Python, SQL, and Tableau: Integrating Python, SQL, and Tableau

Python, SQL, and Tableau: Integrating Python, SQL, and Tableau

by 365 Careers
Released May 2019
Publisher(s): Packt Publishing
ISBN: 9781838987916

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Video description

Python, SQL, and Tableau are three of the most widely used tools in the world of data science. Python is the leading programming language

SQL is the most widely used means for communication with database systems

Tableau is the preferred solution for data visualization;

The course starts off by introducing software integration as a concept. We discuss some important terms such as servers, clients, requests, and responses. Moreover, you will learn about data connectivity, APIs, and endpoints. Then we continue by introducing the real-life example exercise the course is centred around: the Absenteeism at Work dataset. The preprocessing part that follows will give you a taste of what BI and data science look like in real-life, on-the-job situations. Then we continue by applying some Machine Learning to our data. You will learn how to explore the problem at hand from a machine-learning perspective, how to create targets, what kind of statistical preprocessing is necessary for this part of the exercise, how to train a Machine Learning model, and how to test it—a truly comprehensive ML exercise. Connecting Python and SQL is not immediate; we show how that's done in an entire section of the course.

By the end of that section, you will be able to transfer data from Jupyter to Workbench. And finally, as promised, Tableau will allow us to visualize the data we have been working with. We will prepare several insightful charts and will interpret the results together.

What You Will Learn

  • Create a module of the ML model for later use
  • Connect Python and SQL to transfer data from Jupyter to Workbench
  • Visualize data in Tableau
  • Analyze and interpret exercise outputs in Jupyter and Tableau

Audience

This course is for anyone looking for a career in Business Intelligence and Data Science. Data scientists who are eagerly looking to break into the field and learn the necessary essentials of Data Science and software engineers who are interested in building intelligent applications driven by Python and Machine Learning will also benefit from this course.

About The Author

365 Careers: 365 Careers’ courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers’ courses are the perfect place to start.

Table of contents

  1. Chapter 1 : Introduction
    1. What Does the Course Cover?
  2. Chapter 2 : What is software integration?
    1. Properties and Definitions: Data, Servers, Clients, Requests and Responses
    2. Properties and Definitions: Data Connectivity, APIs, and Endpoints
    3. Further Details on APIs
    4. Text Files as Means of Communication
    5. Definitions and Applications
  3. Chapter 3 : Setting up the working environment
    1. Setting Up the Environment - An Introduction (Do Not Skip, Please)!
    2. Why Python and why Jupyter?
    3. Installing Anaconda
    4. The Jupyter Dashboard - Part 1
    5. The Jupyter Dashboard - Part 2
    6. Installing sklearn
  4. Chapter 4 : What's next in the course?
    1. Up Ahead
    2. Real-Life Example: Absenteeism at Work
    3. Real-Life Example: The Dataset
  5. Chapter 5 : Preprocessing
    1. Data Sets in Python
    2. Data at a Glance
    3. A Note on Our Usage of Terms with Multiple Meanings
    4. Picking the Appropriate Approach for the Task at Hand
    5. Removing Irrelevant Data
    6. Examining the Reasons for Absence
    7. Splitting a Column into Multiple Dummies
    8. Dummy Variables and Their Statistical Importance
    9. Grouping - Transforming Dummy Variables into Categorical Variables
    10. Concatenating Columns in Python
    11. Changing Column Order in Pandas DataFrame
    12. Implementing Checkpoints in Coding
    13. Exploring the Initial "Date" Column
    14. Using the "Date" Column to Extract the Appropriate Month Value
    15. Introducing "Day of the Week"
    16. Further Analysis of the DataFrame: Next 5 Columns
    17. Further Analysis of the DaraFrame: "Education", "Children", "Pets"
    18. A Final Note on Preprocessing
  6. Chapter 6 : Machine Learnings
    1. Exploring the Problem from a Machine Learning Point of View
    2. Creating the Targets for the Logistic Regression
    3. Selecting the Inputs
    4. A Bit of Statistical Preprocessing
    5. Train-test Split of the Data
    6. Training the Model and Assessing its Accuracy
    7. Extracting the Intercept and Coefficients from a Logistic Regression
    8. Interpreting the Logistic Regression Coefficients
    9. Omitting the dummy variables from the Standardization
    10. Interpreting the Important Predictors
    11. Simplifying the Model (Backward Elimination)
    12. Testing the Machine Learning Model
    13. How to Save the Machine Learning Model and Prepare it for Future Deployment
    14. Creating a Module for Later Use of the Model
  7. Chapter 7 : Installing MySQL and Getting Acquainted with the Interface
    1. Installing MySQL
    2. Setting Up a Connection
    3. Introduction to the MySQL Interface
  8. Chapter 8 : Connecting Python and SQL
    1. Implementing the 'absenteeism_module' - Part I
    2. Implementing the 'absenteeism_module' - Part II
    3. Creating a Database in MySQL
    4. Importing and Installing 'pymysql'
    5. Creating a Connection and Cursor
    6. Creating the 'predicted_outputs' table in MySQL
    7. Running an SQL SELECT Statement from Python
    8. Transferring Data from Jupyter to Workbench - Part I
    9. Transferring Data from Jupyter to Workbench - Part II
    10. Transferring Data from Jupyter to Workbench - Part III
  9. Chapter 9 : Analyzing the Obtained data in Tableau
    1. Analysis in Tableau: Age vs Probability
    2. Analysis in Tableau: Reasons vs Probability
    3. Analysis in Tableau: Transportation Expense vs Probability

Product information

  • Title: Python, SQL, and Tableau: Integrating Python, SQL, and Tableau
  • Author(s): 365 Careers
  • Release date: May 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781838987916