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[FreeCoursesOnline.Me] [Packt] Python, SQL, Tableau Integrating Python, SQL, And Tableau [FCO]

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	6f6d7c39c32ae97ccdb01b2afa3df98d3ba6ac1a
Size:	1.7 GB in 65 files
Release Date(s):	Torrent Release: 10-08-2019 by user
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DESCRIPTION

For More Udemy Free Courses >>> https://ftuforum.com/

For more Lynda and other Courses >>> https://www.freecoursesonline.me/

Our Forum for discussion >>> https://discuss.ftuforum.com/

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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FILE LIST

Filename		Size
	0. Websites you may like/How you can help Team-FTU.txt	237 B
	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	30.3 MB
	02.What is software integration/0202.Properties and Definitions Data Connectivity, APIs, and Endpoints.mp4	56.2 MB
	02.What is software integration/0203.Further Details on APIs.mp4	55.8 MB
	02.What is software integration/0204.Text Files as Means of Communication.mp4	29.5 MB
	02.What is software integration/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	4.9 MB
	03.Setting up the working environment/0302.Why Python and why Jupyter.mp4	35.3 MB
	03.Setting up the working environment/0303.Installing Anaconda.mp4	30.4 MB
	03.Setting up the working environment/0304.The Jupyter Dashboard - Part 1.mp4	6.8 MB
	03.Setting up the working environment/0305.The Jupyter Dashboard - Part 2.mp4	14.4 MB
	03.Setting up the working environment/0306.Installing sklearn.mp4	7.9 MB
	04.What's next in the course/0401.Up Ahead.mp4	25.8 MB
	04.What's next in the course/0402.Real-Life Example Absenteeism at Work.mp4	25.5 MB
	04.What's next in the course/0403.Real-Life Example The Dataset.mp4	25.8 MB
	05.Preprocessing/0501.Data Sets in Python.mp4	13 MB
	05.Preprocessing/0502.Data at a Glance.mp4	39.4 MB
	05.Preprocessing/0503.A Note on Our Usage of Terms with Multiple Meanings.mp4	20.1 MB
	05.Preprocessing/0504.Picking the Appropriate Approach for the Task at Hand.mp4	10.9 MB
	05.Preprocessing/0505.Removing Irrelevant Data.mp4	34.3 MB
	05.Preprocessing/0506.Examining the Reasons for Absence.mp4	20.3 MB
	05.Preprocessing/0507.Splitting a Column into Multiple Dummies.mp4	46.7 MB
	05.Preprocessing/0508.Dummy Variables and Their Statistical Importance.mp4	5.8 MB
	05.Preprocessing/0509.Grouping - Transforming Dummy Variables into Categorical Variables.mp4	38.5 MB
	05.Preprocessing/0510.Concatenating Columns in Python.mp4	18.4 MB
	05.Preprocessing/0511.Changing Column Order in Pandas DataFrame.mp4	6.9 MB
	05.Preprocessing/0512.Implementing Checkpoints in Coding.mp4	13 MB
	05.Preprocessing/0513.Exploring the Initial Date Column.mp4	26.6 MB
	05.Preprocessing/0514.Using the Date Column to Extract the Appropriate Month Value.mp4	24.1 MB
	05.Preprocessing/0515.Introducing Day of the Week.mp4	15.1 MB
	05.Preprocessing/0516.Further Analysis of the DataFrame Next 5 Columns.mp4	14.1 MB
	05.Preprocessing/0517.Further Analysis of the DaraFrame Education, Children, Pets.mp4	18.8 MB
	05.Preprocessing/0518.A Final Note on Preprocessing.mp4	22 MB
	06.Machine Learnings/0601.Exploring the Problem from a Machine Learning Point of View.mp4	25.5 MB
	06.Machine Learnings/0602.Creating the Targets for the Logistic Regression.mp4	34.4 MB
	06.Machine Learnings/0603.Selecting the Inputs.mp4	12.3 MB
	06.Machine Learnings/0604.A Bit of Statistical Preprocessing.mp4	15.6 MB
	06.Machine Learnings/0605.Train-test Split of the Data.mp4	39.9 MB
	06.Machine Learnings/0606.Training the Model and Assessing its Accuracy.mp4	32.7 MB
	06.Machine Learnings/0607.Extracting the Intercept and Coefficients from a Logistic Regression.mp4	33.4 MB
	06.Machine Learnings/0608.Interpreting the Logistic Regression Coefficients.mp4	46.5 MB
	06.Machine Learnings/0609.Omitting the dummy variables from the Standardization.mp4	33.9 MB
	06.Machine Learnings/0610.Interpreting the Important Predictors.mp4	28.1 MB
	06.Machine Learnings/0611.Simplifying the Model (Backward Elimination).mp4	37.9 MB
	06.Machine Learnings/0612.Testing the Machine Learning Model.mp4	41.5 MB
	06.Machine Learnings/0613.How to Save the Machine Learning Model and Prepare it for Future Deployment.mp4	30.4 MB
	06.Machine Learnings/0614.Creating a Module for Later Use of the Model.mp4	49.6 MB
	07.Installing MySQL and Getting Acquainted with the Interface/0701.Installing MySQL.mp4	49.7 MB
	07.Installing MySQL and Getting Acquainted with the Interface/0702.Setting Up a Connection.mp4	9.5 MB
	07.Installing MySQL and Getting Acquainted with the Interface/0703.Introduction to the MySQL Interface.mp4	17.8 MB
	08.Connecting Python and SQL/0801.Implementing the 'absenteeism_module' - Part I.mp4	15.6 MB
	08.Connecting Python and SQL/0802.Implementing the 'absenteeism_module' - Part II.mp4	28.4 MB
	08.Connecting Python and SQL/0803.Creating a Database in MySQL.mp4	33.5 MB
	08.Connecting Python and SQL/0804.Importing and Installing 'pymysql'.mp4	11.2 MB
	08.Connecting Python and SQL/0805.Creating a Connection and Cursor.mp4	10.4 MB
	08.Connecting Python and SQL/0806.Creating the 'predicted_outputs' table in MySQL.mp4	27.4 MB
	08.Connecting Python and SQL/0807.Running an SQL SELECT Statement from Python.mp4	12.5 MB
	08.Connecting Python and SQL/0808.Transferring Data from Jupyter to Workbench - Part I.mp4	45.4 MB
	08.Connecting Python and SQL/0809.Transferring Data from Jupyter to Workbench - Part II.mp4	32.1 MB
	08.Connecting Python and SQL/0810.Transferring Data from Jupyter to Workbench - Part III.mp4	22.4 MB
	09.Analyzing the Obtained data in Tableau/0901.Analysis in Tableau Age vs Probability.mp4	26.4 MB
	09.Analyzing the Obtained data in Tableau/0902.Analysis in Tableau Reasons vs Probability.mp4	30.2 MB
	09.Analyzing the Obtained data in Tableau/0903.Analysis in Tableau Transportation Expense vs Probability.mp4	67 MB
	Exercise Files/exercise_files.zip	12 MB

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