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Power BI Desktop: Build Data Model, Get Data, DAX Formulas, Visualizations, Publish 2 Web (EMT 1366)
 
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Download File: http://people.highline.edu/mgirvin/excelisfun.htm Excel Magic Trick 1366 Full Lesson on Power BI Desktop to build Product Analysis for Gross Profit with Average, Standard Deviation, Coefficient of Variation and Histogram Calculations and Visualizations: 1. (00:04) Files to download 2. (00:12) Introduction 3. (04:42) Import Related Tables from Access 4. (05:42) Edit automatic Relationships Bi-directional Filtering to Single-directional Filtering 5. (07:22) Import Text Files From Folder 6. (08:36) Filter out file extensions that are NOT Text .txt 7. (09:38) Use “Combine Binary” Icon to combine Text Files into one table 8. (10:40) Look at “Combine Binary”: Query Creation Steps, including M Code and Power Query Function that is automatically created 9. (12:23) Change Data Types in fSales (Fact Sales) Table 10. (13:23) edit Relationship between fSales Product Table 11. (14:14) Create Calendar Table in Excel 12. (18:33) Create Frequency Distribution Category Table in Excel using Text Formula 13. (21:39) Import tables from Excel File 14. (22:52) Manually Create Relationships Between Tables 15. (23:40) Create DAX Calculated Column for Net Revenue using the RELATED function (works like VLOOKUP Exact Match in Excel) & ROUND function. Net Revenue values are stored in the “In RAM Memory” Data Model 16. (25:40) Discuss Convention for using Columns in formulas: ALWAYS USE TABLE NAME AND COLUMN/FIELD NAME IN SQUARE BRACKETS 17. (26:24) Look at How REALTED works across relationships 18. (27:07) Discussion of Row Context 19. (29:25) Create Measure for Total Revenue. This Measure is a Measure that is based on values in a Calculated Column 20. (31:15) Add Number Format to Measure so that every time the Measure is used the Number Format will appear 21. (31:53) Learn about Measures that are not dependent on Calculated Columns. See how to create Measure that does not use a Calculated Column as a source for values. UseSUMX function 22. (34:59) and (36:40) Compare creating: 1) Measures based on Calculated Columns and or Measures not based on Calculated 23. (35:39) and (42:40) Discussion of Filter Context and how it helps DAX formulas calculate Quickly on Big Data. Filter Context: When a Conditions or Criteria are selected from the Lookup Tables (Dimension Tables) they flow across the Relationships from the One-Side to the Many-Side to Filter the Fact Table down to a smaller size so that the formulas have to work over a smaller data set 24. (36:52) and (37:52) Discussion of how values created in Calculated Colum are stored in the Data Model Columnar Database and this uses RAM Memory 25. (38:54) When you must use a Calculated Column: When you need to extend the data set and add a column that has Conditions or Criteria that you want to use to Filter the Data Set 26. (40:06) Create Calculated Column For COGS using ROUND and RELATED Functions 27. (41:50) Create Calculated Column for Gross Profit 28. (43:35) Create Calculated Column on fSales Table that will create the Sales Categories “Retail” or “Wholesale” using IF & OR functions. Because it creates Criteria that will use as Filters for our Measures, This DAX formula can only be created using a Calculated Column, not a Measure 29. (46:00) Measure for Total COGS 30. (46:36) Measure for Total Gross Profit 31. (47:20) Measure for Gross Profit Percentage. This is a Ratio of two numbers. This is an example of a Measure that can ONLY be created as a Measure. It cannot be created as a Measure based on a Calculated Column 32. (48:35) Discuss Convention for using Measures in other Measures: USE SQUARE BRACKETS ONLY around the Measure name 33. (49:52) Measure for Average (Mean) Gross Profit 34. (50:20) Measure for Standard Deviation of the Gross Profit 35. (51:09) Measure for Coefficient of Variation of the Gross Profit 36. (52:43) Hide Unnecessary Columns from Report View 37. (53:01) Sort Month Name Column by Month Number 38. (54:19) Sort Category Column By Lower Limit 39. (55:25) Add Data Category Image URL for Image File Paths 40. (57:10) Create DAX Column to simulate Approximate Match Lookup using the FLOOR function 41. (59:54) Manually Create Relationship For Category Table 42. (01:00:18) Update Excel Table and Test to see if Power BI Report Updates when we Refresh 43. (01:01:57) Create Product Analysis Visualization with the first visualization: Create Table with Product Pictures and Metrics. This is Page one of our Power BI Report. 44. (01:03:13) Create Bar Chart For Mean and Standard Deviation of Gross Profit 45. (01:03:39) Create Slicers to Filter Visualizations 46. (01:04:11) Create Frequency Distribution Table & Measure to Count Transactions 47. (01:05:35) Format Table, Chart and Slicers 48. (01:07:45) Create second Page in Power BI Report with Product Revenue and COGS by Year & Month 49. (01:09:05) Publish Power BI Report online 50. (01:10:37) Generate Embed code for e-mailing Report and for embedding in web sites 51. (01:11:38) Summary
Views: 159344 ExcelIsFun
How to Import Data, Copy Data from Excel to R: .csv & .txt Formats (R Tutorial 1.5)
 
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Learn how to import or copy data from excel (or other spreadsheets) into R using both comma-separated values and tab-delimited text file. You will learn to use "read.csv", "read.delim" and "read.table" commands along with "file.choose", "header", and "sep" arguments. This video is a tutorial for programming in R Statistical Software for beginners. You can access the dataset here: our website: http://www.statslectures.com/index.php/r-stats-videos-tutorials/getting-started-with-r/1-3-import-excel-data or here: Excel Data Used in This Video: http://bit.ly/1uyxR3O Excel Data Used in Subsequent Videos: https://bit.ly/LungCapDataxls Tab Delimited Text File Used in Subsequent Videos: https://bit.ly/LungCapData Here is a quick overview of the topics addressed in this video; click on time stamps to jump to a specific topic: 0:00:17 the two main file types for saving a data file 0:00:36 how to save a file in excel as a csv file ("comma-separated value") 0:01:10 how to open a comma-separated (.csv) data file into excel 0:01:20 how to open a comma-separated (.csv) data file into a text editor 0:01:36 how to import comma-separated (.csv) data file into R using "read.csv" command 0:01:44 how to access the help menu for different commands in R 0:02:04 how to use "file.choose" argument on "read.csv" command to specify the file location in R 0:02:31 how to use the "header" argument on "read.csv" command to let R know that data has headers or variable names 0:03:22 how to import comma-separated (.csv) data file into R using "read.table" command 0:03:38 how to use "file.choose" argument on "read.table" command to specify the file location in R 0:03:41 how to use the "header" argument on "read.table" command to let R know the data has headers or variable names 0:03:46 how to use the "sep" argument on "read.table" command to let R know how the data values are separated 0:04:10 how to save a file in excel as tab-delimited text file 0:04:50 how to open a tab-delimited (.txt) data file into a text editor 0:05:07 how to open a tab-delimited (.txt) data file into excel 0:05:20 how to import tab-delimited (.txt) data file into R using "read.delim" command 0:05:44 how to use "file.choose" argument on "read.delim" command to specify the file path in R 0:05:49 how to use the "header" argument on "read.delim" command to let R know that the data has headers or variable 0:06:06 how to import tab-delimited (.txt) data file into R using "read.table" command 0:06:20 how to use "file.choose" argument on "read.table" command to specify the file location 0:06:23 how to use the "header" argument on "read.table" command to let R know that the data has headers or variable names 0:06:27 how to use the "sep" argument on "read.table" command to let R know how the data values are separated
Views: 491422 MarinStatsLectures
Import Data and Analyze with Python
 
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Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 177710 APMonitor.com
Importing Data into R - How to import csv and text files into R
 
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In this video you will learn how to import your flat files into R. Want to take the interactive coding exercises and earn a certificate? Join DataCamp today, and start our intermediate R tutorial for free: https://www.datacamp.com/courses/importing-data-into-r In this first chapter, we'll start with flat files. They're typically simple text files that contain table data. Have a look at states.csv, a flat file containing comma-separated values. The data lists basic information on some US states. The first line here gives the names of the different columns or fields. After that, each line is a record, and the fields are separated by a comma, hence the name comma-separated values. For example, there's the state Hawaii with the capital Honolulu and a total population of 1.42 million. What would that data look like in R? Well, actually, the structure nicely corresponds to a data frame in R, that ideally looks like this: the rows in the data frame correspond to the records and the columns of the data frame correspond to the fields. The field names are used to name the data frame columns. But how to go from the CSV file to this data frame? The mother of all these data import functions is the read.table() function. It can read in any file in table format and create a data frame from it. The number of arguments you can specify for this function is huge, so I won't go through each and every one of these arguments. Instead, let's have a look at the read.table() call that imports states.csv and try to understand what happens. The first argument of the read.table() function is the path to the file you want to import into R. If the file is in your current working directory, simply passing the filename as a character string works. If your file is located somewhere else, things get tricky. Depending on the platform you're working on, Linux, Microsoft, Mac, whatever, file paths are specified differently. To build a path to a file in a platform-independent way, you can use the file.path() function. Now for the header argument. If you set this to TRUE, you tell R that the first row of the text file contains the variable names, which is the case here. read.table() sets this argument FALSE by default, which would mean that the first row is already an observation. Next, sep is the argument that specifies how fields in a record are separated. For our csv file here, the field separator is a comma, so we use a comma inside quotes. Finally, the stringsAsFactors argument is pretty important. It's TRUE by default, which means that columns, or variables, that are strings, are imported into R as factors, the data structure to store categorical variables. In this case, the column containing the country names shouldn't be a factor, so we set stringsAsFactors to FALSE. If we actually run this call now, we indeed get a data frame with 5 observations and 4 variables, that corresponds nicely to the CSV file we started with. The read table function works fine, but it's pretty tiring to specify all these arguments every time, right? CSV files are a common and standardized type of flat files. That's why the utils package also provides the read.csv function. This function is a wrapper around the read.table() function, so read.csv() calls read.table() behind the scenes, but with different default arguments to match with the CSV format. More specifically, the default for header is TRUE and for sep is a comma, so you don't have to manually specify these anymore. This means that this read.table() call from before is thus exactly the same as this read.csv() call. Apart from CSV files, there are also other types of flat files. Take this tab-delimited file, states.txt, with the same data: To import it with read.table(), you again have to specify a bunch of arguments. This time, you should point to the .txt file instead of the .csv file, and the sep argument should be set to a tab, so backslash t. You can also use the read.delim() function, which again is a wrapper around read.table; the default arguments for header and sep are adapted, among some others. The result of both calls is again a nice translation of the flat file to a an R data frame. Now, there's one last thing I want to discuss here. Have a look at this US csv file and its european counterpart, states_eu.csv. You'll notice that the Europeans use commas for decimal points, while normally one uses the dot. This means that they can't use the comma as the field-delimiter anymore, they need a semicolon. To deal with this easily, R provides the read.csv2() function. Both the sep argument as the dec argument, to tell which character is used for decimal points, are different. Likewise, for read.delim() you have a read.delim2() alternative. Can you spot the differences again? This time, only the dec argument had to change.
Views: 31980 DataCamp
How To... Calculate Pearson's Correlation Coefficient (r) by Hand
 
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Step-by-step instructions for calculating the correlation coefficient (r) for sample data, to determine in there is a relationship between two variables.
Views: 320381 Eugene O'Loughlin
R tutorial: Introduction to cleaning data with R
 
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Learn more about cleaning data with R: https://www.datacamp.com/courses/cleaning-data-in-r Hi, I'm Nick. I'm a data scientist at DataCamp and I'll be your instructor for this course on Cleaning Data in R. Let's kick things off by looking at an example of dirty data. You're looking at the top and bottom, or head and tail, of a dataset containing various weather metrics recorded in the city of Boston over a 12 month period of time. At first glance these data may not appear very dirty. The information is already organized into rows and columns, which is not always the case. The rows are numbered and the columns have names. In other words, it's already in table format, similar to what you might find in a spreadsheet document. We wouldn't be this lucky if, for example, we were scraping a webpage, but we have to start somewhere. Despite the dataset's deceivingly neat appearance, a closer look reveals many issues that should be dealt with prior to, say, attempting to build a statistical model to predict weather patterns in the future. For starters, the first column X (all the way on the left) appears be meaningless; it's not clear what the columns X1, X2, and so forth represent (and if they represent days of the month, then we have time represented in both rows and columns); the different types of measurements contained in the measure column should probably each have their own column; there are a bunch of NAs at the bottom of the data; and the list goes on. Don't worry if these things are not immediately obvious to you -- they will be by the end of the course. In fact, in the last chapter of this course, you will clean this exact same dataset from start to finish using all of the amazing new things you've learned. Dirty data are everywhere. In fact, most real-world datasets start off dirty in one way or another, but by the time they make their way into textbooks and courses, most have already been cleaned and prepared for analysis. This is convenient when all you want to talk about is how to analyze or model the data, but it can leave you at a loss when you're faced with cleaning your own data. With the rise of so-called "big data", data cleaning is more important than ever before. Every industry - finance, health care, retail, hospitality, and even education - is now doggy-paddling in a large sea of data. And as the data get bigger, the number of things that can go wrong do too. Each imperfection becomes harder to find when you can't simply look at the entire dataset in a spreadsheet on your computer. In fact, data cleaning is an essential part of the data science process. In simple terms, you might break this process down into four steps: collecting or acquiring your data, cleaning your data, analyzing or modeling your data, and reporting your results to the appropriate audience. If you try to skip the second step, you'll often run into problems getting the raw data to work with traditional tools for analysis in, say, R or Python. This could be true for a variety of reasons. For example, many common algorithms require variables to be arranged into columns and for missing values to be either removed or replaced with non-missing values, neither of which was the case with the weather data you just saw. Not only is data cleaning an essential part of the data science process - it's also often the most time-consuming part. As the New York Times reported in a 2014 article called "For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights", "Data scientists ... spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets." Unfortunately, data cleaning is not as sexy as training a neural network to identify images of cats on the internet, so it's generally not talked about in the media nor is it taught in most intro data science and statistics courses. No worries, we're here to help. In this course, we'll break data cleaning down into a three step process: exploring your raw data, tidying your data, and preparing your data for analysis. Each of the first three chapters of this course will cover one of these steps in depth, then the fourth chapter will require you to use everything you've learned to take the weather data from raw to ready for analysis. Let's jump right in!
Views: 21697 DataCamp
Creating a  Cube in SSAS
 
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SQL Server Analysis Services (SSAS) is the technology from the Microsoft Business Intelligence stack, to develop Online Analytical Processing (OLAP) solutions. In simple terms, you can use SSAS to create cubes using data from data marts / data warehouse for deeper and faster data analysis. Cubes (Analysis Services) A cube is a set of related measures and dimensions that is used to analyze data. A measure is a fact, which is a transactional value or measurement that a user may want to aggregate. Measures are sourced from columns in one or more source tables, and are grouped into measure groups. A Data source is a connection that represents a simple connection to a data store; it includes all tables and views in the data store. A data source has project scope, which means that a data source created in an Integration Services project is available to all the packages in the project. A data source can be defined and then referenced by connection managers in multiple packages. This makes it easy to update all connection managers that use that data source. A project can have multiple data sources, just as it can have multiple connection managers A Data source view contains the logical model of the schema used by Analysis Services multidimensional database objects—namely cubes, dimensions, and mining structures. A data source view is the metadata definition, stored in an XML format, of these schema elements used by the Unified Dimensional Model (UDM) and by the mining structures
Introduction to Web Scraping (Python) - Lesson 02 (Scrape Tables)
 
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In this video, I show you how to web scrape a table. Kieng Iv/SAF Business Analytics https://ca.linkedin.com/in/kiengiv https://www.facebook.com/UWaterlooBusinessAnalytics
Views: 28430 SAF Business Analytics
Connecting SQL Tables and data in Excel spreadsheets
 
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For more videos on technology, visit http://www.Techytube.com By [email protected] SQL server is a powerful database platform, this means that it can also be complex to understand and work with. However the major users of the data in the database are still a non-technical business user. A key problem that most business users face when it comes to working with SQL Server is the dependency on an IT professional to query and return the data in a CSV format or Excel sheet. With the ability of MS Excel to be able to connect to SQL Server via the ODBC Drivers allows users to work with SQL tables within the familiar Excel User interface. This approach allows users to be productive with SQL Server without having to know Transact SQL. The feature set provided with MS Excel and newer versions of SQL allow business users to do much more than just query the database. These include the ability to query and view data in pivot format for large data sets using power pivot. Perform ad hoc calculations to underlying data and create models that are specific to their business case using the powerful DAX language. Embedding reports in SharePoint services using PowerPivot. Working with Excel is one of the most powerful ways that end users can work with MS SQL Server to deliver results faster and improve productivity.
Views: 177606 techytube
Simple Explanation of Chi-Squared
 
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An explanation of how to compute the chi-squared statistic for independent measures of nominal data. For an explanation of significance testing in general, see http://evc-cit.info/psych018/hyptest/index.html There is also a chi-squared calculator at http://evc-cit.info/psych018/chisquared/index.html
Views: 809702 J David Eisenberg
How to extract tabular data from a web page into LibreOffice using "Link to External Data"
 
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http://courses.robobunnyattack.com/courses/collect-extract-and-use-online-data-quickly-and-more-easily This tutorial screencast demonstrates how to extract tabular data from a web page into LibreOffice using "Link to External Data". I use LibreOffice in this tutorial, but you could use any spreadsheet application such as OpenOffice.org or Microsoft Excel. ***** This is a sample lesson from the online course "Collect, Extract and Use Online Data Quickly and More Easily". Learn data extraction tools and techniques to get information from websites & other sources into useable, useful formats. Enroll today at: http://courses.robobunnyattack.com/courses/collect-extract-and-use-online-data-quickly-and-more-easily Looking for friendly, practical, jargon-free technology training? Visit my website at: http://learn.robobunnyattack.com Thanks for watching! - Kathleen, [email protected]
Views: 15568 Robobunnyattack!
Business Intelligence: Multidimensional Analysis
 
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An introduction to multidimensional business intelligence and OnLine Analytical Processing (OLAP) suitable for both a technical and non-technical audience. Covers dimensions, attributes, measures, Key Performance Indicators (KPIs), aggregates, hierarchies, and data cubes. Downloadable slides available from SlideShare at http://goo.gl/4tIjVI
Views: 49325 Michael Lamont
SSAS 02 - Understanding  Data Source Views (DSV)
 
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This video will help in Create new DSV Explore data in the table Calculated columns Tables from Queries (Named Query) Relationships between tables
PDF Data Extraction and Automation 3.1
 
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Learn how to read and extract PDF data. Whether in native text format or scanned images, UiPath allows you to navigate, identify and use PDF data however you need. Read PDF. Read PDF with OCR.
Views: 87695 UiPath
Creating a database, table, and inserting - SQLite3 with Python 3 part 1
 
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Welcome to an SQLite mini-series! SQLite, as the name suggests, is a lite version of an SQL database. SQLite3 comes as a part of the Python 3 standard library. Databases offer, typically, a superior method of high-volume data input and output over a typical file such as a text file. SQLite is a "light" version that works based on SQL syntax. SQL is a programming language in itself, but is a very popular database language. Many websites use MySQL, for example. SQLite truly shines because it is extremely lightweight. Setting up an SQLite database is nearly instant, there is no server to set up, no users to define, and no permissions to concern yourself with. For this reason, it is often used as a developmental and protyping database, but it can and is used in production. The main issue with SQLite is that it winds up being much like any other flat-file, so high volume input/output, especially with simultaneous queries, can be problematic and slow. You may then ask, what really is the difference between a typical file and sqlite. First, SQLite will let you structure your data as a database, which can easily be queried, so you get that functionality both with adding new content and calling upon it later. Each table would likely need its own file if you were doing plain files, and SQLite is all in one. SQLite is also going to be buffering your data. A flat file will require a full load before you can start querying the full dataset, SQLite files don't work that way. Finally, edits do not require the entire file to be re-saved, it's just that part of the file. This improves performance significantly. Alright great, let's dive into some SQLite. https://pythonprogramming.net/sql-database-python-part-1-inserting-database/ Playlist: https://www.youtube.com/playlist?list=PLQVvvaa0QuDezJh0sC5CqXLKZTSKU1YNo https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 183396 sentdex
Table Detection and Analysis on Document Images - OpenCV
 
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Table detection and Table analysis on document images. This project is a part of an undergrad thesis in Computer Engineering. The test data set of this project contains over 100 images. Only 10 image examples has been illustrated on this video. The program can detect a table on a document image and can mark the columns and rows of a table. The program does not use OCR/ICR motors therefore its performance is considerably high. IEEE Signal Processing and Communications Applications Conference : http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6830480&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F6820096%2F6830164%2F06830480.pdf%3Farnumber%3D6830480 GIT Computer Vision Lab : http://vision.gyte.edu.tr/publications.php https://bilmuh.gtu.edu.tr/vislab/pdfs/2014/414.pdf
Views: 4014 ilham kalyon
Acquire Data from PDF reports by Automatic Report Parsing
 
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One of the most common challenges in business today is extracting data from formatted reports so that the underlying data can be analyzed in a flexible way. The default solution to this problem is re-keying printed reports into spreadsheets. That is a very time-consuming and error-prone method, especially if it has to be repeated on a monthly, weekly or even daily basis. Let’s take a look at a better way… Datawatch makes the data acquisition process simple and easy through a drag-and-drop interface that intelligently parses PDF reports and other desktop files, and extracts the data it finds into a flat table of rows and columns. Occasionally the automatic parser needs some human guidance to ensure it is interpreting the report data correctly. These fine-tuning operations are also presented in an intuitive way. This table can then be sent to downstream applications and business processes, or further prepared and joined with other data to get a complete view of the information. But it doesn’t end here. With Datawatch, to ACQUIRE data means reaching and loading data where ever it is, in whatever format it is. In addition to loading semi-structured and multi-structured data, Datawatch offers out-of-the-box connectivity to a large number of structured data sources. Your data can be stored locally or online, in a file or in a database, it can be historic data-at-rest or streaming data generated in the moment – Datawatch lets you use it all.
Views: 4566 Datawatch
Hot Tip: Tableau - Cleaning Up The Text Table
 
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How to get rid of the -ABC- in a Tableau Text Table Check out our blog at HotpieceofApps.Com for more tips, reviews, applications and lessons in the world of Business Intelligence.
Views: 5799 HotPiece ofApps
AutoCAD Excel Data Link Table
 
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This AutoCAD tutorial is about excel data link, table to excel, drawing to excel, and insert excel with easy command, check it out!!! More Video Tutorial AutoCAD 3D House Modeling: https://www.youtube.com/watch?v=FERNTAh5s0I AutoCAD 3D Soccer Ball: https://www.youtube.com/watch?v=hE09jKBlWYA AutoCAD Tutorial Playlist: https://www.youtube.com/playlist?list=PLjyiWW2QlmFwXcacgfcrwHWU2jNMYd37C
Views: 175562 Mufasu CAD
How to Clean Up Raw Data in Excel
 
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Al Chen (https://twitter.com/bigal123) is an Excel aficionado. Watch as he shows you how to clean up raw data for processing in Excel. This is also a great resource for data visualization projects. Subscribe to Skillshare’s Youtube Channel: http://skl.sh/yt-subscribe Check out all of Skillshare’s classes: http://skl.sh/youtube Like Skillshare on Facebook: https://www.facebook.com/skillshare Follow Skillshare on Twitter: https://twitter.com/skillshare Follow Skillshare on Instagram: http://instagram.com/Skillshare
Views: 53493 Skillshare
Import Data from the Web into Excel
 
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http://alanmurray.blogspot.co.uk/2013/06/import-data-from-web-into-excel.html Import data from the web into Excel. Importing data from the web creates a connection. This connection can be refreshed to ensure your spreadsheet is up to date.
Views: 177005 Computergaga
End-To-End-Example: Data Analysis with Pandas
 
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In this end to end example we web scrape the HTML of this class schedule off of this website: https://ischool.syr.edu/classes/ into a pandas dataframe. From there we extract a feature column for which classes are Undergraduate versus Graduate, then we finish by finding the Undergraduate classes on Fridays or at 8AM. Like all End-To-End examples the program is written organically piece by piece until complete. I make mistakes and figure things out as I go. You can download the code for this example on GitHub: https://github.com/IST256/learn-python/tree/master/content/lessons/12/End-To-End-Example
Views: 2256 Michael Fudge
Multiple Data Sources for a Multidimensional Model
 
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Multiple Data Sources for a Multidimensional Model
Views: 1104 Rob Kerr
Python Trainer Tip: Parsing Data Into Tables from Text Files with Python's Pandas Library
 
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To parse text files into tables for analysis you'd need to build a custom parser, use a loop function to read text chunks, then use an if/then statement or regular expressions to decide what to do with the data. Or, you can simply use Python's Pandas library to read the text into a DataFrame (table) with a single function! Download the set of 8 Pandas Cheat Sheets for more Python Trainer Tips: https://www.enthought.com/pandas-mastery-workshop.
Views: 4401 Enthought
Introduction to Data Mining: Basic Vocabulary
 
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It all starts with the fundamentals! In this data mining session we give you all the background information, technical terminology, and basic knowledge that you will need to hit the ground running. In part 1 of this data mining video series, we cover what data is and the basic vocabulary associated with it. Topics: - Data and Data Types - Data Quality - Data Preprocessing - Similarity and Dissimilarity - Data Exploration and Visualization -- At Data Science Dojo, we're extremely passionate about data science. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2ma7NdR See what our past attendees are saying here: http://bit.ly/2nBLEZO -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 17587 Data Science Dojo
Informatica Scenario Converting Rows into Columns:Best Two approaches explained
 
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The Video Demonstrates a scenario where the Source contains the scores of three students in three subjects in below format. ID Name Subject Score 100 Vivek Science 50 100 Vivek Maths 50 100 Vivek English 70 200 Amit Maths 80 300 Ankit Maths 40 200 Amit Science 70 300 Ankit Science 80 200 Amit English 60 300 Ankit English 60 It explains how we can display the scores of students in cross tabular format using pivot in Source qualifier query or using expression and aggregator in case if source is flat file
Views: 20919 Tech Coach
Convert tabular to matrix data layout
 
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In this video, I have discussed how to extract "Convert a tabular to a matrix data layout" using Power Query. I have blogged about this problem at this link on my website - http://www.ashishmathur.com/converting-a-tabular-data-layout-to-a-matrix-layout/
Views: 831 Ashish Mathur
Python Pandas Tutorial 4: Read Write Excel CSV File
 
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Code (jupyter notebook link): https://github.com/codebasics/py/tree/master/pandas/4_read_write_to_excel This tutorial covers how to read/write excel and csv files in pandas. We will cover, 1) Different options on cleaning up messy data while reading csv/excel files 2) Use convertors to transform data read from excel file 3) Export only portion of dataframe to excel file Website: http://codebasicshub.com/ Facebook: https://www.facebook.com/codebasicshub Twitter: https://twitter.com/codebasicshub Google +: https://plus.google.com/106698781833798756600
Views: 73891 codebasics
Creating KPIs with BISM Tabular Models
 
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Creating KPIs with BISM Tabular Models
Views: 4292 Rob Kerr
SSAS Tabular vs OLAP - Video 1 of 4
 
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SSAS Tabular vs OLAP - Video 1 of 4
Views: 7728 Kevin S. Goff
Convert Summarized Table To Proper Data Set With Power Query!
 
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Another Power Query Demo! Let's see how we can transform a cross-tab report into a more simple data set Original video: http://www.youtube.com/watch?v=UrL-YrhlCJQ Follow me on Twitter: https://twitter.com/EscobarMiguel90 Sponsor: www.poweredsolutions.co
Views: 3228 The Power User
▶ Jupyter Notebook for Data Analytics and Data Mining | Use of Pandas in Jupyter Notebook
 
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How to Read Data in Jupyter Notebook? Read Excel File Using Pandas in Jupyter Notebook - Data MIning »See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on Data Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 ▶ Jupyter Notebook for Data Analytics and Data Mining | Use of Pandas in Jupyter Notebook Learn Data Mining In An Easy Way Data Mining Essential Course Data Mining Course For Beginner #Business_Analysis #Data_Scientist #Data_Analyst Data Mining Bangla Tutorial Data Mining Jupyter Notebook Tutorial in Bangla How to Read Files in Jupyter Notebook Tutorial on Data Mining Tools JN Famous Data Mining Tools Jupyter Notebook
Views: 804 BookBd
What is a HashTable Data Structure - Introduction to Hash Tables , Part 0
 
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This tutorial is an introduction to hash tables. A hash table is a data structure that is used to implement an associative array. This video explains some of the basic concepts regarding hash tables, and also discusses one method (chaining) that can be used to avoid collisions. Wan't to learn C++? I highly recommend this book http://amzn.to/1PftaSt Donate http://bit.ly/17vCDFx
Views: 684679 Paul Programming
Creating  Dimension and Fact Tables in MSBI
 
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A dimension table is a table in a star schema of a data warehouse. A dimension table stores attributes, or dimensions, that describe the objects in a fact table. The fact table contains business facts(or measures), and foreign keys which refer to candidate keys (normally primary keys) in the dimension tables.  Measures. When you first connect to a data source, Tableau assigns any fields that contain quantitative, numerical information (that is, fields where the values are numbers) to the Measures area in the Data pane. When you drag a field from the Measures area to Rows or Columns, Tableau creates a continuous axis.
Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis 2012 IEEE DOTNET
 
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Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis 2012 IEEE DOTNET TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, whereas the SPJ method does not.
Views: 649 jpinfotechprojects
What's New in SQL Server 2012 (Part 6 of 13): An Overview of the New BISM and BI
 
01:07:47
For several years, there was one way to create analytic databases in the Microsoft world—create OLAP cubes in Microsoft SQL Server Analysis Services for users to "slice and dice" data. While that path is still alive and well, Microsoft has established other paths and approaches for creating business intelligence database solutions. While these new approaches are intended to be a little easier than using Analysis Services, there is still a learning curve—not just on these new features (a.k.a. the new "tabular" model, PowerPivot 2.0, etc.), but also on how all the new pieces fit. In this case, all the pieces fit into the overall business intelligence paradigm (which includes OLAP) called the Microsoft Business Intelligence Semantic Model (BISM). In this webcast, we look at the new BISM and the different roadmaps that Microsoft has provided for creating business intelligence applications in SQL Server 2012. This is a general webcast that provides a "what's what" in the new BISM. It paves the way for subsequent webcasts where we look at the details of the tabular model and PowerPivot 2.0. Presenter: Kevin S. Goff, Microsoft SQL Server MVP | SQL Server & Business Intelligence Practice Manager, SetFocus
Views: 8647 ht195367
Multi-dimensional data analysis (CubeGrid) in your web app
 
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CubeGrid is SmartClient UI component that allows instant analysis of multi-dimensional data via your web browser. Learn more about CubeGrid here: https://www.smartclient.com/product/cube.jsp See CubeGrid documentation here: https://www.smartclient.com/smartgwt/javadoc/com/smartgwt/client/widgets/cube/CubeGrid.html Learn more about SmartClient at www.SmartClient.com More about CubeGrid: The CubeGrid is a high-end data analysis engine that wraps OLAP cube functionality into a single interactive grid component for fast access to multidimensional data and calculations. The CubeGrid enables you to view, analyze, and extract data using any standard data source from multiple perspectives. They are typically used as front-ends for business intelligence, analytics and reporting applications to empower your teams to generate powerful reports, identify key patterns and trends, and plan ahead using real-time data. The CubeGrid allow users to perform sophisticated analysis of very large data sets to rapidly answer multidimensional analytical (MDA) queries. Cubes allow the user to re-organize or re-orient the way information is viewed on the fly. Effortlessly slice, dice, drill in and out, roll-up, and pivot data according to your needs. Components such as the CubeGrid are often called crosstabs for their cross-tabular display of data dimensions in nested rows and columns. They are also called pivot tables for their ability to ‘pivot’ dimensions between rows and columns to view a data cube from different perspectives. Please see below for an explanation of the terminology associated with our CubeGrid component, or read the documentation for more details.
What's New in SQL Server 2012 (Part 7 of 13)—What's New in the Tabular Model
 
01:19:42
lthough Microsoft SQL Server Analysis Services has tremendous capabilities for building analytic (OLAP) databases, the learning curve can intimidate developers. The new tabular model in Analysis Services provides developers with some of the same general features as the regular multidimensional capabilities of OLAP, but in a somewhat simplified user interface. In this webcast, we create an analytic database by using the tabular model in Microsoft Visual Studio (SQL Server Data Tools), pointing to a relational database as the source. Because there's still a learning curve with the tabular model, we split this up into two parts. This webcast focuses on the basics of the tabular model (because it's new to many people), and in the second part, we look at some of the more detailed features. If you need a "first look" at what the tabular model offers, this webcast is for you. Presented By: Kevin S. Goff, Microsoft SQL Server MVP | SQL Server & Business Intelligence Practice Manager, SetFocus
Views: 10462 ht195367
How To Create An SSAS Tabular Database (Tutorial)
 
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OakTree IT Senior Technology Evangelist Jim Hudson discusses how to create an SQL Server Analysis Services (SSAS) Tabular Database. OakTree IT Training ranks in the top 5% of Microsoft Learning Partners. Learn more about training OakTree Training in Tulsa, OK - a Microsoft Certified Learning Partner: http://www.oaktreestaffing.com/training
Microsoft Access: Creating a subdatasheet
 
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A subdatasheet is useful when you want to see the information from several data sources in a single datasheet view. For example, in the Northwind sample database, the Orders table has a one-to-many relationship with the Order Details table. If the Order Details table is added as a subdatasheet in the Orders table, you can view and edit data such as the products included in a specific order (each row) by opening the subdatasheet for that Order. For more: http:/www.msofficegurus.com
Views: 44255 Robert Martim
SAP HANA Academy - Series Data: Creating an Equidistant Series Table
 
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*** Important: please read this for prerequisites and links to code. Please note that there is currently no Introduction to Series Data which was mentioned in the videos. In this video we'll create an equidistant Series Data table in SAP HANA. We'll also look at how to generate the underlying series definition / data for this table and use a couple of SQL functions to get certain data from this definition / data. The syntax used in this video is here: https://raw.githubusercontent.com/saphanaacademy/SeriesData/master/Series_Table_Equidistant.sql This video is part of the Series Data playlist here: https://www.youtube.com/playlist?list=PLkzo92owKnVz93lo3R3KbrbXyJyMrq2O3
Views: 1048 SAP HANA Academy
Importing HTML table into Pandas
 
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Learn how to import HTML table into Python Pandas Dataframe.
Views: 7561 DevNami
Building dataset - p.4 Data Analysis with Python and Pandas Tutorial
 
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In this part of Data Analysis with Python and Pandas tutorial series, we're going to expand things a bit. Let's consider that we're multi-billionaires, or multi-millionaires, but it's more fun to be billionaires, and we're trying to diversify our portfolio as much as possible. We want to have all types of asset classes, so we've got stocks, bonds, maybe a money market account, and now we're looking to get into real estate to be solid. You've all seen the commercials right? You buy a CD for $60, attend some $500 seminar, and you're set to start making your 6 figure at a time investments into property, right? Okay, maybe not, but we definitely want to do some research and have some sort of strategy for buying real estate. So, what governs the prices of homes, and do we need to do the research to find this out? Generally, no, you don't really need to do that digging, we know the factors. The factors for home prices are governed by: The economy, interest rates, and demographics. These are the three major influences in general for real estate value. Now, of course, if you're buying land, various other things matter, how level is it, are we going to need to do some work to the land before we can actually lay foundation, how is drainage etc. If there is a house, then we have even more factors, like the roof, windows, heating/AC, floors, foundation, and so on. We can begin to consider these factors later, but first we'll start at the macro level. You will see how quickly our data sets inflate here as it is, it'll blow up fast. So, our first step is to just collect the data. Quandl still represents a great place to start, but this time let's automate the data grabbing. We're going to pull housing data for the 50 states first, but then we stand to try to gather other data as well. We definitely dont want to be manually pulling this data. First, if you do not already have an account, you need to get one. This will give you an API key and unlimited API requests to the free data, which is awesome. Once you create an account, go to your account / me, whatever they are calling it at the time, and then find the section marked API key. That's your key, which you will need. Next, we want to grab the Quandl module. We really don't need the module to make requests at all, but it's a very small module, and the size is worth the slight ease it gives us, so might as well. Open up your terminal/cmd.exe and do pip install quandl (again, remember to specify the full path to pip if pip is not recognized). Next, we're ready to rumble, open up a new editor. http://pythonprogramming.net https://twitter.com/sentdex
Views: 85165 sentdex
Data Transformation Patterns in AWS - AWS Online Tech Talks
 
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"Addressing the transformation of the variety of data empowers organizations to prepare their data for analytics. In this talk, we are going discuss on how to do common data transformations on the AWS Data Lake. We will start our journey by using the Data Catalog on the variety of data within the AWS Data Lake. And then develop the rules to apply the common data transformation patterns. Finally completing the process by showing the different methods in orchestrating the transformation jobs getting the data ready for analytics. Learning Objectives: - Learn how to accelerate common data transformations from a variety of data - Learn how to efficiently orchestrate transformation jobs - Learn best practices and methodologies in data preparation for analytics"
Loading Data Into R Software - (read.table, Data/CSV Import Tutorial)
 
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Basic instructions on importing data into R statistics software for people just starting with R. You'll load a .csv file, tab-delineated text file, and a space-separated file. Download Data from this video: http://sites.google.com/site/curtiskephart/ta/econ113/NHIS_2007_data.csv More Econometrics and R Software Resources: https://sites.google.com/site/curtiskephart/ta/econ113 Download and install R: http://www.google.com/search?hl=en&q=Download+R+Software&btnI=745 Please send me questions. "Load data into r" finally ----------------------------------------
Views: 163410 economicurtis
How to Collate Sports Fixtures Results into a League Table in Excel (4/6)
 
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How can you collate sports fixtures or results into a league table in Excel? Download file link: goo.gl/4wBpl0 Video 1 - INTRODUCTION https://youtu.be/AokM8LW_yW8 Video 2 - convert scores to a result using if https://youtu.be/UkCyukcyIDg Video 3 - collate wins and losses using countif 1 https://youtu.be/a7M7udI_boQ Video 4 - collate wins and losses using countif 2 https://youtu.be/2D5cy560kMc Video 5 - use sumif to find goal difference https://youtu.be/Il2d6xaa1Qc Video 6 - use match and offset to create the league table https://youtu.be/NlyWNoJof8s Sport enthusiasts might wish to create a league table from a set of results in Excel, but it can be difficult to know how to start. Without good pivot table or VBA programming skills, it is a complex task that cannot be completed in one fell swoop. It necessitates some clear, logical thinking about the required steps, and the creation of a well-structured Excel file. This invites us to reflect on what a well-structured Excel file looks like. We propose a structure comprising three elements - a backend, calculations and frontend - and apply it in the video series. This is a good general structure to apply to your next Excel-based task! Along the way, we apply Excel formulae that are essential in spreadsheet modelling including if, sumif, offset, match and vlookup. As a starting point, Chris takes the results data from the 2015-16 Premier League season and works through the steps towards a league table. In the final video in the series, Chris tests the model created against the actual Premier League table. Will it be accurate? For regular spreadsheet hints and tips and more on the #ExcelRevolution: https://www.facebook.com/TigerSpreadsheetSolutions https://twitter.com/TigSpreadsheets http://tigerspreadsheetsolutions.co.uk
Webinar - Soil Data Aggregation Using R (3/2015)
 
01:01:44
R is a powerful tool for soil scientists to employ in combining and summarizing a wide range of spatial and tabular data. This webinar will focus on the use of R as a new tool for creating graphical and tabular summaries of pedon data. Captioning available upon request by e-mailing [email protected] USDA is an equal opportunity provider and employer.
Views: 494 NRCS NSSC
Joining data based on attributes in QGIS
 
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A introduction to joining data based on attribute values in QGIS the video covers how to join data in QGIS and how to handle situations where the join is defined by more then one attribute.
Views: 2403 Esbern Holmes
Scraping data from 2010.voa.gov.uk using WebHarvy
 
02:26
10000 is added to the end of post data. RegEx string used to correctly start table extraction from column 'reference' is copied below. first>([^<]*) Replace > with angular right bracket and < with angular left bracket, since YouTube does not allows angular brackets in description.
Views: 58 sysnucleus
ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery
 
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Demonstration Video for ConTour, the VAST 2014 paper by Christian Partl, Alexander Lex, Marc Streit, Hendrik Strobelt, Anne-Mai Wassermann, Hanspeter Pfister, and Dieter Schmalstieg Details at: http://contour.caleydo.org Large scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms.
Views: 662 Caleydo Project