SQL + Tableau + Python
Building on your SQL + Tableau expertise, this course provides you with the skills to do complex preprocessing and agile real-time machine learning.
with Martin Ganchev
Start CourseCourse Overview
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.
61 High Quality Lessons
9 Practical Tasks
4 Hours of Video
Certificate of Achievement
Skills you will gain
data analysisdata processingmachine learningprogrammingpythontableautheory
What You'll Learn
Combine Python with SQL and Tableau to create the ultimate tool for solving business problems! This course will teach you how.
Understand key software integration terms
Predict probabilities
Clean and preprocess an entire dataset
Handle modern machine learning tools
Learn about the structure necessary for connecting two compatible software tools
Load, analyze, and visualize data in Tableau
Curriculum
- Software Integration Free6 Lesson 32 Min
- What's Next in the Course? Free3 Lesson 10 Min
- Preprocessing the 'Absenteeism_data'23 Lesson 83 MinWhat 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 Learning15 Lesson 65 MinExploring 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 SQL11 Lesson 46 MinDownloading 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 Tableau3 Lesson 23 Min
“Step by step, I will walk you through a complete exercise consisting of the integration of SQL, Tableau, and Python – three of the most popular technologies in the data science world. This course will help you gain first-hand experience with the types of tasks data scientists solve daily when helping businesses leverage their data, giving you strong competitive edge over other data science professionals.”
Martin Ganchev
Worked at European Commission
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