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