DOCSLIB.ORG

Affymetrix Data Mining Tool Manual

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Affymetrix Data Mining Tool Manual Affymetrix® Data Mining Tool User’s Guide Version 3.0 For Research Use Only. Not for use in diagnostic procedures. Affymetrix Confidential 700233 Rev. 3 Trademarks Affymetrix®, GeneChip®, EASI™, ™,, ™, HuSNP™, GenFlex™, Jaguar™, MicroDB™, 417™, 418™, 427™, 428™, Pin-and-Ring™, Flying Objective™, NetAffx™ and CustomExpress™ are trademarks owned or used by Affymetrix, Inc. Microsoft® is a registered trademark of Microsoft Corporation. Oracle® is a registered trademark of Oracle Corporation. Limited License PROBE ARRAYS, INSTRUMENTS, SOFTWARE AND REAGENTS ARE LICENSED FOR RESEARCH USE ONLY AND NOT FOR USE IN DIAGNOSTIC PROCEDURES. NO RIGHT TO MAKE, HAVE MADE, OFFER TO SELL, SELL, OR IMPORT OLIGONUCLEOTIDE PROBE ARRAYS OR ANY OTHER PRODUCT IN WHICH AFFYMETRIX HAS PATENT RIGHTS IS CONVEYED BY THE SALE OF PROBE ARRAYS, INSTRUMENTS, SOFTWARE, OR REAGENTS HEREUNDER. THIS LIMITED LICENSE PERMITS ONLY THE USE OF THE PARTICULAR PRODUCT(S) THAT THE USER HAS PURCHASED FROM AFFYMETRIX. Patents Software products may be covered by one or more of the following patents: U.S. Patent Nos. 5,733,729; 5,795,716; 5,974,164; 6,066,454; 6,090,555; 6,185,561 and 6,188,783; and other U.S. or foreign patents. Copyright ©1999, 2001 Affymetrix, Inc. All rights reserved. Contents CHAPTER 1 Welcome 3 Data Mining Tool User’s Guide 3 What’s New in DMT 3.0 3 Conventions Used 4 On-line Documentation 5 Technical Support 6 Your Feedback is Welcome 6 CHAPTER 2 Installing Data Mining Tool 3.0 9 Before You Begin 9 Microsoft® SQL Server LIMS Users 9 Oracle® LIMS Users 9 MicroDB™ Users 9 Installing Data Mining Tool 10 Creating an Oracle® Alias 17 Oracle 8.1.7 Alias Configuration 17 CHAPTER 3 Affymetrix® Data Mining Tool Overview 25 Access Data 25 Affymetrix Publish Database 25 Affymetrix® Analysis Data Model 26 i ii Contents DMT Windowpanes 27 Query Data 30 Building and Running a Query 30 Viewing Query Results 33 Tables 33 Graphs 38 Analyze Query Results 43 Statistical Analyses 43 Cluster Analysis 43 Matrix Analysis 46 CHAPTER 4 Getting Started 49 Starting DMT 49 Managing Database Connections 50 Registering a Database 51 Unregistering a Database 52 Selecting a Database 53 Specifying the Default Directory 54 CHAPTER 5 Building and Running a Query 59 Building a Query 59 Starting a New Query 59 Specifying the Filters 61 Query Builder 68 Selecting Analyses for the Query 70 Specifying Analysis Filters 70 Running a Query 79 Affymetrix® Data Mining Tool User’s Guide iii Normalizing GeneChip® Signal Data 79 Choosing Normalization Before a Query or Pivot 80 Choosing Normalization After a Query or Pivot 81 Normalization Options 81 CHAPTER 6 Managing Queries 87 Saving a Query 87 Using the Save As Command 88 Opening a Previously Saved Query 89 Deleting a Query 90 CHAPTER 7 Query Results Tables 93 Experiment Information Table 93 GeneChip® Data Mode 94 Spot Data Mode 95 Query Table 96 Pivot Data Table 97 Selecting Results for the Pivot Table 99 Running the Pivot Operation 101 Including Probe Descriptions in the Pivot Table 102 Including Annotations in the Pivot Table 102 Sorting Pivot Table Columns 103 Pivot Options 104 Working with Tables 106 Finding Probes 106 Viewing Descriptions & Obtaining Further Gene Information 107 Annotating Probes 108 Adding Probes to the Filter Grid 109 iv Contents Copying Tables 110 Exporting Data 111 Expanding the Results Pane 111 Clearing the Results Pane 112 CHAPTER 8 Annotations 115 Annotating Probes 115 Loading Annotations 116 Querying Annotations 118 Adding Probes to the Filter Grid 121 Deleting Annotations 122 CHAPTER 9 Probe Lists 127 Creating Probe Lists 127 Creating a Probe List from the Query or Pivot Table 128 Creating a Probe List from Cluster Analysis 130 Creating a Probe List from Search Array Descriptions 131 Creating a Probe List from Filter 132 Creating a Probe List by Combining Existing Lists 132 Loading a Probe List 134 Specifying Probe List Members 134 Specifying an Input File 135 Using Probe Lists 137 Adding a Probe List to the Filter Grid 137 Displaying Selected Probe List Members 138 Managing Probe Lists 140 Viewing and Editing Probe List Members 140 Combining Probe Lists 142 Affymetrix® Data Mining Tool User’s Guide v Exporting a Probe List 143 Deleting a Probe List 144 CHAPTER 10 Array Sets 149 Creating an Array Set 149 Working with Array Sets 151 Viewing Array Sets 151 Managing Array Sets 152 Editing an Array Set 152 Deleting an Array Set 153 CHAPTER 11 Graphing Results 157 Scatter Graph 158 Plotting the Scatter Graph 158 Working with the Scatter Graph 161 Scatter Graph Options 168 Fold Change Graph 171 Plotting the Fold Change Graph 173 Working with the Fold Change Graph 176 Fold Change Graph Options 183 Series Graph 185 Plotting the Series Graph 186 Working with the Series Graph 188 Series Graph Options 191 Histogram 193 Plotting the Histogram 193 Working with the Histogram 195 Histogram Options 199 vi Contents Other Graphing Features 202 Enlarging the Graph Pane 202 Changing Graph Colors 202 Copying and Clearing Graphs 204 Printing Graphs 204 CHAPTER 12 Statistical Analyses 209 Selecting an Operator 209 Average, Median, Standard Deviation or Inter-Quartile Range 210 Fold Change 212 T-Test 214 Mann-Whitney Test 216 Count & Percentage 218 CHAPTER 13 Matrix Analysis 223 Overview 223 Population Size 224 Running a Matrix Analysis 225 CHAPTER 14 Cluster Analysis 231 Self Organizing Map (SOM) Algorithm 231 Running a SOM Cluster Analysis 232 Saving a Probe List 237 SOM Filters 238 SOM Parameters 239 Affymetrix® Data Mining Tool User’s Guide vii Correlation Coefficient Clustering Algorithm 240 Running the Correlation Coefficient Cluster 241 Correlation Coefficient Clustering Options 244 Effect of Changing Algorithm Parameters 246 Saving and Importing Seed Patterns 248 Saving a Probe List 251 CHAPTER 15 DMT Tutorial 255 Introduction 255 Step 1: Restoring the MicroDB™ Database 256 Step 2: Starting DMT 256 Step 3: Registering the Database 256 Step 4: Selecting the Tutorial Database 258 Step 5: Opening the DMT Session 258 Lesson 1: Identifying Highly Expressed Genes 259 Step 1: Specifying a Filter 259 Step 2: Selecting Analyses for the Query 260 Step 3: Pivoting on Signal & Detection Call 260 Step 4: Querying and Pivoting the Data 262 Step 5: Sorting the Pivot Table by Signal 263 Step 6: Saving a Probe List 263 Step 7: Plotting the Series Line Graph 264 Lesson 1 Summary 268 Suggested Exercise 269 Lesson 2: Calculating Averages of Replicates 270 Step 1: Specifying a Probe List for the Filter 270 Step 2: Selecting Analyses for the Query 272 Step 3: Pivoting on Signal 273 Step 4: Query and Pivot the Data 274 Step 5: Selecting Average & Standard Deviation Operators 276 Step 6: Sorting the Pivot Table 279 viii Contents Step 7: Displaying Probe Set Descriptions 280 Lesson 2 Summary 281 Suggested Exercise 281 Lesson 3: Summarizing Qualitative Data 282 Step 1: Pivoting on Detection Call 282 Step 2: Performing Count & Percentage Analysis 284 Step 3: Sorting Pivot Table Results 286 Step 4: Saving a Probe List 287 Step 5: Annotating Probe List Members 287 Lesson 3 Summary 288 Suggested Exercise 288 Lesson 4: Evaluating Difference Between Two Tissues 289 Step 1: Pivoting on Signal 290 Step 2: Mann-Whitney Test 292 Step 3: Annotating Probe Sets 295 Step 4: Saving a Probe List 295 Lesson 4 Summary 296 Suggested Exercise 296 Lesson 5: Evaluating Change Call Consistency 297 Step 1: Clearing the Filter Grid & Selecting Comparison Analyses 299 Step 2: Pivoting on Difference Call 300 Step 3: Comparison Ranking 301 Step 4: Annotating Probe Sets 303 Step 5: Saving a Probe List 304 Lesson 5 Summary 304 Suggested Exercise 304 Lesson 6: Self Organizing Map (SOM) Cluster Analysis 305 Step 1: Clearing the Filter Grid & Selecting Analyses 306 Step 2: Pivoting on Signal 307 Step 3: Computing Average Signal 308 Step 4: SOM Cluster Analysis 310 Affymetrix® Data Mining Tool User’s Guide ix Step 5: Saving & Annotating a Probe List 318 Lesson 6 Summary 318 APPENDIX A Filter Grid 323 GeneChip Data Mode 323 Statistical Expression Algorithm 323 Empirical Expression Algorithm 324 Spot Data Mode 330 APPENDIX B Working with Windows & Tables 333 Query Windowpanes 333 Expanding a Windowpane 333 Resizing a Windowpane 333 Clearing the Results or Graph Pane 334 Tables 334 Selecting the Entire Table 334 Selecting Rows 334 Resizing Columns 335 Hiding Columns 335 Reordering Columns 336 APPENDIX C Query Table Data 339 GeneChip® Data Mode 339 Statistical Expression Algorithm Metrics 339 Empirical Expression Algorithm Metrics 340 Spot Data Mode 346 x Contents APPENDIX D DMT Algorithms 349 The SOM Algorithm 349 Neighborhood 351 Learning Rate 352 The Correlation Coefficient Clustering Algorithm 353 The Matrix Algorithm 354 APPENDIX E Toolbars & Shortcuts 359 DMT Main Toolbar 359 Session Toolbar 360 Shortcut Descriptions 361 1 Chapter 1 Welcome 1 Welcome to the Affymetrix® Data Mining Tool (DMT) User’s Guide. The DMT filters, queries and analyzes publish databases of GeneChip® or spotted array expression data. Data Mining Tool User’s Guide This manual explains how to use DMT to: I Build a query. I Display the query results in table or graph format. I Evaluate and compare replicate data using statistical analyses. I Calculate the overlap significance between two lists of GeneChip® probe sets or spot probes. I Apply cluster analysis to experimental results to help identify gene expression patterns. This manual also includes a tutorial that demonstrates; 1) a data mining strategy to identify genes that significantly change expression level, 2) statistical analyses of replicate data, and 3) cluster analysis. What’s New in DMT 3.0 Compatible with Microarray Suite Statistical or Empirical Expression Algorithm DMT can query and analyze experimental results generated by the Statistical Expression algorithm (in Microarray Suite 5.0) as well as the Empirical Expression algorithm (in versions of Microarray Suite prior to 5.0).
Load more

Recommended publications
  • (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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Business Intelligence Tools
    How Commercial Off-the-Shelf (COTS) Business Intelligence (BI) Tools Can Improve Financial Management Analysis Carolyn L. North, CPA, CGFM, CDFM/A, PMP R. Ross Hosse, CDFM Purpose of Business Intelligence Consolidate complex data into simple displays Engility Proprietary Information 2 Situational Awareness • Know your audience – Immediate group – Commander • Consider your tool sets – Boss hates canned report – Boss loves bubble charts Engility Proprietary Information 3 Tool Sets • Excel • Accounting Reports • Business Intelligence Tools Engility Proprietary Information 4 Creating Data Sets • Certain reports, exported spreadsheets, or pivot tables have blank cells – Easy for reading – Unsuitable for importing into other systems or Excel • Steps to make more useful – Download data – Remove any subtotals or totals – Remove blanks – Ensure each column has a title • Example • https://www.whitehouse.gov/omb/budget/Suppleme ntal – Table 9 Pivot Table Engility Proprietary Information 5 Creating Data Sets - Blanks • Filling in blank space – Select entire data range – Press Ctrl + G – Displays the “Go To” dialog – Choose Special – In Paste Special dialog, choose “Blanks”. Click OK – Type equals sign (=), then the up arrow – Ctrl + Enter – Select entire data range – Ctrl C to copy – Paste dropdown, select values Engility Proprietary Information 6 VLOOKUP Function • VLOOKUP function – Searches first column of a range of cells – Returns value from any cell on the same row of the range – 1st Step is to create your lookup table – Ensure table is sorted
    [Show full text]
  • Pivot Tables
    PIVOT TABLES In data processing, a pivot table is a data summarization tool found in data visualization programs such as spreadsheets or business intelligence software. Among other functions, a pivot-table can automatically sort, count total or give the average of the data stored in one table or spreadsheet. It displays the results in a second table (called a "pivot table") showing the summarized data. Pivot tables can be seen as a simplification of the more complete and complex. Pivot tables are my best friend. GIS ANAYSIS NEEDED: The Panther Focus Area represents lands that are crucial for the Florida panther’s survival. Currently, the breeding population of the Florida panther is limited to south of the Caloosahatchee River. As urban encroachment increases and the sea levels rise, the FL panther is expected to naturally migrate to the Expansion Zone. Information Needed Who are the large landowners by County? How many acres do they own? How much of their land is currently protected? • Expansion Zone • Expansion Zone • Protected Lands • Expansion Zone • Protected Lands • Parcel Data Four Counties Charlotte Glades Highlands Lee 4,670 Parcels 2,281 Charlotte 2,083 Glades 137 Highlands 169 Lee 2,066 Owners 981 Charlotte 976 Glades 85 Highlands 30 Lee Protected 717 Protected 28 Partially 3,925 Unprotected “COUNTY” = ‘Charlotte’ and “PROTECTED” = ‘Protected” “COUNTY” = ‘Charlotte’ and “PROTECTED” = ‘Partially Protected” “COUNTY” = ‘Charlotte’ and “PROTECTED” = ‘Unprotected” 4 Counties x (protected, partially protected, and unprotected)
    [Show full text]
  • Appendix D: How to Use a Data Spreadsheet: Excel
    Appendix D How to Use a Data Spreadsheet: Excel ne does not necessarily have special statistical software to perform statistical analyses. Microsoft Office Excel can be used to run statistical procedures. Although in some respects Excel is not as preferable for data analyses as IBM SPSS, it is very user- friendly with simpler statistical procedures. This appendix describes how to use Excel to execute basic statistical calculations. Data from the 2004 version of the General Social Survey (GSS) is used for examples. OThis Appendix is based on Excel 2007 version, which differs in certain aspects from Excel’s previous versions. The most notable change that affects the exercises presented in this appendix concerns the pivot table feature. 22BASIC PROCEDURES Starting Excel: To start Excel using Windows, click on the Start button at the bottom left corner of the screen. Under Programs locate and click the Microsoft Excel icon. The layout of the Excel program has changed substantially for the Microsoft Office 2007 edition compared to its predeces- sors. Commands are now grouped in ribbons that are accessed by clicking on a specific tab. Thus, the Home tab grants access to a ribbon of several command groups: Clipboard, Font, Alignment, Number, Styles, Cells, and Editing. Once the program is started you will see a Worksheet Area that consists of cells forming columns and rows. Rows are identi- fied by numbers, and columns are identified by letters. Consequently, each cell has its own unique address—a combination of letters and numbers. For example, cell C6 is in column C, row6. The dark rim around a cell means that the cell is highlighted or active.
    [Show full text]
  • The Benefits of Data Modeling in Business Intelligence
    WHITE PAPER: THE BENEFITS OF DATA MODELING IN BUSINESS INTELLIGENCE The Benefits of Data Modeling in Business Intelligence DECEMBER 2008 Table of Contents Executive Summary 1 SECTION 1 2 Introduction 2 SECTION 2 2 Why Data Modeling for BI Is Unique 2 SECTION 3 4 Understanding the Meaning of Information 4 SECTION 4 7 Supporting Reporting Needs 7 SECTION 5 8 Conclusion 8 Copyright © 2008 CA. All rights reserved. All trademarks, trade names, service marks and logos referenced herein belong to their respective companies. This document is for your informational purposes only. To the extent permitted by applicable law, CA provides this document “As Is” without warranty of any kind, including, without limitation, any implied warranties of merchantability or fitness for a particular purpose, or noninfringement. In no event will CA be liable for any loss or damage, direct or indirect, from the use of this document, including, without limitation, lost profits, business interruption, goodwill or lost data, even if CA is expressly advised of such damages. Page 1 Executive Summary CHALLENGES Business intelligence (BI) is critical to many organizations today. Faced with ever- growing amounts of data, the challenge is to make sense of this data and unlock information that is useful and relevant to the business. A data model is a valuable communication tool to ensure that database developers understand and meet the needs of the business in the physical database system. Challenges include: Understanding the meaning of key business terms. Ensuring that reporting needs are met so that users can create flexible queries using the correct information.
    [Show full text]
  • Data Warehousing
    Data Warehousing Jens Teubner, TU Dortmund [email protected] Winter 2014/15 © Jens Teubner · Data Warehousing · Winter 2014/15 1 Part IV Modelling Your Data © Jens Teubner · Data Warehousing · Winter 2014/15 38 Business Process Measurements Want to store information about business processes. ! Store “business process measurement events” Example: Retail sales ! Could store information like: date/time, product, store number, promotion, customer, clerk, sales dollars, sales units, … ! Implies a level of detail, or grain. Observe: These stored data have different flavors: Ones that refer to other entities, e.g., to describe the context of the event (e.g., product, store, clerk) (; dimensions) Ones that look more like “measurement values” (sales dollars, sales units) (; facts or measures) © Jens Teubner · Data Warehousing · Winter 2014/15 39 Business Process Measurements Events A flat table view of the events could look like State City Quarter Sales Amount California Los Angeles Q1/2013 910 California Los Angeles Q2/2013 930 California Los Angeles Q3/2013 925 California Los Angeles Q4/2013 940 California San Francisco Q1/2013 860 California San Francisco Q2/2013 885 California San Francisco Q3/2013 890 California San Francisco Q4/2013 910 . © Jens Teubner · Data Warehousing · Winter 2014/15 40 Analysis Business people are used to analyzing such data using pivot tables in spreadsheet software. © Jens Teubner · Data Warehousing · Winter 2014/15 41 OLAP Cubes Data cubes are alternative views on such data. Facts: points in the k-dimensional space Aggregates on sides and edges of the cube would clerk make this a “k-dimensional pivot table”. date product © Jens Teubner · Data Warehousing · Winter 2014/15 42 OLAP Cubes for Analytics More advanced analyses: “slice and dice” the cube.
    [Show full text]
  • Microsoft Excel • Pivottables • Dashboards 3
    Excel Masterclass to Digitise your Business Pivot Tables and building a dashboard Tim Parle 1 July 2020 Agenda 1. Brief overview of spreadsheets in the market 2. Theory: • Brief introduction to Microsoft Excel • PivotTables • Dashboards 3. Practical examples • Simple PivotTables • Advanced PivotTales • PivotCharts • Dashboard Spreadsheet: The first ‘killer app’? The other ground breakers….. Spreadsheet programmes Microsoft Excel Calligra Sheets Google Sheets NeoOffice OpenOffice.org Calc LibreOffice Calc Gnumeric Kingsoft Market shares: Excel versus Google Sheets Source: https://medium.com/grid-spreadsheets-run-the-world/excel-vs-google- sheets-usage-nature-and-numbers-9dfa5d1cadbd Microsoft Excel: Introduction and overview Microsoft Excel is a spreadsheet application that was first launched by Microsoft Corporation in 1985. In order to perform mathematical functions on the data, the program organizes the data into columns and rows. This can then be manipulated through formulas that allow users to input and analyze large sets of data. The uses of Microsoft Excel are practically limitless - especially when you combine it with the accompanying Office Suite Programs. Source: https://excelsemipro.com/2019/10/history-of-microsoft-exce/ Popular Features of Microsoft Excel Top 10 features of Microsoft Excel to improve your ability to analyze data for your personal use or for your business: 1. Efficiently model and analyze almost any data 2. Zero in on the right data points quickly 3. Create data charts in a single cell 4. Access your spreadsheets from virtually anywhere 5. Connect, share, and accomplish more when working together 6. Take advantage of more interactive and dynamic PivotCharts 7. Add more sophistication to your data presentations 8.
    [Show full text]
  • Teaching Tip a Teaching Module of Database-Centric Online Analytical Process for MBA Business Analytics Programs
    Journal of Information Volume 30 Systems Issue 1 Education Winter 2019 Teaching Tip A Teaching Module of Database-Centric Online Analytical Process for MBA Business Analytics Programs Shouhong Wang and Hai Wang Recommended Citation: Wang, S. & Wang, H. (2019). Teaching Tip: A Teaching Module of Database-Centric Online Analytical Process for MBA Business Analytics Programs. Journal of Information Systems Education, 30(1), 19-26. Article Link: http://jise.org/Volume30/n1/JISEv30n1p19.html Initial Submission: 4 January 2018 Accepted: 26 June 2018 Abstract Posted Online: 4 December 2018 Published: 13 March 2019 Full terms and conditions of access and use, archived papers, submission instructions, a search tool, and much more can be found on the JISE website: http://jise.org ISSN: 2574-3872 (Online) 1055-3096 (Print) Journal of Information Systems Education, Vol. 30(1) Winter 2019 Teaching Tip A Teaching Module of Database-Centric Online Analytical Process for MBA Business Analytics Programs Shouhong Wang Charlton College of Business University of Massachusetts – Dartmouth Dartmouth, MA 02747, USA [email protected] Hai Wang Sobey School of Business Saint Mary’s University Halifax, NS B3H 2W3, Canada [email protected] ABSTRACT Business schools are increasingly establishing MBA business analytics programs. This article discusses the importance of a sufficient body of knowledge about databases for MBA business analytics students. It presents the pedagogical design and the teaching method of a module of database-centric OLAP (online analytical process) for an MBA business analytics course when a standalone database course is infeasible for the MBA business analytics program. The teaching module includes key database concepts for business analytics, a tutorial on database-centric OLAP, and a database-centric OLAP exercise assignment.
    [Show full text]