Data Sciense And Ml (Sql - Python - Tableau) For Integration

How to combine the three most important tools in data science: Python, SQL, and Tableau

What you'll learn

How to use Python, SQL, and Tableau together

Connect Python and SQL to transfer data from Jupyter to Workbench

Apply machine learning

Analysis and interpretation of the exercise outputs in Jupyter and Tableau

Software integration

Create a module for later use of the ML model

Requirements

Python (Basic)

SQL (Basic)

Tableau (Basic)

Description

Python, SQL, and Tableau are three of the most essential tools in the data science and business intelligence landscape.Python is the leading programming language, known for its versatility and powerful libraries.SQL is the universal language for communicating with and managing database systems.Tableau is the go-to platform for creating compelling data visualizations.To break it down: SQL helps store and manipulate data, Python enables complex calculations and coding, and Tableau transforms raw data into visually appealing insights. When these three tools are integrated seamlessly, businesses can save millions of dollars annually by automating reporting and analysis tasks.It’s no surprise that employers frequently seek candidates skilled in Python, SQL, and Tableau for Data Scientist and Business Intelligence Analyst roles. However, they’re not just looking for proficiency in each tool individually—they need professionals who can integrate these tools effectively to automate recurring data analysis tasks.In this course, we’ll teach you how to do just that: integrate Python, SQL, and Tableau to create powerful, automated solutions. Mastering this skill will give you a significant advantage in the job market. Unlike many other courses, this one covers a topic that’s rarely addressed—meaning you’ll acquire a unique skill set that truly differentiates your profile.While many people have a basic understanding of Python, and others might use SQL and Tableau to some extent, few have the expertise to integrate all three and deliver a comprehensive solution. As more businesses move towards automating their reporting and analysis, the ability to implement these techniques will become increasingly valuable—whether you’re working within a corporation or as a consultant.Our experience with a major global company has shown that consultants with these skills can command a four-figure hourly rate. And companies are more than willing to pay for this expertise because the long-term efficiencies and insights gained are invaluable.This course begins by introducing the concept of software integration. You’ll learn key terms such as servers, clients, requests, and responses, along with data connectivity, APIs, and endpoints.Next, we’ll dive into a real-life exercise centered around the 'Absenteeism at Work' dataset. The preprocessing phase will give you a realistic glimpse into the day-to-day work of a BI professional or data scientist—a crucial experience, as preprocessing often constitutes a significant portion of a data scientist’s work.Following that, you’ll apply Machine Learning techniques to the dataset. You’ll learn how to frame the problem from a machine learning perspective, create targets, perform necessary statistical preprocessing, train a model, and evaluate its performance. This is a thorough and practical introduction to machine learning.We’ll also cover how to connect Python with SQL—a task that requires careful attention and skill. By the end of this section, you’ll know how to transfer data seamlessly between Jupyter and Workbench.Finally, we’ll use Tableau to visualize the processed data, creating insightful charts and interpreting the results together.This is a comprehensive and practical data science course that equips you with invaluable skills to stand out in the competitive job market. With our 30-day unconditional money-back guarantee, there’s no risk—only the opportunity to significantly boost your career.Don’t wait—start now and gain the skills that will make you a standout candidate. Your only regret will be not discovering this course sooner!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Data Connectivity, APIs, and Endpoints

Lecture 3 API

Lecture 4 4. Exchanging Information using Text Files

Lecture 5 Software Integration

Section 2: What's Next

Lecture 6 What's next in the course

Lecture 7 Defining the Task - Absenteeism at Work

Lecture 8 3. The Data Set

Section 3: Preprocessing the 'Absenteeism_data'

Lecture 9 Importing the Data Set in Python

Lecture 10 Eyeballing the Data

Lecture 11 Introduction to Terms with Multiple Meanings

Lecture 12 An Analytical Approach to Solving the Task

Lecture 13 Dropping the 'ID' Column

Lecture 14 Analysis of the 'Reason for Absence' Column

Lecture 15 Converting a Feature into Multiple Dummy Variables

Lecture 16 Working with Dummy Variables from a Statistical Perspective

Lecture 17 Grouping the Various Reasons for Absence

Lecture 18 Concatenating Column Values

Lecture 19 Reordering Columns

Lecture 20 Creating Checkpoints in Jupyter

Lecture 21 Working on the 'Date' Column

Lecture 22 Extracting the Month Value

Lecture 23 Creating the 'Day of the Week' Column

Lecture 24 Analyzing the Next 5 Columns in our DataFrame

Lecture 25 Modifying 'Education' and discussing 'Children' and 'Pets'

Lecture 26 Final Remarks on the Data Preprocessing Part of the Exercise

Section 4: Applying Machine Learning to the Preprocessed Datav

Lecture 27 Exploring the Problem from a Machine Learning Point of View

Lecture 28 Creating the Targets for the Regression

Lecture 29 Selecting the Inputs for the Regression

Lecture 30 Standardizing the Dataset for Better Results

Lecture 31 Train-Test Split

Lecture 32 Training and evaluating the model

Lecture 33 Extracting the Intercept and Coefficients

Lecture 34 Interpreting the Coefficients

Lecture 35 Creating a Custom Scaler to Standardize Only Numerical Features

Lecture 36 Interpreting the (Important) Coefficients

Lecture 37 Simplifying the Model (Backward Elimination)

Lecture 38 Testing the Logistic Regression Model

Lecture 39 Saving the Logistic Regression Model

Lecture 40 Creating a module for later use of the model

Section 5: Python and SQL

Lecture 41 Loading the 'absenteeism_module'

Lecture 42 Working with the 'absenteeism_module'

Lecture 43 Creating a Database Structure in MySQL

Lecture 44 Installing and Importing 'pymysql'

Lecture 45 Setting up a Connection and Creating a Cursor

Lecture 46 Creating the 'predicted_outputs' table in MySQL

Lecture 47 Executing an SQL Query from Python

Lecture 48 Moving Data from Python to SQL - Part I

Lecture 49 Moving Data from Python to SQL - Part II

Lecture 50 Moving Data from Python to SQL - Part III

Section 6: Analyzing the Obtained Data in Tableau

Lecture 51 Tableau Analysis - Age vs Probability

Lecture 52 Tableau Analysis - Reasons vs Probability

Lecture 53 Tableau Analysis - Transportation Expense vs Probability

Individuals interested in a career in Business Intelligence and Data Science