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.

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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.

Premium course icon Introduction to Data, Servers, Clients, Requests, and Responses
Premium course icon Introduction to Data Connectivity, APIs, and Endpoints
Premium course icon More on APIs
Premium course icon Exchanging Information using Text Files
Premium course icon 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.

Premium course icon What's next in the course?
Premium course icon Defining the Task: Absenteeism at Work
Premium course icon The Data Set

Section 3

Preprocessing the 'Absenteeism_data'

In this section we focus on preprocessing an entire dataset without skipping any steps. 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.

Premium course icon Importing and Eyeballing the Data Set in Python
Premium course icon Introduction to Terms with Multiple Meanings
Premium course icon An Analytical Approach to Solving the Task
Premium course icon Dropping the "ID" Column
Premium course icon Working on the "Reasons for Absence" Column
Premium course icon Grouping the Various Reasons for Absence
Premium course icon Creating Checkpoints in Jupyter
Premium course icon Extracting the Month and Day of the Week values from the "Date" Column
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Premium course icon Analyzing the Next 5 Columns in our DataFrame
Premium course icon Final Remarks on the Data Preprocessing Part of the Exercise
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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!

Premium course icon Exploring the Problem from a Machine Learning Point of View
Premium course icon Creating the Inputs and the Targets for the Regression
Premium course icon Standardizing the Dataset for Better Results
Premium course icon Train-Test Split
Premium course icon Training and evaluating the model
Premium course icon Extraction and Interpretation of the Intercept and Coefficients
Premium course icon Creating a Custom Scaler to Standardize Only Numerical Features
Premium course icon Simplifying the Model (Backward Elimination)
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Premium course icon Testing the Logistic Regression Model
Premium course icon Saving the Model and Preparing it for Deployment
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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 first hand, 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.

Premium course icon Loading the 'absenteeism_module'
Premium course icon Working with the 'absenteeism_module'
Premium course icon Creating a Database Structure in MySQL
Premium course icon Installing and Importing 'pymysql'
Premium course icon Setting up a Connection and Creating a Cursor
Premium course icon Creating the 'predicted_outputs' table in MySQL
Premium course icon Executing an SQL Query from Python
Premium course icon Moving Data from Python to SQL
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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.

Premium course icon Tableau Analysis: Age vs Probability
Premium course icon Tableau Analysis: Reasons vs Probability
Premium course icon Tableau Analysis: Transportation Expense vs Probability
MODULE 3

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 up on 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.

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