Google Sheet Pivot Table Multiple Sheets
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Google Docs Accessibility (Pdf)
Google Docs Accessibility (A11y) Building Accessible Google Docs • Heading Styles • Images • Table of Contents • Captioning • Columns and Lists • Tables A11y • Tab Stops • Color Contrast • Paragraph Spacing • Headers and Footers • Meaningful Link Text • Accessibility Checker What is Assistive Technology? Assistive Technology (AT) are “products, equipment, and systems that enhance learning, working, and daily living for persons with disabilities.” Magnification Speech Screen Readers Software Recognition Trackball Mouse Keyboard Zoom Text Braille Computer Keyboard Captions/Subtitles Captioned Telephone Video Relay Services Captioning Videos Per federal and state law, and CSU policy, instructional media (e.g., videos, captured lectures, recorded presentations) must have captions. This includes instructional media used in classrooms, posted on websites or shared in Canvas. • All students who are enrolled in a course must be able to access the content in the course. • Faculty: Funding is available to help faculty generate captions and transcripts for instructional media. Materials should be submitted at least six weeks in advance of their use in instruction. • Staff: For CSUN staff who do not provide classroom material, there is a cost through chargeback. For information on the chargeback, email [email protected]. csun.edu/captioning What are Screen Readers Screen readers are a form of assistive technology (AT) software that enables access to a computer, and all the things a computer does, by attempting to identify and interpret what is being displayed on the computer screen using text-to-speech. Screen readers can only access and process live text (fully editable or selectable text). • Provides access to someone who is visually impaired, mobility or has a learning disability to access text on the screen. -
(BI) Using MS Excel Powerpivot
2018 ASCUE Proceedings Developing an Introductory Class in Business Intelligence (BI) Using MS Excel Powerpivot Dr. Sam Hijazi Trevor Curtis Texas Lutheran University 1000 West Court Street Seguin, Texas 78130 [email protected] Abstract Asking questions about your data is a constant application of all business organizations. To facilitate decision making and improve business performance, a business intelligence application must be an in- tegral part of everyday management practices. Microsoft Excel added PowerPivot and PowerPivot offi- cially to facilitate this process with minimum cost, knowing that many business people are already fa- miliar with MS Excel. This paper will design an introductory class to business intelligence (BI) using Excel PowerPivot. If an educator decides to adopt this paper for teaching an introductory BI class, students should have previ- ous familiarity with Excel’s functions and formulas. This paper will focus on four significant phases all students need to complete in a three-credit class. First, students must understand the process of achiev- ing small database normalization and how to bring these tables to Excel or develop them directly within Excel PowerPivot. This paper will walk the reader through these steps to complete the task of creating the normalization, along with the linking and bringing the tables and their relationships to excel. Sec- ond, an introduction to Data Analysis Expression (DAX) will be discussed. Introduction It is not that difficult to realize the increase in the amount of data we have generated in the recent memory of our existence as a human race. To realize that more than 90% of the world’s data has been amassed in the past two years alone (Vidas M.) is to realize the need to manage such volume. -
Calculated Field in Pivot Table Data Model
Calculated Field In Pivot Table Data Model Frostbitten and unjaundiced Eddie always counteracts d'accord and cowl his tana. New-fashioned and goniometrically,anarchical Ronny however never swop potentiometric his belittlement! Torre enunciatedRevolved Gordan harmonically tunneling or beat-up. or gimlets some doxologies In regular Pivot Tables, you can group numeric, data or text fields. Product of Reliable Bioreactors on Site. Here are exclusive to model in pivot calculated field table data model that data model and used when creating pivot. Power Query, Data model, DAX, Filters, Slicers, Conditional formats and beautiful charts. Eg if you are counting customers that have purchased and have years on rows. Why is this last part important? Depending on the source of data, relationships may or may not be created when the model is initially set up. This data is provided by Microsoft for informational purposes only as an aid to illustrate a concept. To use and limitations and share some limitations of calculated field in pivot table data model. Yeah, good points Derek. Date field, and use it to show a count of orders. Ins menu in the model in pivot calculated field list table that i mentioned earlier, we shall not. Please start a new test to continue. Displays all of the values in each column or series as a percentage of the total for the column or series. This is used to present users with ads that are relevant to them according to the user profile. Note: use the Insert Item button to quickly insert items when you type a formula. -
Profiles Research Networking Software Installation Guide
Profiles Research Networking Software Installation Guide Documentation Version : July 25, 2014 Software Version : ProfilesRNS_2.1.0 Table of Contents Introduction ..................................................................................................................... 2 Hardware and Operating System Requirements ............................................................. 3 Download Options ........................................................................................................... 4 Installing the Database .................................................................................................... 5 Loading Person Data....................................................................................................... 8 Loading Person Data: Part 1 – Importing SSIS Packages into SQL Server msdb Database ..................................................................................................................... 8 Loading Person Data: Part 2 – Importing Demographic Data .................................... 10 Loading Person Data: Part 3 – Geocoding ................................................................ 15 Loading Person Data: Part 4 – Obtaining Publications .............................................. 16 Loading Person Data: Part 5 – Convert data to RDF ................................................. 19 Scheduling Database Jobs ............................................................................................ 21 Installing the Code........................................................................................................ -
A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering
1 A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering Ahmad Abdellatif, Khaled Badran, Diego Elias Costa, and Emad Shihab, Senior Member, IEEE Abstract—Chatbots are envisioned to dramatically change the future of Software Engineering, allowing practitioners to chat and inquire about their software projects and interact with different services using natural language. At the heart of every chatbot is a Natural Language Understanding (NLU) component that enables the chatbot to understand natural language input. Recently, many NLU platforms were provided to serve as an off-the-shelf NLU component for chatbots, however, selecting the best NLU for Software Engineering chatbots remains an open challenge. Therefore, in this paper, we evaluate four of the most commonly used NLUs, namely IBM Watson, Google Dialogflow, Rasa, and Microsoft LUIS to shed light on which NLU should be used in Software Engineering based chatbots. Specifically, we examine the NLUs’ performance in classifying intents, confidence scores stability, and extracting entities. To evaluate the NLUs, we use two datasets that reflect two common tasks performed by Software Engineering practitioners, 1) the task of chatting with the chatbot to ask questions about software repositories 2) the task of asking development questions on Q&A forums (e.g., Stack Overflow). According to our findings, IBM Watson is the best performing NLU when considering the three aspects (intents classification, confidence scores, and entity extraction). However, the results from each individual aspect show that, in intents classification, IBM Watson performs the best with an F1-measure>84%, but in confidence scores, Rasa comes on top with a median confidence score higher than 0.91. -
Connected Cl As Sroom
Data in the Cloud he ability to move from one representation monthly temperatures for cities in the United of data to another is one of the key char- States, South America, and Russia. Tacteristics of expert mathematicians and A quick inspection of the graph makes it evi- scientists. Cloud computing will offer more dent that on average it is always colder in Verk- opportunities to create and display multiple hoyansk than in Washington (and much colder representations of data, making this skill in the winter) and that the seasons in the south- even more important in the future. ern hemisphere are reversed. These patterns would have been much more difficult to discern Multiple Representations in a table of numbers alone. We can represent data in a variety of forms— Even young students can explore multiple rep- graphs, charts, tables of numbers, equations. resentations of the same data with software such Mathematicians, scientists, and engineers often as The Graph Club (Tom Snyder Productions). look for patterns in data. Different representations This application allows students to view two of the same data sometimes make it easier to see linked representations of data simultaneously. As a pattern. For example, the pattern in the table children drag icons to form a picture graph or of numbers below is not immediately evident. enter numbers in a simple table, a corresponding bar graph or pie chart takes shape (see Figure 2). Changing the size of a sector in the circle graph or the height of a bar in a bar chart Table 1. changes the pictogram or vice versa. -
Voice.AI Gateway One-Click Dialogflow Integration Guide
Integration Guide AudioCodes Intuitive Human Communications for Chatbot Services Voice.AI Gateway Google One-Click DialogFlow Integration Integration Guide Contents Table of Contents 1 Introduction ......................................................................................................... 7 1.1 Purpose .................................................................................................................. 7 2 Messages Sent by Voice.AI Gateway ................................................................ 9 2.1 Welcome Message ................................................................................................. 9 3 Messages Sent by Agent .................................................................................. 11 3.1 Basic Activity Syntax ............................................................................................ 11 3.2 hangup activity ..................................................................................................... 11 3.3 Bot Framework Specific Details ............................................................................ 12 3.3.1 Google Dialogflow ....................................................................................................12 Voice Bot Solutions 3 Voice.AI Gateway One-Click Dialogflow List of Tables Table 2-1: Description of Initial Message Sent by Voice.AI Gateway ...................................................... 9 Table 3-1: Properties of JSON Object Activities.....................................................................................11 -
Sharing Files with Microsoft Office Users
Sharing Files with Microsoft Office Users Title: Sharing Files with Microsoft Office Users: Version: 1.0 First edition: November 2004 Contents Overview.........................................................................................................................................iv Copyright and trademark information........................................................................................iv Feedback.................................................................................................................................... iv Acknowledgments......................................................................................................................iv Modifications and updates......................................................................................................... iv File formats...................................................................................................................................... 1 Bulk conversion............................................................................................................................... 1 Opening files....................................................................................................................................2 Opening text format files.............................................................................................................2 Opening spreadsheets..................................................................................................................2 Opening presentations.................................................................................................................2 -
The Ultimate Guide to Google Sheets Everything You Need to Build Powerful Spreadsheet Workflows in Google Sheets
The Ultimate Guide to Google Sheets Everything you need to build powerful spreadsheet workflows in Google Sheets. Zapier © 2016 Zapier Inc. Tweet This Book! Please help Zapier by spreading the word about this book on Twitter! The suggested tweet for this book is: Learn everything you need to become a spreadsheet expert with @zapier’s Ultimate Guide to Google Sheets: http://zpr.io/uBw4 It’s easy enough to list your expenses in a spreadsheet, use =sum(A1:A20) to see how much you spent, and add a graph to compare your expenses. It’s also easy to use a spreadsheet to deeply analyze your numbers, assist in research, and automate your work—but it seems a lot more tricky. Google Sheets, the free spreadsheet companion app to Google Docs, is a great tool to start out with spreadsheets. It’s free, easy to use, comes packed with hundreds of functions and the core tools you need, and lets you share spreadsheets and collaborate on them with others. But where do you start if you’ve never used a spreadsheet—or if you’re a spreadsheet professional, where do you dig in to create advanced workflows and build macros to automate your work? Here’s the guide for you. We’ll take you from beginner to expert, show you how to get started with spreadsheets, create advanced spreadsheet-powered dashboard, use spreadsheets for more than numbers, build powerful macros to automate your work, and more. You’ll also find tutorials on Google Sheets’ unique features that are only possible in an online spreadsheet, like built-in forms and survey tools and add-ons that can pull in research from the web or send emails right from your spreadsheet. -
Fast Foreign-Key Detection in Microsoft SQL
Fast Foreign-Key Detection in Microsoft SQL Server PowerPivot for Excel Zhimin Chen Vivek Narasayya Surajit Chaudhuri Microsoft Research Microsoft Research Microsoft Research [email protected] [email protected] [email protected] ABSTRACT stored in a relational database, which they can import into Excel. Microsoft SQL Server PowerPivot for Excel, or PowerPivot for Other sources of data are text files, web data feeds or in general any short, is an in-memory business intelligence (BI) engine that tabular data range imported into Excel. enables Excel users to interactively create pivot tables over large data sets imported from sources such as relational databases, text files and web data feeds. Unlike traditional pivot tables in Excel that are defined on a single table, PowerPivot allows analysis over multiple tables connected via foreign-key joins. In many cases however, these foreign-key relationships are not known a priori, and information workers are often not be sophisticated enough to define these relationships. Therefore, the ability to automatically discover foreign-key relationships in PowerPivot is valuable, if not essential. The key challenge is to perform this detection interactively and with high precision even when data sets scale to hundreds of millions of rows and the schema contains tens of tables and hundreds of columns. In this paper, we describe techniques for fast foreign-key detection in PowerPivot and experimentally evaluate its accuracy, performance and scale on both synthetic benchmarks and real-world data sets. These techniques have been incorporated into PowerPivot for Excel. Figure 1. Example of pivot table in Excel. It enables multi- dimensional analysis over a single table. -
Building an Effective Data Warehousing for Financial Sector
Automatic Control and Information Sciences, 2017, Vol. 3, No. 1, 16-25 Available online at http://pubs.sciepub.com/acis/3/1/4 ©Science and Education Publishing DOI:10.12691/acis-3-1-4 Building an Effective Data Warehousing for Financial Sector José Ferreira1, Fernando Almeida2, José Monteiro1,* 1Higher Polytechnic Institute of Gaya, V.N.Gaia, Portugal 2Faculty of Engineering of Oporto University, INESC TEC, Porto, Portugal *Corresponding author: [email protected] Abstract This article presents the implementation process of a Data Warehouse and a multidimensional analysis of business data for a holding company in the financial sector. The goal is to create a business intelligence system that, in a simple, quick but also versatile way, allows the access to updated, aggregated, real and/or projected information, regarding bank account balances. The established system extracts and processes the operational database information which supports cash management information by using Integration Services and Analysis Services tools from Microsoft SQL Server. The end-user interface is a pivot table, properly arranged to explore the information available by the produced cube. The results have shown that the adoption of online analytical processing cubes offers better performance and provides a more automated and robust process to analyze current and provisional aggregated financial data balances compared to the current process based on static reports built from transactional databases. Keywords: data warehouse, OLAP cube, data analysis, information system, business intelligence, pivot tables Cite This Article: José Ferreira, Fernando Almeida, and José Monteiro, “Building an Effective Data Warehousing for Financial Sector.” Automatic Control and Information Sciences, vol. -
Recovering Semantics of Tables on the Web
Recovering Semantics of Tables on the Web Petros Venetis Alon Halevy Jayant Madhavan Marius Pas¸ca Stanford University Google Inc. Google Inc. Google Inc. [email protected] [email protected] [email protected] [email protected] Warren Shen Fei Wu Gengxin Miao Chung Wu Google Inc. Google Inc. UC Santa Barbara Google Inc. [email protected] [email protected] [email protected] [email protected] ABSTRACT away. Furthermore, unlike text documents, where small changes The Web offers a corpus of over 100 million tables [6], but the in the document structure or wording do not correspond to vastly meaning of each table is rarely explicit from the table itself. Header different content, variations in table layout or terminology change rows exist in few cases and even when they do, the attribute names the semantics significantly. In addition to table search, knowing are typically useless. We describe a system that attempts to recover the semantics of tables is also necessary for higher-level operations the semantics of tables by enriching the table with additional anno- such as combining tables via join or union. tations. Our annotations facilitate operations such as searching for In principle, we would like to associate semantics with each table tables and finding related tables. in the corpus, and use the semantics to guide retrieval, ranking and To recover semantics of tables, we leverage a database of class table combination. However, given the scale, breadth and hetero- labels and relationships automatically extracted from the Web. The geneity of the tables on the Web, we cannot rely on hand-coded database of classes and relationships has very wide coverage, but domain knowledge.