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.
4 hours
10 exercises
1 assignments
8 quizzes
19 downloadables Sign up to
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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.
Introduction to Data, Servers, Clients, Requests, and Responses Introduction to Data Connectivity, APIs, and Endpoints More on APIs Exchanging Information using Text Files 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.
What's next in the course? Defining the Task: Absenteeism at Work 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.
Importing and Eyeballing the Data Set in Python Introduction to Terms with Multiple Meanings An Analytical Approach to Solving the Task Dropping the "ID" Column Working on the "Reasons for Absence" Column Grouping the Various Reasons for Absence Creating Checkpoints in Jupyter Extracting the Month and Day of the Week values from the "Date" Column Analyzing the Next 5 Columns in our DataFrame Final Remarks on the Data Preprocessing Part of the Exercise 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!
Exploring the Problem from a Machine Learning Point of View Creating the Inputs and the Targets for the Regression Standardizing the Dataset for Better Results Train-Test Split Training and evaluating the model Extraction and Interpretation of the Intercept and Coefficients Creating a Custom Scaler to Standardize Only Numerical Features Simplifying the Model (Backward Elimination) Testing the Logistic Regression Model Saving the Model and Preparing it for Deployment 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.
Loading the 'absenteeism_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 an SQL Query from Python Moving Data from Python to SQL 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.
Tableau Analysis: Age vs Probability Tableau Analysis: Reasons vs Probability Tableau Analysis: Transportation Expense vs Probability 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.
See All Modules Why Choose the 365 Data Science Program?
Practice
Real-life project and data. Solve them on your own computer as you would in the office.
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Trust the other 276,000 students
The course is in-depth and is delivered at a steady pace with eye catching visuals. The instructors go through all the basics really well. They try not to over-simplify the material, you get a good sense аof how deep Data Science is in the course. Great job!!!
This course is amazing! After watching the video carefully and doing all the exercises, I am even capable of having discussions with Machine learning major Master’s students! High standard course with reasonable pricing.
Very clear and in-depth explanation of data science and how all the inter-related concepts apply in real life business environment. Absolutely great for beginners! Best data science course I have come across so far!
I would highly recommend the course to any beginner who wants to venture into the world of Data Science. The concepts are very well explained and there is an emphasis on practical application which really helps create a better understanding of the concepts.
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