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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: 171986 ExcelIsFun
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 352128 APMonitor.com
Data Modeling for Power BI
 
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A data model is like the foundation for your house, get it right and everything else goes better. Join the Power BI desktop team in this session to learn about the key steps, and best practices, you need to take to ensure a good data model.
Views: 80992 Microsoft Power BI
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: 71159 Skillshare
Retrieving tabular data from SSAS Cubes with PivotTables:
 
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How to get tabular data from SSAS cubes (perhaps to save as a CSV for import to another system). SSAS cubes often have user-defined hierarchies where getting a certain level of detail, and only that level can be tricky. Video shows how to get the level of detail you want, how to get a tabular, rather then compact format, and how to minimize lengthy updates as you build your table.
Views: 2156 ExcelcraftDotCom
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: 40518 DataCamp
Predixion Create Hierarchy in PowerPivot
 
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Predixion Create Hierarchy in PowerPivot (Data Mining and Predictive Analytics Tutorial). For more information: http://www.predixionsoftware.com Brought to you by Rapid Progress Marketing and Modeling, LLC http://www.rpmsquared.com
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: 195863 APMonitor.com
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: 400944 Eugene O'Loughlin
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: 1005 Ashish Mathur
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: 745844 Paul Programming
Import Data, Copy Data from Excel to R CSV & TXT Files | R Tutorial 1.5 |MarinStatsLectures
 
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Import Data, Copy Data from Excel (or other spreadsheets) to R CSV & TXT Files; Practice with Dataset: https://goo.gl/tJj5XG More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT How to Import CSV data into R or How to Import TXT files into R from Excel or other spreadsheets using function in R ▶︎How to import CSV data into R? We will be using "read.table" function to import comma separated data into R ▶︎ How to import txt data file into R? You will learn to use "read.delim" function to import the data to R ▶︎ In addition, you will also learn to use "file.choose" argument for file location, "header" argument to let R know the data has headers or variable names and "sep" argument to let R know how the data values are separated. ▶︎▶︎Download the dataset here: https://statslectures.com/r-stats-datasets ▶︎▶︎Watch More: ▶︎Intro to Statistics Course: https://bit.ly/2SQOxDH ▶︎Getting Started with R: https://bit.ly/2PkTneg ▶︎Graphs and Descriptive Statistics in R: https://bit.ly/2PkTneg ▶︎Probability distributions in R: https://bit.ly/2AT3wpI ▶︎Bivariate analysis in R: https://bit.ly/2SXvcRi ▶︎Linear Regression in R: https://bit.ly/1iytAtm ◼︎ Table of Content 0:00:17 What are the two main file types for saving a data file 0:00:36 How to save an Excel file as a CSV file (comma-separated value) 0:01:10 How to open a CSV data file in Excel 0:01:20 How to open a CSV file in text editor 0:01:36 How to import CSV file into R? using read.csv function 0:01:44 How to access the help menu for different commands/functions in R 0:02:04 How to specify file location in R? using file.choose argument on read.csv function 0:02:31 How to let R know data has headers or variable names? using the header argument on read.csv function 0:03:22 How to import CSV file into R? using read.table function 0:03:38 How to specify the file location in R for read.table function? using file.choose argument 0:03:46 How to specify in R know how the data values are separated? the "sep" argument on read.table function 0:04:10 How to save a file in Excel as tab-delimited text (TXT) file 0:04:50 How to open a tab-delimited (.TXT) data file in a text editor 0:05:07 How to open a tab-delimited (.TXT) data file in excel 0:05:20 How to import tab-delimited (.TXT) data file into R? using read.delim function 0:05:44 How to to specify the file path for read.delim function in R? using file.choose argument 0:06:06 How to import tab-delimited (.TXT) data file into R? using read.table function 0:06:23 How to specify that the data has headers or variable in R?Using header argument on read.table function This video is a tutorial for programming in R Statistical Software for beginners, using RStudio. Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) The #RTutorial is created by #marinstatslectures to support the statistics course (SPPH400 #IntroductoryStatistics) at The University of British Columbia(UBC) although we make all videos available to the everyone everywhere for free! Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
Create a 3D Table Cube
 
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Check out my Blog: http://exceltraining101.blogspot.com This video shows how to create a 3D Table Cube. P.S. Feel free to provide a comment or share it with a friend! ---------------------------------- #exceltips #exceltipsandtricks #exceltutorial #doughexcel #exceltips #exceltipsandtricks #exceltutorial #doughexcel #exceltips #exceltipsandtricks #exceltutorial #doughexcel #exceltips #exceltipsandtricks #exceltutorial #doughexcel
Views: 70676 Doug H
MDX Query Basics (Analysis Services 2012)
 
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This video is part of LearnItFirst's SQL Server 2012: A Comprehensive Introduction course. More information on this video and course is available here: http://www.learnitfirst.com/Course170 In this video, we walk through the basics of the MDX Query language. It is a very logical language, however, is somewhat large in syntax. If you enjoy writing Transact-SQL, you will really enjoy the MDX language. The AdventureWorks2012 multidimensional models need to be installed on your SSAS Multidimensional mode instance from the CodePlex web site. Highlights from this video: - The basics of an MDX query - What is the basic format of the MDX query language? - Is it necessary to have a WHERE clause in an MDX query? - How to signal the end of a statement in the MDX query language - Using the Internet Order Count and much more...
Views: 101119 LearnItFirst.com
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: 205342 Mufasu CAD
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: 5188 Datawatch
GenesisOne™ Unscrambler - Automated Code Documentation for SQL Server
 
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GenesisOne Unscrambler is the first in the industry to provide dataflow visualization for SQL Server. http://www.genesisonesolutions.com What we will show you on the user demo, is the unique code documentation capabilities, that automatically generates full legacy code documentation for large databases with complicated objects - you will also see how the Unscrambler is the ideal component to the highest quality metadata tools already available. 1. Dataflow Visualization for SQL Server 2. Partnered with IBM and Red Gate 3. GSA Contract Holder 4. Fortune 500 Companies 5. Automatic Source Code Documentation 6. Ideal Component for Metadata We have partnered up with top organization such as IBM, and are a current federal GSA Schedule Contract holder. The GenesisOne technology is also being used by fortune 500 companies. How will this help me? Facilitates inventory taking of information systems for CIO’s. Dramatically reduce time and cost necessary to document your databases. Eliminate continuous document updates and synchronization after database changes. Get new hires up to speed in less than half the time. Increase your customer satisfaction by providing comprehensive, professional looking database documentation. Fully standardize your SQL documentation with no effort. Meet your audit/SOX requirements easily by keeping up-to-date documentation. Product Overview GenesisOne™ provides software development tools for the professional software developer. With our ever-expanding suite of code analysis tools, both developers and management gain thorough insight and understanding of their code. Starting with SQL Server, our tool suite (COTS products) shows all data flow within the code. The GenesisOne™ T-SQL Source Code Unscrambler™ automatically scans all SQL Server objects and documents the code in detail, which has never been done before and is a breakthrough product for the software industry. The resulting documentation provides comprehensive graphical, tabular or verbal explanation of the structure and data flow of all scanned objects. This documentation can be printed to PDF, PNG or SVG as desired. - See more at: http://www.genesisonesolutions.com/#sthash.DeLGA9zw.dpuf Product Features GenesisOne™ T-SQL Code Unscrambler Automatically generates full legacy code documentation for large databases with complicated objects. "By being able to 'unwind' dozens of legacy processes, quickly presenting the entire 'picture', both graphically and verbally, the user can more quickly grasp the complexity of a problem domain, or in other words, getting the "big picture". And therefore save time and money that can better be invested in actually solving the pressing business problem. UnScrambler is a tool that solves the problem of understanding hidden complexity." GenesisOne™ SQL Server documentation (Microsoft SQL Server 2008/ 2008R2/ 2012) in PDF, PNG and SVG format; enables documenting all of the major SQL Server components. New documents can be regenerated at the touch of a button. UnScrambler saved me hours, and in some cases, even days of manually printing out and tracing code, in order to understand what some of the old legacy procedures were doing -BEFORE we could even start working on the new requirements. A Business Process owner can easily follow the UnScrambler Data Flow chart to help guide Developers when upgrading old procedures. The T-SQL Code UnScrambler makes it easy for a Project Manager to visualize the extent of testing that will be necessary after we make changes to legacy procedures. The Dependency Diagram clearly shows every table, view, procedure, function –and even trigger, that may be impacted by code changes. The UnScrambler Dependency Diagram makes it easy for me to get the ‘big picture’ of how all the parts fit together before I start digging into the actual code. I can click on any dependent object to quickly jump into its’ code. Ability to standardize code documentation with zero effort. The Unscrambler technology understands your security concerns and only uses selected permissions, so you can be assured no changes or havoc will effect your system. GenesisOne™ application download includes Microsoft SQL Server Analysis Objects, Microsoft SQL Server Management Objects, Microsoft SQL Server Native Client and SQL Server CLR Types. GenesisOne™ enables your ability to browse database structures. The structures shown depends on the database, the minimum level of detail includes Stored Procedures, tables, indexes and constraints as well as displaying information such as column names, column data types, column lengths, column null ability, and primary and foreign key information. Description editor for tables (table, columns, indexes, foreign keys), Stored Procedures (stored procedure, parameters), defaults, rules. Description data is saved using SQL Server extended properties. Please visit http://www.genesisonesolutions.com for more details.
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: 55153 Michael Lamont
How do I remove columns from a pandas DataFrame?
 
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If you have DataFrame columns that you're never going to use, you may want to remove them entirely in order to focus on the columns that you do use. In this video, I'll show you how to remove columns (and rows), and will briefly explain the meaning of the "axis" and "inplace" parameters. SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool == RESOURCES == GitHub repository for the series: https://github.com/justmarkham/pandas-videos "drop" documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop.html == LET'S CONNECT! == Newsletter: https://www.dataschool.io/subscribe/ Twitter: https://twitter.com/justmarkham Facebook: https://www.facebook.com/DataScienceSchool/ LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 39153 Data School
Creating KPIs with BISM Tabular Models
 
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Creating KPIs with BISM Tabular Models
Views: 4555 Rob Kerr
Applying Data Mining Models with SQL Server Integration Services SSIS
 
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Applying Data Mining Models with SQL Server Integration Services SSIS
Views: 138 PRS Entertainment
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
Views: 2663 Learning Hub
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: 4853 ilham kalyon
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: 31713 SAF Business Analytics
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: 3461 The Power User
Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis
 
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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: 238 Renown Technologies
Dynamic Filtering with Power BI and Analysis Services
 
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In this video, Patrick answers your question about how to do this in Analysis Services Tabular and Multidimensional. Also, he adds a little bit of SQL to the mix. Make sure to watch the previous dynamic filtering videos to understand the basics of how to do this. https://www.youtube.com/watch?v=EXObcA9G9Vw To begin, you need to make sure to get the URL for your published report. For Tabular, you will need to do the following: 1. Identify table and field you want to filter 2. Build the URL formula for custom column 3. Change data category to web url For Multidimensional, you will need to do the following: 1. Identify the dimension 2. Add named calculation in Data Source View 3. Set the URL formula for the calculation 4. Drag the named calc to your dimension as an attribute 5. Change Basic Type to General to WebURL LET'S CONNECT! Guy in a Cube -- https://guyinacube.com -- http://twitter.com/guyinacube -- http://www.facebook.com/guyinacube -- Snapchat - guyinacube -- https://www.instagram.com/guyinacube/ ***Gear*** Check out my Tools page - https://guyinacube.com/tools/
Views: 13626 Guy in a Cube
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: 27947 DataCamp
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
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: 197256 Computergaga
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: 23093 Tech Coach
Working with Tabular Data in GIS Workshop
 
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This workshop by the Clemson Center for Geospatial Technologies will introduce you to querying attributes, joining data, and geocoding addresses. The data and tutorials are available at:
Views: 60 Clemson GIS
BISM Tabular in Analysis Services 2012 - Alberto Ferrari
 
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Scopri la prossima edizione della conferenza su http://www.sqlconference.it Tabular è il nuovo sistema di modellazione dati in memoria disponibile con SQL Server Analysis Services 2012, basato sul motore Vertipaq e sul linguaggio DAX, già disponibili in precedenza con PowerPivot per Excel ma notevolmente potenziati nella nuova versione di SQL. Direttamente dalla SQL Server & Business Intelligence Conference 2012 una panoramica su cosa è Tabular, quali sono i principi che guidano lo sviluppo con questo tool, soffermandoci sulle differenze principali con il modello OLAP (che ora si chiama Multidimensional) e sugli scenari in cui l'implementazione di Tabular offre dei vantaggi rispetto a Multidimensional. Speaker: Alberto Ferrari Conferenza: SQL Server & Business Intelligence Conference 2012 http://www.sqlconference.it
Views: 1137 Technical Conferences
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: 7395 Enthought
How to Install SQL Server 2016 Analysis Services - SQL Server 2016 DBA Tutorial
 
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How to Install and Configure SQL Server 2016 Analysis Services 1. SSAS 2016 installation step by step 2. What are pre-requisites of SQL Server 2016 Analysis Services 3. How to add SSAS 2016 to an existing SQL Server 4. When to choose Multidimensional Data and Mining mode 5. When to Choose Tabular Mode 6. When to Choose PowerPivot Mode 7. SSAS 2016 Installation best practices 8. SSAS 2016 Configuration best practices 9. How to connect to Analysis Services using SQL Server Management Studio Link to scripts used in SQL Server2016 DBA tutorial http://www.techbrothersit.com/2017/03/how-to-install-and-configure-sql-server_24.html Check out our website for Different SQL Server, MSBI tutorials and interview questions such as SQL Server Reporting Services(SSRS) Tutorial SQL Server Integration Services(SSIS) Tutorial SQL Server DBA Tutorial SQLServer 2016 DBA tutorial SQL Server / TSQL Tutorial ( Beginner to Advance) http://www.techbrothersit.com/
Views: 9294 TechBrothersIT
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: 6177 HotPiece ofApps
Web scraping in Python (Part 4): Exporting a CSV with pandas
 
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This is part 4 of an introductory web scraping tutorial. In this video, we'll use Python's pandas library to apply a tabular data structure to our scraped dataset and then export it to a CSV file. I'll end the video with advice and resources for getting better at web scraping. Watch the 4-video series: https://www.youtube.com/playlist?list=PL5-da3qGB5IDbOi0g5WFh1YPDNzXw4LNL == RESOURCES == Download the Jupyter notebook: https://github.com/justmarkham/trump-lies New York Times article: https://www.nytimes.com/interactive/2017/06/23/opinion/trumps-lies.html Beautiful Soup documentation: https://www.crummy.com/software/BeautifulSoup/bs4/doc/ pandas installation: http://pandas.pydata.org/pandas-docs/stable/install.html == DATA SCHOOL VIDEOS == Machine learning with scikit-learn: https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1 Data analysis with pandas: https://www.youtube.com/watch?v=yzIMircGU5I&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=1 Version control with Git: https://www.youtube.com/watch?v=xKVlZ3wFVKA&list=PL5-da3qGB5IBLMp7LtN8Nc3Efd4hJq0kD&index=1 == SUBSCRIBE FOR MORE VIDEOS == https://www.youtube.com/user/dataschool?sub_confirmation=1 == JOIN THE DATA SCHOOL COMMUNITY == Newsletter: http://www.dataschool.io/subscribe/ Twitter: https://twitter.com/justmarkham Facebook: https://www.facebook.com/DataScienceSchool/ Patreon: https://www.patreon.com/dataschool
Views: 18176 Data School
SiilatsFinancePart1 VHD Setup.mp4
 
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Financial Data Mining, Part 1 -- Portfolio Management in Microsoft SQL, Data mining with Excel Keith Siilats, 2/4/2012 CS246 Mining of Massive Datasets 1. Download Microsoft VHD for Mac/Windows/Unix http://www.microsoft.com/download/en/details.aspx?id=26113 2. Download VirtualBox and configure it for HyperV http://dynamicsnavax.blogspot.com/2011/04/how-to-run-ax2012-hyperv-on-virtualbox.html 3. Start your VHD and log in with Username Administrator Password [email protected] 4. Install Guest additions from VirtualBox Menu 5. Install shared folders 6. When the 10 day trial period finishes you can rearm up to 4 times a. Click Start, and then click Command Prompt. b. To reset the activation period, type slmgr.vbs --rearm, and then press ENTER. 7. Install Office http://technet.microsoft.com/en-us/evalcenter/ee390818 8. Get some stock data. Key is to get both sector and market cap info, sp500 is the largest 500 companies a. http://www.stockmarketsreview.com/companies_sp500/ b. http://www.cboe.com/products/snp500.aspx 9. Sign up with www.tdameritrade.com It will take you 3 days to sign up and you will get $500. You will see in the video how to export sector data from tdameritrade, it's easier than using the links above. 10. Get an excel macro to download latest prices. You need excel to clean up all the junk prices. Going through text files is no fun. Hoping that machine learning algorithms know how to deal with rubbish data is optimistic. http://code.google.com/p/finance-data-to-excel/ 11. Install Analysis Server samples http://msftdbprodsamples.codeplex.com/wikipage?title=Installing%20SQL%20Server%202008R2%20Databases See how Microsoft does basket analysis, recommendations and all the other algorithms we have covered in class (hint, it's a lot of point and click and very little code). 12. Watch the video on how to create a VBA script to generate data and export it to SQL server (this is the Map step). In practice you will have multiple pricing servers running complex derivative pricing monte carlos and clustered sql server over several machines with hot backup. If you would like to build a machine like this instructions are http://www.tpc.org/tpcc/results/tpcc_price_perf_results.asp Microsoft Analysis server will work with any SQL source including Oracle. 13. In class we will create a custom portfolio management cube. This is the Reduce step, but its kind of a universal reduce. Once you have the cube you connect to it from Excel Pivot Table and can create any Reduce real time. Tutorial on PivotTables http://www.timeatlas.com/5_minute_tips/chunkers/learn_to_use_pivot_tables_in_excel_2007_to_organize_data 14. Here is a tutorial connecting excel to sql server http://newtech.about.com/od/tutorials/ss/How-To-Configure-Excel-2010-Pivot-Table-For-Business-Intelligence.htm 15. Here is a tutorial how to connect Excel to the Analysis Server http://blogs.office.com/b/microsoft-excel/archive/2008/08/28/using-excel-excel-services-with-sql-server-analysis-services-2008.aspx 16. In the second part I will show you how to create cubes on tick data (trades and quotes) and do high frequency trading.
Views: 530 siilats
Data Wrangling using Programming by Examples
 
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Sumit Gulwani founded the PROSE research and engineering team at Microsoft that develops programming-by-example (PBE) APIs and ships them through multiple Microsoft products. PBE is a new frontier in AI wherein the computer programs itself---the user provides input-output examples and the computer synthesizes an intended script. This is significant because 99% of computer users do not know programming. Even for programmers, this can provide a 10-100x productivity increase for many task domains. A killer application of PBE is in the space of data cleaning/preparation since data scientists often spend up to 80% time wrangling data into a form suitable for learning models or drawing insights. In this video, Sumit illustrates how a data cleaning task, that Python programmers took an average of 30 minutes to finish, can be performed in 30 seconds by non-programmers using the PBE paradigm. In particular, PBE can help ingest a file into tabular format, split a column to extract constituent sub-fields, derive new columns, and suggest form entries.  Sumit Gulwani: https://www.microsoft.com/en-us/research/people/sumitg/ PROSE team: https://microsoft.github.io/prose/ See more at https://www.microsoft.com/en-us/research/video/data-wrangling-using-programming-by-examples/
Views: 2929 Microsoft Research
rvest: Web Scraping Using R
 
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This example shows how to import a table from a web page in both matrix and data frame format using the rvest library. You can find the website table and full code at: http://www.michaeljgrogan.com/rvest-web-scraping-using-r/
Views: 936 Michael Grogan
Importing HTML table into Pandas
 
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Learn how to import HTML table into Python Pandas Dataframe.
Views: 8884 DevNami
Data Lineage using SSIS, SSAS and Power Pivot
 
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Recorded on 30 Oct 2013 at PASS Data Warehousing and Business Intelligence Virtual Chapter (PASS DW/BI VC) Data Lineage is the concept of enabling a client ability to analyze data in a NEW way yet still be able to see the original values, critical in Big Data. Ira Warren Whiteside and Victoria Stasiewicz will demonstrate how to do this in SSIS AND SSAS using Power Pivot, Power BI ,Office 365 and Power Query. We have several case studies for our current clients. Speakers: Ira Warren Whiteside, MDM Architect / BI Architect Ira has over 40 years of IT experience and has extensive knowledge of data warehousing. His roles have included management, Technical Team Lead, Business Analysis, Analytical Application Development, Data Wa rehousing Architecture, Data Modeling, Data Profiling, Data Mining, Text Mining, E-Commerce Implementation, Project Management, Decision Sup port/OLAP, Financial Application Development, E-Commerce, B2C, B2B with primary emphasis in Business Intelligence Applications and Data Warehousing Management. Mr. Whiteside has managed multi-million dollar projects from start to completion. His experience includes the planning, budgeting, project management/technical leadership and product management for large projects and software companies. In addition, Mr. Whiteside has been hands on, in that he has extensively used Microsoft SQL Server 2005/2012 tools, including SSIS, SSAS, SSRS (Microsoft Reporting Services) and Data mining. In addition Ira has authored and published various white papers, articles, provided numerous training seminars and presentations on the methodology required for data-driven application cogeneration in the Microsoft stack. Victoria Stasiewicz, Lead Data profiling Analyst and SSIS developer Victoria is a senior business analyst and data profiling analyst. She has extensive experience in the healthcare industry in analyzing and implementing the Sundial metric decomposition methodology as well as extensive experiencing in developing SSIS packages Join PASS DW/BI Virtual Chapter at http://bi.sqlpass.org Follow us on Twitter @PASSBIVC
Dimensions in SSAS | SSAS Tutorial | SSAS Course | Intellipaat
 
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This tutorial on SSAS explains how to create dimensions, create cubes using data sources, types of dimensions, key column and name column, browsing dimensins in great detail. If you’ve enjoyed this video, Like us and Subscribe to our channel for more similar informative videos and free tutorials. Got any questions about SSAS? Ask us in the comment section below. Are you looking for something more? Enroll in our SSAS training course and become a certified SSAS Professional (https://goo.gl/c2ZWdo). It is a 08 hrs instructor led training provided by Intellipaat which is completely aligned with industry standards and certification bodies ------------------------------ Intellipaat Edge 1. 24x7 Life time Access & Support 2. Flexible Class Schedule 3. Job Assistance 4. Mentors with +14 yrs industry experience 5. Industry Oriented Courseware 6. Life time free Course Upgrade ------------------------------ Why take this course? The MSBI SSAS is a powerful data analytics service to make sense of huge volumes of data in a data warehouse. There is increased demands for professionals who are well-versed in working with OLAP Cubes, multidimensional data modeling, and deriving business insights out of it. This Intellipaat training will help you master SSAS tool to get the best jobs in the corporate world. ------------------------------ What you will learn in this course? This course will be covering following topics: 1.Learning how SSAS helps deploy quality BI solutions 2.Designing Online Analytical Processing Cubes 3.Querying and manipulating data with MDX 4.Cube hierarchy extension and advanced dimension relationship 5.Data source views and data schemas 6.Deploying data mining for improved Business Intelligence 7.Cube operations and limitations 8.Mastering in-memory analytics techniques. ------------------------------ For more information: Please write us to [email protected] or call us at: +91-7847955955 Website: https://goo.gl/c2ZWdo Facebook: https://www.facebook.com/intellipaatonline LinkedIn: https://www.linkedin.com/in/intellipaat/ Twitter: https://www.twitter.com/intellipaat
Views: 111 Intellipaat
Why You Should Care about Data Layout in the Filesystem - Vida Ha & Cheng Lian
 
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"Efficient data access is one of the key factors for having a high performance data processing pipeline. Determining the layout of data values in the filesystem often has fundamental impacts on the performance of data access. In this talk, we will show insights on how data layout affects the performance of data access. We will first explain how modern columnar file formats like Parquet and ORC work and explain how to use them efficiently to store data values. Then, we will present our best practice on how to store datasets, including guidelines on choosing partitioning columns and deciding how to bucket a table. Session hashtag: #SFexp20"
Views: 945 Databricks
Introducción a Data Analysis Expressions (DAX)
 
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Presentado durante en la reunion mensual de Puerto Rico PASS Chapter.
Views: 7072 Alan Koo
Input every row of a data table
 
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This tutorial will continue change the project build by previous tutorial. It will read every keyword from the data table and input them to the control to make searching with FMiner. http://www.fminer.com
Views: 163 flyaaflya
Python Trainer Tip: Parsing Tables from the Web with Python's Pandas Library
 
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To parse a table from the web you'd need to learn about HTML, CSS, web scraping with the Beautiful Soup package, and regular expressions. Or, you can simply use Python's Pandas library to get all the tables 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: 2546 Enthought

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