Packt | Python, SQL, Tableau: Integrating Python, SQL, and Tableau [FCO]

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Packt | Python, SQL, Tableau: Integrating Python, SQL, and Tableau [FCO] (Size: 1.68 GB)
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  01.Introduction
  0101.What Does the Course Cover.mp4 39.5 MB
  02.What is software integration
  0201.Properties and Definitions Data, Servers, Clients, Requests and Responses.mp4 30.27 MB
  0202.Properties and Definitions Data Connectivity, APIs, and Endpoints.mp4 56.2 MB
  0203.Further Details on APIs.mp4 55.78 MB
  0204.Text Files as Means of Communication.mp4 29.46 MB
  0205.Definitions and Applications.mp4 33.9 MB
  03.Setting up the working environment
  0301.Setting Up the Environment - An Introduction (Do Not Skip, Please)!.mp4 4.92 MB
  0302.Why Python and why Jupyter.mp4 35.26 MB
  0303.Installing Anaconda.mp4 30.36 MB
  0304.The Jupyter Dashboard - Part 1.mp4 6.77 MB
  0305.The Jupyter Dashboard - Part 2.mp4 14.39 MB
  0306.Installing sklearn.mp4 7.93 MB
  04.What's next in the course
  0401.Up Ahead.mp4 25.84 MB
  0402.Real-Life Example Absenteeism at Work.mp4 25.54 MB
  0403.Real-Life Example The Dataset.mp4 25.81 MB
  05.Preprocessing
  0501.Data Sets in Python.mp4 13.03 MB
  0502.Data at a Glance.mp4 39.36 MB
  0503.A Note on Our Usage of Terms with Multiple Meanings.mp4 20.06 MB
  0504.Picking the Appropriate Approach for the Task at Hand.mp4 10.93 MB
  0505.Removing Irrelevant Data.mp4 34.34 MB
  0506.Examining the Reasons for Absence.mp4 20.25 MB
  0507.Splitting a Column into Multiple Dummies.mp4 46.7 MB
  0508.Dummy Variables and Their Statistical Importance.mp4 5.75 MB
  0509.Grouping - Transforming Dummy Variables into Categorical Variables.mp4 38.49 MB
  0510.Concatenating Columns in Python.mp4 18.38 MB
  0511.Changing Column Order in Pandas DataFrame.mp4 6.94 MB
  0512.Implementing Checkpoints in Coding.mp4 13.01 MB
  0513.Exploring the Initial Date Column.mp4 26.59 MB
  0514.Using the Date Column to Extract the Appropriate Month Value.mp4 24.11 MB
  0515.Introducing Day of the Week.mp4 15.08 MB
  0516.Further Analysis of the DataFrame Next 5 Columns.mp4 14.11 MB
  0517.Further Analysis of the DaraFrame Education, Children, Pets.mp4 18.79 MB
  0518.A Final Note on Preprocessing.mp4 22.05 MB
  06.Machine Learnings
  0601.Exploring the Problem from a Machine Learning Point of View.mp4 25.5 MB
  0602.Creating the Targets for the Logistic Regression.mp4 34.39 MB
  0603.Selecting the Inputs.mp4 12.26 MB
  0604.A Bit of Statistical Preprocessing.mp4 15.62 MB
  0605.Train-test Split of the Data.mp4 39.92 MB
  0606.Training the Model and Assessing its Accuracy.mp4 32.65 MB
  0607.Extracting the Intercept and Coefficients from a Logistic Regression.mp4 33.39 MB
  0608.Interpreting the Logistic Regression Coefficients.mp4 46.47 MB
  0609.Omitting the dummy variables from the Standardization.mp4 33.87 MB
  0610.Interpreting the Important Predictors.mp4 28.11 MB
  0611.Simplifying the Model (Backward Elimination).mp4 37.87 MB
  0612.Testing the Machine Learning Model.mp4 41.45 MB
  0613.How to Save the Machine Learning Model and Prepare it for Future Deployment.mp4 30.4 MB
  0614.Creating a Module for Later Use of the Model.mp4 49.61 MB
  07.Installing MySQL and Getting Acquainted with the Interface
  0701.Installing MySQL.mp4 49.73 MB
  0702.Setting Up a Connection.mp4 9.54 MB
  0703.Introduction to the MySQL Interface.mp4 17.82 MB
  08.Connecting Python and SQL
  0801.Implementing the 'absenteeism_module' - Part I.mp4 15.57 MB
  0802.Implementing the 'absenteeism_module' - Part II.mp4 28.36 MB
  0803.Creating a Database in MySQL.mp4 33.46 MB
  0804.Importing and Installing 'pymysql'.mp4 11.17 MB
  0805.Creating a Connection and Cursor.mp4 10.44 MB
  0806.Creating the 'predicted_outputs' table in MySQL.mp4 27.4 MB
  0807.Running an SQL SELECT Statement from Python.mp4 12.54 MB
  0808.Transferring Data from Jupyter to Workbench - Part I.mp4 45.36 MB
  0809.Transferring Data from Jupyter to Workbench - Part II.mp4 32.11 MB
  0810.Transferring Data from Jupyter to Workbench - Part III.mp4 22.42 MB
  09.Analyzing the Obtained data in Tableau
  0901.Analysis in Tableau Age vs Probability.mp4 26.42 MB
  0902.Analysis in Tableau Reasons vs Probability.mp4 30.18 MB
  0903.Analysis in Tableau Transportation Expense vs Probability.mp4 66.99 MB
  Exercise Files
  exercise_files.zip 12.01 MB

Description


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By : 365 Careers
Released : 28 May 2019 (New Release!)
Torrent Contains : 70 Files, 11 Folders
Course Source : https://www.packtpub.com/application-development/python-sql-tableau-integrating-python-sql-and-tableau-video

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

Video Details

ISBN 9781838987916
Course Length 5 hour 1 minutes

Table of Contents

• Introduction
• What is software integration?
• Setting up the working environment
• What's next in the course?
• Preprocessing
• Machine Learnings
• Installing MySQL and Getting Acquainted with the Interface
• Connecting Python and SQL
• Analyzing the Obtained data in Tableau

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

About

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.

All the code files are placed at https://github.com/PacktPublishing/Python-SQL-Tableau-Integrating-Python-SQL-and-Tableau

Style and Approach

This course is designed so that each section covers a new scenario and uses a step-by-step approach to help you learn and understand each concept.

Features:

• How to use Python, SQL, and Tableau together
• Software integration
• Data preprocessing techniques
• Apply machine learning

Author

365 Careers

The company's 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 training. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can to 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.



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