SQL + Tableau + Python

Curriculum

• Software Integration Free

  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 Free Properties and Definitions: Data, Servers, Clients, Requests, and Responses Free Properties and Definitions: Data Connectivity, APIs, and Endpoint Free Further Details on APIs Free Text Files as Means of Communication Free Definitions and Applications Free
• What's Next in the Course? Free

  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 Free Real-Life Example: Absenteeism at Work Free Real-Life Example: The Dataset Free
• Preprocessing the 'Absenteeism_data'

  23 Lessons 83 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 importing the Dataset in Python Eyeballing the Data Introduction to Terms with Multiple Meanings A Refresher on Regression Analysis An Analytical Approach to Solving the Task Dropping the "ID" Column Analysis of the "Reason for Absence" Column Converting a Feature into Multiple Dummy Variables Dropping a Dummy Variable Working with Dummy Variables from a Statistical Perspective Grouping the Various Reasons for Absence Concatenating Column Values Reordering Columns Creating Checkpoints in Jupyter Working on the "Date" Column Extracting the Month Value Creating the "Day of the Week" Column Dropping the "Date" Column Modifying "Education" and Discussing "Children" and "Pets" Analyzing the Next 5 Columns in our DataFrame Final Remarks on the Data Preprocessing Part of the Exercise A Note on Exporting Your Data as a *.csv File
• Machine Learning

  15 Lessons 65 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 Creating the Targets for the Logistic Regression Selecting the Inputs for the Regression Standardizing the Dataset for Better Results Train-test Split of the Data Training and Evaluating the Model Extracting the Intercept and Coefficients Interpreting the Coefficients Creating a Custom Scaler to Standardize Only Numerical Features Interpreting the Important Predictors Simplifying the Model (Backward Elimination) Testing the Machine Learning Model Saving theLogistic Regression Model More about 'pickling' Creating a Module for Later Use of the Model
• Connecting Python and SQL

  11 Lessons 46 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 Loading the "abssenteeism_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 and SQL Query from Python Moving Data from Python to SQL - Part I Moving Data from Python to SQL - Part II Moving Data from Python to SQL - Part III
• Analyzing the Obtained Data in Tableau

  3 Lessons 23 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 Tableau Analysis: Reasons vs Probability Tableau Analysis: Transportation Expense vs Probability