Tableau file types
0 likes273 views
The document discusses different file types used in Tableau for saving data analysis results. It describes .twb (Tableau workbook), .twbx (packaged workbook), .tds (data source), .tdsx (packaged data source), .tde (data extract), .tbm (bookmark), and .tps (preferences) file types used to save and distribute Tableau work. These files store things like workbook details, data connections, extracted data, and user preferences in XML format for sharing Tableau analyses.
1 of 11
Download to read offline
Ad
Recommended
Tableau
TableauNilesh Patel This document provides an overview of Tableau, a data visualization tool. It discusses what Tableau is, how it allows users to transform raw data into understandable visual formats without coding. It also covers the benefits of data visualization for decision making, customer relationships, and performance. The document outlines Tableau's product suite, advantages like handling large data and mobile support, disadvantages like report scheduling. It provides requirements for Tableau Desktop and Server and considers Tableau alternatives.
Data Warehouse Modeling
Data Warehouse Modelingvivekjv This document provides an overview of data warehousing concepts including dimensional modeling, online analytical processing (OLAP), and indexing techniques. It discusses the evolution of data warehousing, definitions of data warehouses, architectures, and common applications. Dimensional modeling concepts such as star schemas, snowflake schemas, and slowly changing dimensions are explained. The presentation concludes with references for further reading.
Introduction to Azure Data Factory
Introduction to Azure Data FactorySlava Kokaev Azure Data Factory is a cloud-based data integration service that orchestrates and automates the movement and transformation of data. In this session we will learn how to create data integration solutions using the Data Factory service and ingest data from various data stores, transform/process the data, and publish the result data to the data stores.
Date warehousing concepts
Date warehousing conceptspcherukumalla The document provides an overview of key concepts in data warehousing and business intelligence, including:
1) It defines data warehousing concepts such as the characteristics of a data warehouse (subject-oriented, integrated, time-variant, non-volatile), grain/granularity, and the differences between OLTP and data warehouse systems.
2) It discusses the evolution of business intelligence and key components of a data warehouse such as the source systems, staging area, presentation area, and access tools.
3) It covers dimensional modeling concepts like star schemas, snowflake schemas, and slowly and rapidly changing dimensions.
Microsoft Azure Data Factory Hands-On Lab Overview Slides
Microsoft Azure Data Factory Hands-On Lab Overview SlidesMark Kromer This document outlines modules for a lab on moving data to Azure using Azure Data Factory. The modules will deploy necessary Azure resources, lift and shift an existing SSIS package to Azure, rebuild ETL processes in ADF, enhance data with cloud services, transform and merge data with ADF and HDInsight, load data into a data warehouse with ADF, schedule ADF pipelines, monitor ADF, and verify loaded data. Technologies used include PowerShell, Azure SQL, Blob Storage, Data Factory, SQL DW, Logic Apps, HDInsight, and Office 365.
Azure data platform overview
Azure data platform overviewJames Serra This document provides an overview and summary of the author's background and expertise. It states that the author has over 30 years of experience in IT working on many BI and data warehouse projects. It also lists that the author has experience as a developer, DBA, architect, and consultant. It provides certifications held and publications authored as well as noting previous recognition as an SQL Server MVP.
Tableau Visual analytics complete deck 2
Tableau Visual analytics complete deck 2Arun K This document provides an overview of Visual Analytics Session 3. It discusses data joining and blending in Tableau. Specifically, it explains why joining or blending data is necessary when data comes from multiple sources. It then describes the different types of data joins in Tableau - inner joins, left joins, right joins, and outer joins. An example is provided to demonstrate an inner join using a primary key to connect related data between two tables. The goal is to understand how to connect different but related data sources in Tableau using common keys or variables.
Sql fundamentals
Sql fundamentalsRavinder Kamboj SQL is a language used to create, access, and manipulate data in databases. SQL statements are categorized into data definition language, data manipulation language, data control language, transaction control language, and embedded SQL statements. Data definition language statements define, alter, or drop database objects. Data manipulation language statements query or manipulate data in existing database objects. Data control language statements grant and revoke privileges to users. Transaction control language statements manage transactions of data manipulation language statements. Embedded SQL statements incorporate other SQL statements into procedural programs.
ETL in the Cloud With Microsoft Azure
ETL in the Cloud With Microsoft AzureMark Kromer Short introduction to different options for ETL & ELT in the Cloud with Microsoft Azure. This is a small accompanying set of slides for my presentations and blogs on this topic
Snowflake: The most cost-effective agile and scalable data warehouse ever!
Snowflake: The most cost-effective agile and scalable data warehouse ever!Visual_BI In this webinar, the presenter will take you through the most revolutionary data warehouse, Snowflake with a live demo and technical and functional discussions with a customer. Ryan Goltz from Chesapeake Energy and Tristan Handy, creator of DBT Cloud and owner of Fishtown Analytics will also be joining the webinar.
Azure Data Factory
Azure Data FactoryHARIHARAN R Azure Data Factory is a data integration service that allows for data movement and transformation between both on-premises and cloud data stores. It uses datasets to represent data structures, activities to define actions on data with pipelines grouping related activities, and linked services to connect to external resources. Key concepts include datasets representing input/output data, activities performing actions like copy, and pipelines logically grouping activities.
Azure Synapse Analytics Overview (r1)
Azure Synapse Analytics Overview (r1)James Serra Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Azure data factory
Azure data factoryBizTalk360 What are the evolving approaches to analytics? What is Azure Data Factory? Capabilities of Azure Data Factory
Moving to Databricks & Delta
Moving to Databricks & DeltaDatabricks At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Visualization using Tableau
Visualization using TableauGirija Muscut
The assistant panel present on the right side provides
guidance and suggestions for visualizations.
Status Bar:
The status bar present at the bottom provides information
about the current selection, filter, and other details.
Tableau Navigation
Dr.Girija Narasimhan 52
Tableau Desktop Workspace
Dr.Girija Narasimhan 53
The Tableau Desktop workspace consists of the following key components:
1. Data pane: Displays all the data sources, fields, sets, parameters etc. connected to the workbook.
2. Sheet pane: Displays all the sheets, dashboards, stories created in the workbook.
3. Worksheet: Area to build visual
Data Lake Architecture
Data Lake ArchitectureDATAVERSITY Data Lakes are meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
Building an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureJames Serra Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Tableau Desktop Material
Tableau Desktop MaterialKishore Chaganti This document provides information about Tableau, a data visualization software. It discusses Tableau's prerequisites, products, and architecture. Tableau allows users to easily connect to various data sources and transform data into interactive visualizations and dashboards. Key Tableau concepts covered include data sources, worksheets, dashboards, stories, filters, marks, color and size properties. The document also explains Tableau's desktop and server products, and the stages of importing data, analyzing it, and sharing results.
1- Introduction of Azure data factory.pptx
1- Introduction of Azure data factory.pptxBRIJESH KUMAR Azure Data Factory is a cloud-based data integration service that allows users to easily construct extract, transform, load (ETL) and extract, load, transform (ELT) processes without code. It offers job scheduling, security for data in transit, integration with source control for continuous delivery, and scalability for large data volumes. The document demonstrates how to create an Azure Data Factory from the Azure portal.
Azure Data Factory Introduction.pdf
Azure Data Factory Introduction.pdfMaheshPandit16 This document provides an overview of Azure Data Factory (ADF), including why it is used, its key components and activities, how it works, and differences between versions 1 and 2. It describes the main steps in ADF as connect and collect, transform and enrich, publish, and monitor. The main components are pipelines, activities, datasets, and linked services. Activities include data movement, transformation, and control. Integration runtime and system variables are also summarized.
Azure Synapse Analytics
Azure Synapse AnalyticsWinWire Technologies Inc Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
Intro to Delta Lake
Intro to Delta LakeDatabricks Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
Azure Data Factory presentation with links
Azure Data Factory presentation with linksChris Testa-O'Neill This is a slide deck that went through the fundamentals of Azure Data Factory. There are links to further information.
Data Lake Overview
Data Lake OverviewJames Serra The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Building Data Lakehouse.pdf
Building Data Lakehouse.pdfLuis Jimenez This document discusses building the data lakehouse, a new data architecture that combines the best of data warehouses and data lakes. It introduces the concept of the data lakehouse and compares it to traditional data warehouses and data lakes. The data lakehouse stores all types of data in an open, cloud-based environment and uses machine learning and analytics to deliver insights. It provides a single platform for various users and workloads, from data engineering to data science.
Tableau Visual Guidebook
Tableau Visual GuidebookAndy Kriebel This document provides tips for creating effective visualizations in Tableau, focusing on techniques for making visualizations useful, beautiful, and interactive. It discusses best practices such as asking a question to define the purpose of a visualization, choosing appropriate visual types, using dashboards to show multiple perspectives, and formatting visualizations for clarity and readability. Interactive features like filters, actions, and hyperlinks are also covered to help users understand and explore the data.
Delta Lake with Azure Databricks
Delta Lake with Azure DatabricksDustin Vannoy Dustin Vannoy presented on using Delta Lake with Azure Databricks. He began with an introduction to Spark and Databricks, demonstrating how to set up a workspace. He then discussed limitations of Spark including lack of ACID compliance and small file problems. Delta Lake addresses these issues with transaction logs for ACID transactions, schema enforcement, automatic file compaction, and performance optimizations like time travel. The presentation included demos of Delta Lake capabilities like schema validation, merging, and querying past versions of data.
Chapter 4 functions, views, indexing
Chapter 4 functions, views, indexingbaabtra.com - No. 1 supplier of quality freshers The document discusses user defined functions (UDF), views, and indexing in SQL Server. It provides an example of a UDF that returns a teacher's name based on their ID. Key differences between stored procedures and UDFs are that UDFs are compiled at runtime, can't perform DML operations, and must return a value. Views are described as customized representations of data from tables that don't take up storage space themselves. Indexing improves the speed of operations by organizing data to allow faster searches.
Sql server lesson3
Sql server lesson3Ala Qunaibi The document discusses configuring files and filegroups in SQL Server. It describes how SQL Server uses data files to store database contents and transaction log files to store transactions. It also discusses filegroups, which map database objects to files on disk. The document outlines the types of file extensions (.mdf, .ndf, .ldf) used and how the proportional fill algorithm works. It recommends best practices for configuring files and filegroups when creating a new database. The document also briefly discusses FILESTREAM, the tempdb database, and file naming conventions.
Ui path interview questions
Ui path interview questionsBalajihope The document discusses Tableau interview questions and answers related to context filters, disadvantages of context filters, Tableau products, data visualization, reasons for using Tableau, differences between heat maps and tree maps, and blended axes. Specifically, it provides definitions and explanations of context filters, lists five main Tableau products, defines data visualization, lists reasons for using Tableau including working with different data sources, and explains heat maps compare categories using color and size while tree maps illustrate hierarchical data relationships.
Ad
More Related Content
What's hot (20)
ETL in the Cloud With Microsoft Azure
ETL in the Cloud With Microsoft AzureMark Kromer Short introduction to different options for ETL & ELT in the Cloud with Microsoft Azure. This is a small accompanying set of slides for my presentations and blogs on this topic
Snowflake: The most cost-effective agile and scalable data warehouse ever!
Snowflake: The most cost-effective agile and scalable data warehouse ever!Visual_BI In this webinar, the presenter will take you through the most revolutionary data warehouse, Snowflake with a live demo and technical and functional discussions with a customer. Ryan Goltz from Chesapeake Energy and Tristan Handy, creator of DBT Cloud and owner of Fishtown Analytics will also be joining the webinar.
Azure Data Factory
Azure Data FactoryHARIHARAN R Azure Data Factory is a data integration service that allows for data movement and transformation between both on-premises and cloud data stores. It uses datasets to represent data structures, activities to define actions on data with pipelines grouping related activities, and linked services to connect to external resources. Key concepts include datasets representing input/output data, activities performing actions like copy, and pipelines logically grouping activities.
Azure Synapse Analytics Overview (r1)
Azure Synapse Analytics Overview (r1)James Serra Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Azure data factory
Azure data factoryBizTalk360 What are the evolving approaches to analytics? What is Azure Data Factory? Capabilities of Azure Data Factory
Moving to Databricks & Delta
Moving to Databricks & DeltaDatabricks At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Visualization using Tableau
Visualization using TableauGirija Muscut
The assistant panel present on the right side provides
guidance and suggestions for visualizations.
Status Bar:
The status bar present at the bottom provides information
about the current selection, filter, and other details.
Tableau Navigation
Dr.Girija Narasimhan 52
Tableau Desktop Workspace
Dr.Girija Narasimhan 53
The Tableau Desktop workspace consists of the following key components:
1. Data pane: Displays all the data sources, fields, sets, parameters etc. connected to the workbook.
2. Sheet pane: Displays all the sheets, dashboards, stories created in the workbook.
3. Worksheet: Area to build visual
Data Lake Architecture
Data Lake ArchitectureDATAVERSITY Data Lakes are meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
Building an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureJames Serra Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Tableau Desktop Material
Tableau Desktop MaterialKishore Chaganti This document provides information about Tableau, a data visualization software. It discusses Tableau's prerequisites, products, and architecture. Tableau allows users to easily connect to various data sources and transform data into interactive visualizations and dashboards. Key Tableau concepts covered include data sources, worksheets, dashboards, stories, filters, marks, color and size properties. The document also explains Tableau's desktop and server products, and the stages of importing data, analyzing it, and sharing results.
1- Introduction of Azure data factory.pptx
1- Introduction of Azure data factory.pptxBRIJESH KUMAR Azure Data Factory is a cloud-based data integration service that allows users to easily construct extract, transform, load (ETL) and extract, load, transform (ELT) processes without code. It offers job scheduling, security for data in transit, integration with source control for continuous delivery, and scalability for large data volumes. The document demonstrates how to create an Azure Data Factory from the Azure portal.
Azure Data Factory Introduction.pdf
Azure Data Factory Introduction.pdfMaheshPandit16 This document provides an overview of Azure Data Factory (ADF), including why it is used, its key components and activities, how it works, and differences between versions 1 and 2. It describes the main steps in ADF as connect and collect, transform and enrich, publish, and monitor. The main components are pipelines, activities, datasets, and linked services. Activities include data movement, transformation, and control. Integration runtime and system variables are also summarized.
Azure Synapse Analytics
Azure Synapse AnalyticsWinWire Technologies Inc Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
Intro to Delta Lake
Intro to Delta LakeDatabricks Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
Azure Data Factory presentation with links
Azure Data Factory presentation with linksChris Testa-O'Neill This is a slide deck that went through the fundamentals of Azure Data Factory. There are links to further information.
Data Lake Overview
Data Lake OverviewJames Serra The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Building Data Lakehouse.pdf
Building Data Lakehouse.pdfLuis Jimenez This document discusses building the data lakehouse, a new data architecture that combines the best of data warehouses and data lakes. It introduces the concept of the data lakehouse and compares it to traditional data warehouses and data lakes. The data lakehouse stores all types of data in an open, cloud-based environment and uses machine learning and analytics to deliver insights. It provides a single platform for various users and workloads, from data engineering to data science.
Tableau Visual Guidebook
Tableau Visual GuidebookAndy Kriebel This document provides tips for creating effective visualizations in Tableau, focusing on techniques for making visualizations useful, beautiful, and interactive. It discusses best practices such as asking a question to define the purpose of a visualization, choosing appropriate visual types, using dashboards to show multiple perspectives, and formatting visualizations for clarity and readability. Interactive features like filters, actions, and hyperlinks are also covered to help users understand and explore the data.
Delta Lake with Azure Databricks
Delta Lake with Azure DatabricksDustin Vannoy Dustin Vannoy presented on using Delta Lake with Azure Databricks. He began with an introduction to Spark and Databricks, demonstrating how to set up a workspace. He then discussed limitations of Spark including lack of ACID compliance and small file problems. Delta Lake addresses these issues with transaction logs for ACID transactions, schema enforcement, automatic file compaction, and performance optimizations like time travel. The presentation included demos of Delta Lake capabilities like schema validation, merging, and querying past versions of data.
Chapter 4 functions, views, indexing
Chapter 4 functions, views, indexingbaabtra.com - No. 1 supplier of quality freshers The document discusses user defined functions (UDF), views, and indexing in SQL Server. It provides an example of a UDF that returns a teacher's name based on their ID. Key differences between stored procedures and UDFs are that UDFs are compiled at runtime, can't perform DML operations, and must return a value. Views are described as customized representations of data from tables that don't take up storage space themselves. Indexing improves the speed of operations by organizing data to allow faster searches.
Similar to Tableau file types (20)
Sql server lesson3
Sql server lesson3Ala Qunaibi The document discusses configuring files and filegroups in SQL Server. It describes how SQL Server uses data files to store database contents and transaction log files to store transactions. It also discusses filegroups, which map database objects to files on disk. The document outlines the types of file extensions (.mdf, .ndf, .ldf) used and how the proportional fill algorithm works. It recommends best practices for configuring files and filegroups when creating a new database. The document also briefly discusses FILESTREAM, the tempdb database, and file naming conventions.
Ui path interview questions
Ui path interview questionsBalajihope The document discusses Tableau interview questions and answers related to context filters, disadvantages of context filters, Tableau products, data visualization, reasons for using Tableau, differences between heat maps and tree maps, and blended axes. Specifically, it provides definitions and explanations of context filters, lists five main Tableau products, defines data visualization, lists reasons for using Tableau including working with different data sources, and explains heat maps compare categories using color and size while tree maps illustrate hierarchical data relationships.
Tableau Interview Questions
Tableau Interview QuestionsBalajihope The document discusses Tableau interview questions and answers related to context filters, disadvantages of context filters, Tableau products, data visualization, reasons for using Tableau, differences between heat maps and tree maps, and blended axes. Specifically, it provides definitions and explanations of context filters, lists five main Tableau products, defines data visualization, lists reasons for using Tableau including working with different data sources, and explains heat maps compare categories using color and size while tree maps illustrate hierarchical data relationships.
Database concepts
Database conceptsFLYMAN TECHNOLOGY LIMITED This document provides an introduction to database concepts. It defines what a database is and gives examples. It also defines key database terminology like data, fields, records, tables, and relationships. Additionally, it discusses the history of databases from manual systems to file processing systems to modern database management systems. DBMSs allow for centralized, organized data storage and retrieval and overcome many of the limitations of earlier data storage methods. The document also briefly describes different types of database models and relational database concepts like tables, primary keys, and foreign keys.
Sql Server Basics
Sql Server Basicsrainynovember12 The document provides an overview of basic concepts related to SQL server databases including database objects, file systems, storage structures, and query processing. It discusses topics like SQL server databases, storage files and file groups, data pages and extents, data organization in heaps vs indexed tables, and how queries are processed through either full table scans or using indexes.
Tableau interview questions-ppt
Tableau interview questions-pptMayank Kumar
Tableau software is a basic requirement for any business to gain insight into the development of the company. It allows any non-technical user to easily create or develop the customized dashboards that facilitate insight into a broad spectrum of information. It is a must know interactive business intelligence tool in the field of data visualization.
Database fundamentals
Database fundamentalsThen Murugeshwari This document discusses the basics of database management systems (DBMS). It begins by explaining the data storage hierarchy from the bit level up to the database level. It then covers different database models including hierarchical, network, relational, and object-oriented. Key components of a DBMS like DDL, DML, query language, and report generators are defined. Commercial DBMS examples are provided. The document concludes with an overview of creating and using a database, including defining the structure, entering data, and searching for information.
SAS - Training 
SAS - Training Bilal Mazhar MS(IS)Cyber Security II Privacy Professional This document provides an overview of the Statistical Analysis System (SAS) software. It discusses what SAS is used for, including data management, statistical analysis, reporting, and more. It also covers SAS components and their usage, how to install and use the SAS studio interface, basic SAS syntax like variables and data sets, and common statistical procedures in SAS like PROC MEANS, PROC FREQ, and PROC UNIVARIATE to produce measures, frequencies and graphs.
Data Wrangling and Visualization Using Python
Data Wrangling and Visualization Using PythonMOHITKUMAR1379 Python is open source and has so many libraries for data wrangling and visualization that makes life of data scientists easier. For data wrangling pandas is used as it represent tabular data and it has other function to parse data from different sources, data cleaning, handling missing values, merging data sets etc. To visualize data, low level matplotlib can be used. But it is a base package for other high level packages such as seaborn, that draw well customized plot in just one line of code. Python has dash framework that is used to make interactive web application using python code without javascript and html. These dash application can be published on any server as well as on clouds like google cloud but freely on heroku cloud.
MuleSoft Surat Virtual Meetup#30 - Flat File Schemas Transformation With Mule...
MuleSoft Surat Virtual Meetup#30 - Flat File Schemas Transformation With Mule...Jitendra Bafna This document provides information about an upcoming meetup on transforming flat file schemas with MuleSoft. The meetup will be held on December 21st, 2021 from 9:00-10:00 PM IST and will feature a presentation by Jitendra Bafna on the topic. The meetup organizers are Nitish Jain and Jitendra Bafna. The agenda includes introductions, an overview of CSV format and flat file definitions, and demonstrations of reading single and multiple segment flat files and using streaming mode in DataWeave. Attendees are encouraged to provide feedback and can ask questions during the meetup session.
New
Newchew kok meng The document discusses data dictionaries, which collect and define data terms to provide documentation and eliminate redundancy. A data dictionary includes information about the data maintained by a system. It relates to data flow diagrams by defining the categories of data flows, structures, elements and stores. It also provides examples of how to describe data flows, structures, elements, stores and the relationships between them. The data dictionary is used to generate code, create screens/reports, and analyze system design.
Abap faq
Abap faqKranthi Kumar This document provides definitions and explanations of key concepts in ABAP (Advanced Business Application Programming) and SAP. It defines terms like master data, transactional data, workflow, cost objects, and G/L accounts. It also explains database tables, views, matchcodes, locking, and the data dictionary. The data dictionary manages data definitions and ensures data integrity. Views combine data from multiple tables without duplicating it physically. Matchcodes and locking help control concurrent access to data.
Ijebea14 228
Ijebea14 228Iasir Journals The document discusses the database design for an online examination application developed using SAP ABAP. It describes three main types of database tables used: transparent tables that store data directly with a one-to-one mapping to database tables; pooled tables that store small amounts of control data combined into a single database table to reduce resources; and cluster tables that store data from a few large related tables combined into a single database table. It then notes the online exam application database uses three main tables to store candidate registration details, exam/question details, and results, in addition to system-generated tables.
introduction-stata.pptx
introduction-stata.pptxJacob Pratabaraj The document provides an introduction to using Stata for data analysis. It discusses navigating the Stata interface, importing different types of data files, exploring and managing data, and performing basic statistical analysis. Key features of Stata mentioned include its ease of use, powerful statistical analysis and data management tools, and consistent command syntax. The document also provides examples of commands for viewing, selecting, and summarizing data in Stata.
Dbms
Dbmsharleenmahajan 1. The document discusses how data is organized in a database system using a hierarchy from the bit level up to files, records, fields, and databases.
2. It describes some problems with traditional file-based data storage like redundancy, inconsistency, and lack of flexibility. A database addresses these issues through centralization of data.
3. The key components of a database system are described as people, hardware, software, and data. The database management system (DBMS) acts as an interface between users, applications, and the stored data.
database concepts.pptx
database concepts.pptxslavskrillex A database is a collection of organized data that can be easily accessed, managed, and updated. Data is organized into tables with rows and columns for efficient searching and retrieval. A database management system (DBMS) is software that stores, maintains, and utilizes the database. Databases reduce data redundancy, improve accuracy through validation rules, facilitate easy data updating and sharing, and ensure security and integrity. The relational data model organizes data into relations or tables with rows representing records and columns representing attributes.
6967176.ppt
6967176.pptThemovieCorner This document provides an overview and summary of the key features of the statistical software package SPSS (Statistical Package for the Social Sciences). It describes how SPSS can be used to perform statistical analysis and data management. The summary includes descriptions of the data editor, viewer, pivot tables, database access, transformations, and other core features. Procedures in SPSS can be accomplished through dialog boxes and menus using point and click functionality.
Ad
More from Learnbay Datascience (20)
Top data science projects
Top data science projectsLearnbay Datascience This presentation educates you about top data science project ideas for Beginner, Intermediate and Advanced. the ideas such as Fake News Detection Using Python, Data Science Project on, Detecting Forest Fire, Detection of Road Lane Lines, Project on Sentimental Analysis, Speech Recognition, Developing Chatbots, Detection of Credit Card Fraud and Customer Segmentations etc:
For more topics stay tuned with Learnbay.
Python my SQL - create table
Python my SQL - create tableLearnbay Datascience This presentation educate you about how to create table using Python MySQL with example syntax and Creating a table in MySQL using python.
For more topics stay tuned with Learnbay.
Python my SQL - create database
Python my SQL - create databaseLearnbay Datascience This presentation educates you about Python MySQL - Create Database and Creating a database in MySQL using python with sample program.
For more topics stay tuned with Learnbay.
Python my sql database connection
Python my sql database connectionLearnbay Datascience This presentation educates you about Python MySQL - Database Connection, Python MySQL - Database Connection, Establishing connection with MySQL using python with sample program.
For more topics stay tuned with Learnbay.
Python - mySOL
Python - mySOLLearnbay Datascience This document discusses how to install and use the mysql-connector-python package to connect to a MySQL database from Python. It provides instructions on installing Python and PIP if needed, then using PIP to install the mysql-connector-python package. It also describes verifying the installation by importing the mysql.connector module in a Python script without errors.
AI - Issues and Terminology
AI - Issues and TerminologyLearnbay Datascience This presentation educates you about AI - Issues and the types of issue, AI - Terminology with its list of frequently used terms in the domain of AI.
For more topics stay tuned with Learnbay.
AI - Fuzzy Logic Systems
AI - Fuzzy Logic SystemsLearnbay Datascience This presentation educates you about AI - Fuzzy Logic Systems and its Implementation, Why Fuzzy Logic?, Why Fuzzy Logic?, Membership Function, Example of a Fuzzy Logic System and its Algorithm.
For more topics stay tuned with Learnbay.
AI - working of an ns
AI - working of an nsLearnbay Datascience This presentation educates you about AI - Working of ANNs, Machine Learning in ANNs, Back Propagation Algorithm, Bayesian Networks (BN), Building a Bayesian Network and Gather Relevant Information of Problem.
For more topics stay tuned with Learnbay.
Artificial Intelligence- Neural Networks
Artificial Intelligence- Neural NetworksLearnbay Datascience This presentation educates you about AI- Neural Networks, Basic Structure of ANNs with a sample of ANN and Types of Artificial Neural Networks are Feedforward and Feedback.
For more topics stay tuned with Learnbay.
AI - Robotics
AI - RoboticsLearnbay Datascience This presentation educates you about Artificial Intelligence - Robotics, What is Robotics?, Difference in Robot System and Other AI Program, Robot Locomotion, Components of a Robot and Applications of Robotics.
For more topics stay tuned with Learnbay.
Applications of expert system
Applications of expert systemLearnbay Datascience This presentation educates you about Applications of Expert System, Expert System Technology, Development of Expert Systems: General Steps and Benefits of Expert Systems.
For more topics stay tuned with Learnbay.
Components of expert systems
Components of expert systemsLearnbay Datascience This presentation educates you about AI - Components and Acquisition of Expert Systems and those are Knowledge Base, Knowledge Base and User Interface, AI - Expert Systems Limitation.
For more topics stay tuned with Learnbay.
Artificial intelligence - expert systems
Artificial intelligence - expert systemsLearnbay Datascience This presentation educates you about AI - Expert Systems, Characteristics of Expert Systems, Capabilities of Expert Systems and Components of Expert Systems.
For more topics stay tuned with Learnbay.
AI - natural language processing
AI - natural language processingLearnbay Datascience This presentation educates you about AI - Natural Language Processing, Components of NLP (NLU and NLG), Difficulties in NLU and NLP Terminology and steps of NLP.
For more topics stay tuned with Learnbay.
Ai popular search algorithms
Ai popular search algorithmsLearnbay Datascience This presentation educates you about AI - Popular Search Algorithms, Single Agent Pathfinding Problems, Search Terminology, Brute-Force Search Strategies, Breadth-First Search and Depth-First Search with example chart.
For more topics stay tuned with Learnbay.
AI - Agents & Environments
AI - Agents & EnvironmentsLearnbay Datascience This presentation educates you about AI - Agents & Environments, Agent Terminology, Rationality, What is Ideal Rational Agent?, The Structure of Intelligent Agents and Properties of Environment.
For more topics stay tuned with Learnbay.
Artificial intelligence - research areas
Artificial intelligence - research areasLearnbay Datascience This presentation educates you about Artificial Intelligence - Research Areas, Speech and Voice Recognition., Working of Speech and Voice Recognition Systems and Real Life Applications of Research Areas.
For more topics stay tuned with Learnbay.
Artificial intelligence composed
Artificial intelligence composedLearnbay Datascience This presentation educates you about Artificial intelligence composed and those are Reasoning, Learning, Problem Solving, Perception and Linguistic Intelligence.
For more topics stay tuned with Learnbay.
Artificial intelligence intelligent systems
Artificial intelligence intelligent systemsLearnbay Datascience This presentation educates you about Artificial Intelligence - Intelligent Systems, Types of Intelligence, Linguistic intelligence, Musical intelligence, Logical-mathematical intelligence, Spatial intelligence, Bodily-Kinesthetic intelligence, Intra-personal intelligence and Interpersonal intelligence.
For more topics stay tuned with Learnbay.
Applications of ai
Applications of aiLearnbay Datascience This presentation educates you about Applications of Artificial Intelligence such as Intelligent Robots, Handwriting Recognition, Speech Recognition, Vision Systems and so more.
For more topics stay tuned with Learnbay.
Ad
Recently uploaded (20)
How to Subscribe Newsletter From Odoo 18 Website
How to Subscribe Newsletter From Odoo 18 WebsiteCeline George Newsletter is a powerful tool that effectively manage the email marketing . It allows us to send professional looking HTML formatted emails. Under the Mailing Lists in Email Marketing we can find all the Newsletter.
To study the nervous system of insect.pptx
To study the nervous system of insect.pptxArshad Shaikh The *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responThe *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responses*: Insects can exhibit complex behaviors, such as mating, foraging, and social interactions.
Characteristics
1. *Decentralized*: Insect nervous systems have some autonomy in different body parts.
2. *Specialized*: Different parts of the nervous system are specialized for specific functions.
3. *Efficient*: Insect nervous systems are highly efficient, allowing for rapid processing and response to stimuli.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive in diverse environments.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive
Ultimate VMware 2V0-11.25 Exam Dumps for Exam Success
Ultimate VMware 2V0-11.25 Exam Dumps for Exam SuccessMark Soia Boost your chances of passing the 2V0-11.25 exam with CertsExpert reliable exam dumps. Prepare effectively and ace the VMware certification on your first try
Quality dumps. Trusted results. — Visit CertsExpert Now: https://www.certsexpert.com/2V0-11.25-pdf-questions.html
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...Library Association of Ireland
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...Library Association of Ireland
Odoo Inventory Rules and Routes v17 - Odoo Slides
Odoo Inventory Rules and Routes v17 - Odoo SlidesCeline George Odoo's inventory management system is highly flexible and powerful, allowing businesses to efficiently manage their stock operations through the use of Rules and Routes.
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulse
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulsesushreesangita003 what is pulse ?
Purpose
physiology and Regulation of pulse
Characteristics of pulse
factors affecting pulse
Sites of pulse
Alteration of pulse
for BSC Nursing 1st semester
for Gnm Nursing 1st year
Students .
vitalsign
Geography Sem II Unit 1C Correlation of Geography with other school subjects
Geography Sem II Unit 1C Correlation of Geography with other school subjectsProfDrShaikhImran The correlation of school subjects refers to the interconnectedness and mutual reinforcement between different academic disciplines. This concept highlights how knowledge and skills in one subject can support, enhance, or overlap with learning in another. Recognizing these correlations helps in creating a more holistic and meaningful educational experience.
apa-style-referencing-visual-guide-2025.pdf
apa-style-referencing-visual-guide-2025.pdfIshika Ghosh Title: A Quick and Illustrated Guide to APA Style Referencing (7th Edition)
This visual and beginner-friendly guide simplifies the APA referencing style (7th edition) for academic writing. Designed especially for commerce students and research beginners, it includes:
✅ Real examples from original research papers
✅ Color-coded diagrams for clarity
✅ Key rules for in-text citation and reference list formatting
✅ Free citation tools like Mendeley & Zotero explained
Whether you're writing a college assignment, dissertation, or academic article, this guide will help you cite your sources correctly, confidently, and consistent.
Created by: Prof. Ishika Ghosh,
Faculty.
📩 For queries or feedback: [email protected]
Understanding P–N Junction Semiconductors: A Beginner’s Guide
Understanding P–N Junction Semiconductors: A Beginner’s GuideGS Virdi Dive into the fundamentals of P–N junctions, the heart of every diode and semiconductor device. In this concise presentation, Dr. G.S. Virdi (Former Chief Scientist, CSIR-CEERI Pilani) covers:
What Is a P–N Junction? Learn how P-type and N-type materials join to create a diode.
Depletion Region & Biasing: See how forward and reverse bias shape the voltage–current behavior.
V–I Characteristics: Understand the curve that defines diode operation.
Real-World Uses: Discover common applications in rectifiers, signal clipping, and more.
Ideal for electronics students, hobbyists, and engineers seeking a clear, practical introduction to P–N junction semiconductors.
YSPH VMOC Special Report - Measles Outbreak Southwest US 5-3-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 5-3-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC) A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 817 from Texas, New Mexico, Oklahoma, and Kansas. 97 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003.
The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include:
- Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources.
- Accessibility: Designed for easy reading, wide distribution, and interactive use.
- Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.
CURRENT CASE COUNT: 817 (As of 05/3/2025)
• Texas: 688 (+20)(62% of these cases are in Gaines County).
• New Mexico: 67 (+1 )(92.4% of the cases are from Eddy County)
• Oklahoma: 16 (+1)
• Kansas: 46 (32% of the cases are from Gray County)
HOSPITALIZATIONS: 97 (+2)
• Texas: 89 (+2) - This is 13.02% of all TX cases.
• New Mexico: 7 - This is 10.6% of all NM cases.
• Kansas: 1 - This is 2.7% of all KS cases.
DEATHS: 3
• Texas: 2 – This is 0.31% of all cases
• New Mexico: 1 – This is 1.54% of all cases
US NATIONAL CASE COUNT: 967 (Confirmed and suspected):
INTERNATIONAL SPREAD (As of 4/2/2025)
• Mexico – 865 (+58)
‒Chihuahua, Mexico: 844 (+58) cases, 3 hospitalizations, 1 fatality
• Canada: 1531 (+270) (This reflects Ontario's Outbreak, which began 11/24)
‒Ontario, Canada – 1243 (+223) cases, 84 hospitalizations.
• Europe: 6,814
UNIT 3 NATIONAL HEALTH PROGRAMMEE. SOCIAL AND PREVENTIVE PHARMACY
UNIT 3 NATIONAL HEALTH PROGRAMMEE. SOCIAL AND PREVENTIVE PHARMACYDR.PRISCILLA MARY J NATIONAL HEALTH PROGRAMMEE
Quality Contril Analysis of Containers.pdf
Quality Contril Analysis of Containers.pdfDr. Bindiya Chauhan Quality control test for containers, rubber closures and secondary packing materials.
Political History of Pala dynasty Pala Rulers NEP.pptx
Political History of Pala dynasty Pala Rulers NEP.pptxArya Mahila P. G. College, Banaras Hindu University, Varanasi, India. The Pala kings were people-protectors. In fact, Gopal was elected to the throne only to end Matsya Nyaya. Bhagalpur Abhiledh states that Dharmapala imposed only fair taxes on the people. Rampala abolished the unjust taxes imposed by Bhima. The Pala rulers were lovers of learning. Vikramshila University was established by Dharmapala. He opened 50 other learning centers. A famous Buddhist scholar named Haribhadra was to be present in his court. Devpala appointed another Buddhist scholar named Veerdeva as the vice president of Nalanda Vihar. Among other scholars of this period, Sandhyakar Nandi, Chakrapani Dutta and Vajradatta are especially famous. Sandhyakar Nandi wrote the famous poem of this period 'Ramcharit'.
Presentation of the MIPLM subject matter expert Erdem Kaya
Presentation of the MIPLM subject matter expert Erdem KayaMIPLM Presentation of the MIPLM subject matter expert Erdem Kaya
How to Manage Opening & Closing Controls in Odoo 17 POS
How to Manage Opening & Closing Controls in Odoo 17 POSCeline George In Odoo 17 Point of Sale, the opening and closing controls are key for cash management. At the start of a shift, cashiers log in and enter the starting cash amount, marking the beginning of financial tracking. Throughout the shift, every transaction is recorded, creating an audit trail.
Unit 6_Introduction_Phishing_Password Cracking.pdf
Unit 6_Introduction_Phishing_Password Cracking.pdfKanchanPatil34 Initial stages of attacks, Phishing, Password Cracking, guidelines to prevent password attacks, Strong password, weak password,
How to Set warnings for invoicing specific customers in odoo
How to Set warnings for invoicing specific customers in odooCeline George Odoo 16 offers a powerful platform for managing sales documents and invoicing efficiently. One of its standout features is the ability to set warnings and block messages for specific customers during the invoicing process.
Introduction to Vibe Coding and Vibe Engineering
Introduction to Vibe Coding and Vibe EngineeringDamian T. Gordon Introduction to Vibe Coding and Vibe Engineering
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...Library Association of Ireland
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...Library Association of Ireland
YSPH VMOC Special Report - Measles Outbreak Southwest US 5-3-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 5-3-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC)
Political History of Pala dynasty Pala Rulers NEP.pptx
Political History of Pala dynasty Pala Rulers NEP.pptxArya Mahila P. G. College, Banaras Hindu University, Varanasi, India.
Tableau file types
- 1. Swipe Tableau - File Types Tableau
- 2. The result of data analysis in Tableau can be saved in various formats, to be saved and distributed. The various formats are referred as different file types and they are identified by different extensions. Their formats depend on how they are produced and for what purposes they are used. They are all stored as XML files, which can be opened and edited. Tableau - File Types
- 3. Lists the description of each file type Tableau Workbook twb Tableau Packaged Workbook .twbx Tableau Data Source .tds Tableau Packaged Data source .tdsx Tableau Data Extract .tde Tableau Bookmark tbm Tableau Preferences .tps
- 4. It contains information on each sheet and dashboard that is present in a workbook. It has the details of the fields, which are used in each view and the formula applied to the aggregation of the measures. It also has the formatting and styles applied. It contains the data source connection information and any metadata information created for that connection. Tableau Workbook
- 5. This file format contains the details of a workbook as well as the local data that is used in the analysis. Its purpose is to share with other Tableau desktop or Tableau reader users, assuming it does not need data from the server. Tableau Packaged Workbook
- 6. The details of the connection used to create the tableau report are stored in this file. In the connection details, it stores the source type (excel/relational/sap, etc.) as well as the data types of the columns. Tableau Data Source
- 7. This file is similar to the .tds file with the addition of data along with the connection details. .tdsx Tableau Packaged Data source
- 8. This file contains the data used in a .twb file in a highly compressed columnar data format. This helps in storage optimization. It also saves the aggregated calculations that are applied in the analysis. This file should be refreshed to get the updated data from the source. Tableau Data Extract
- 9. These files contain a single worksheet that is shared easily to be pasted into other workbooks. .tps Tableau Bookmark
- 10. This file stores the color preference used across all the workbooks. It is mainly used for consistent look and feel across the users. Tableau Preferences
- 11. Tableau - Data Types Tableau - Data Terminology Tableau - Operators Stay Tuned with Topics for next Post