post a question and get a personal answer by one of our instructors.
Certificates
Earn a verifiable certificate after each completed course. Celebrate your successes and share your progress with your professional network!
Trust the other 500,000 students
The course is in-depth and is delivered at a steady pace with eye catching visuals. The instructors go through all the basics really well. They try not to over-simplify the material, you get a good sense аof how deep Data Science is in the course. Great job!!!
This course is amazing! After watching the video carefully and doing all the exercises, I am even capable of having discussions with Machine learning major Master’s students! High standard course with reasonable pricing.
Very clear and in-depth explanation of data science and how all the inter-related concepts apply in real life business environment. Absolutely great for beginners! Best data science course I have come across so far!
I would highly recommend the course to any beginner who wants to venture into the world of Data Science. The concepts are very well explained and there is an emphasis on practical application which really helps create a better understanding of the concepts.
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