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Course Curriculum

Software Integration
1.1 Exchanging Information using Text Files 00:00:00
1.2 Introduction to Data Connectivity, APIs, and Endpoint 00:00:00
1.3 Introduction to Data, Servers, Clients, Requests, and Respon 00:00:00
1.4 More on APIs 00:00:00
1.5 Software Integration Python-SQL-Tableau 00:00:00
What's Next in the course
2.1 Defining the Task Absenteeism at Work 00:00:00
2.2 The Data Set 00:00:00
2.3 What’s next in the course 00:00:00
Prepocessing the 'Absenteemls_data'
3.1 An Analytical Approach to Solving the Task 00:00:00
3.2 Analysis of the Reason for Absence Column 00:00:00
3.3 Analyzing the Next 5 Columns in our DataFrame 00:00:00
3.4 Concatenating Column Values 365 00:00:00
3.5 Converting a Feature into Multiple Dummy Variables 00:00:00
3.6 Creating Checkpoints in Jupyter 00:00:00
3.7 Creating the Day of the Week Column 00:00:00
3.8 Dropping the ID Column 00:00:00
3.9 Extracting the Month Value 00:00:00
3.10 Eyeballing the Data 00:00:00
3.11 Final Remarks on the Data Preprocessing Part of the Exercise 00:00:00
3.12 Grouping the Various Reasons for Absence 00:00:00
3.13 Importing the Data Set in Python 00:00:00
3.14 Introduction to Terms with Multiple Meanings 00:00:00
3.15 Modifying Education and discussing Children and Pets 00:00:00
3.16 Reordering Columns 00:00:00
3.17 Working on the Date Column 00:00:00
3.18 Working with Dummy Variables from a Statistical Perspective 00:00:00
Applying Machine Learning to the Preprocessed Data
4.1 Creating a Custom Scaler to Standardize Only Numerical Featu 00:00:00
4.2 Creating a module for later use of the model 00:00:00
4.3 Creating the Targets for the Regression 00:00:00
4.4 Exploring the Problem from a Machine Learning Point of View 00:00:00
4.5 Extracting the Intercept and Coefficients 00:00:00
4.6 Interpreting the (Important) Coefficients 00:00:00
4.7 Interpreting the Coefficients 00:00:00
4.8 Saving the Logistic Regression Model 00:00:00
4.9 Selecting the Inputs for the Regression 00:00:00
4.10 Simplifying the Model (Backward Elimination) 00:00:00
4.11 Standardizing the Dataset for Better Results 00:00:00
4.12 Testing the Logistic Regression Model 00:00:00
4.13 Training and evaluating the model 00:00:00
4.14 Train-Test Split 00:00:00
Connecting Python & SQL
5.1 Creating a Database Structure in MySQL 00:00:00
5.2 Creating the ‘predicted_outputs’ table in MySQL 00:00:00
5.3 Executing an SQL Query from Python 00:00:00
5.4 Installing and Importing ‘pymysql’ 00:00:00
5.5 Loading the ‘absenteeism_module’ 00:00:00
5.6 Moving Data from Python to SQL – Part I 00:00:00
5.7 Moving Data from Python to SQL – Part II 00:00:00
5.8 Moving Data from Python to SQL – Part III 00:00:00
5.9 Setting up a Connection and Creating a Cursor 365 Data Scie 00:00:00
5.10 Working with the ‘absenteeism_module’ 365 Data Science 00:00:00
Analyzing the obtained data in tableau
6.1 Tableau Analysis Age vs Probability 00:00:00
6.2 Tableau Analysis Reasons vs Probability 00:00:00
6.3 Tableau Analysis Transportation Expense vs Probability 00:00:00

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