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Views: 155354 SciShow

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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: 219625 APMonitor.com

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Views: 1774 OL Learn

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Slides PDF: http://15445.courses.cs.cmu.edu/fall2017/slides/24-distributedolap.pdf Annotated Video: https://scs.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=26766b90-eece-462c-8caf-77653d50afa5 Andy Pavlo (http://www.cs.cmu.edu/~pavlo/) 15-445/645 Intro to Database Systems (Fall 2017) Carnegie Mellon University http://15445.courses.cs.cmu.edu/fall2017
Views: 991 CMU Database Group

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Predict who survives the Titanic disaster using Excel. Logistic regression allows us to predict a categorical outcome using categorical and numeric data. For example, we might want to decide which college alumni will agree to make a donation based on their age, gender, graduation date, and prior history of donating. Or we might want to predict whether or not a loan will default based on credit score, purpose of the loan, geographic location, marital status, and income. Logistic regression will allow us to use the information we have to predict the likelihood of the event we're interested in. Linear Regression helps us answer the question, "What value should we expect?" while logistic regression tells us "How likely is it?" Given a set of inputs, a logistic regression equation will return a value between 0 and 1, representing the probability that the event will occur. Based on that probability, we might then choose to either take or not take a particular action. For example, we might decide that if the likelihood that an alumni will donate is below 5%, then we're not going to ask them for a donation. Or if the probability of default on a loan is above 20%, then we might refuse to issue a loan or offer it at a higher interest rate. How we choose the cutoff depends on a cost-benefit analysis. For example, even if there is only a 10% chance of an alumni donating, but the call only takes two minutes and the average donation is 100 dollars, it is probably worthwhile to call.
Views: 198564 Data Analysis Videos

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Views: 9 Karan Ponda

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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 480127 Brandon Weinberg

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The Data Warehouse Tool Kit 3rd Edition Kimball & Ross Ch. 3 Retail Sales Power Point
Views: 1994 Brandon Shelly

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When both protocols and experimental data are uploaded into the ToxBank data warehouse, keywords are selected from a list and used to index the record (https://services.toxbank.net/toxbank-ui/public/resources/ToxBank_Keyword_Hierarchy.pdf). These keywords can be used to search the warehouse and retrieve the protocol or data entries. This tutorial outlines how this keyword search is performed in the ToxBank data warehouse (https://services.toxbank.net/). By Dr. Glenn J. Myatt, Chief Scientific Officer, Leadscope, Inc.

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All Data Warehouse tutorial: https://www.youtube.com/playlist?list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt Lesson 1: Date Warehouse Tutorial - Introduction https://www.youtube.com/watch?v=AfIHINaKD9M&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=2&t=5s Lesson 2: Data Warehouse Tutorial - Creating database https://www.youtube.com/watch?v=b_0RrFXnlhc&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=3&t=511s Lesson 3: Data Warehouse Tutorial - Creating an OLAP cube - Data Warehouse for beginners https://www.youtube.com/watch?v=Z00VTv0GA9I&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=4&t=0s Lesson 4: Data Warehouse tutorial. Creating an ETL https://www.youtube.com/watch?v=9Akvz2x0az4&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=5&t=1371s Lesson 5: Data Warehouse Tutorial - Jobs - Data Warehouse for beginners https://www.youtube.com/watch?v=b997bACm_mE&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=6&t=13s Lesson 6: Data Warehouse Tutorial - Pivot Table in Excel and data presentation https://www.youtube.com/watch?v=n7K6JEvcYFg&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=7&t=3s This Data Warehouse video tutorial demonstrates how to create ETL (Extract, Load, Transform) package.

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The webinar covers - Tableau Demo - Text Mining and Sentiment Analysis in R

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This tutorial demonstrates various preprocessing options in Weka. However, details about data preprocessing will be covered in the upcoming tutorials.
Views: 176933 Rushdi Shams

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This lecture provides the introductory concepts of Frequent pattern mining in transnational databases.
Views: 71216 StudyKorner

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Annotated Video: http://cmudb.io/15721-s16-lect08 Slides PDF: http://15721.courses.cs.cmu.edu/spring2016/slides/08.pdf Reading List: http://15721.courses.cs.cmu.edu/spring2016/schedule.html#08 Andy Pavlo (http://www.cs.cmu.edu/~pavlo/) 15-721 Database Systems (Spring 2016) Carnegie Mellon University
Views: 903 CMU Database Group

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Are you challenged with data privacy regulations like GDPR? Or are you just trying to get an understanding of potentially sensitive data within your applications? In this theater session, learn about a new feature of SQL Server Management Studio called Data Discovery and Classification, which will help you both identify and classify the sensitive data in your database. See this feature in action in a live demo scenario--including labeling, discovery, and reporting.
Views: 609 Microsoft Ignite

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XML Generator transformation used to generate XML from relational tables. etl basics, etl testing, etl informatica, etl tools, etl informatica tutorial, etl informatica training, data warehousing and data mining, data warehousing pdf, informatica for beginners, informatica basic videos, informatica basics for beginners, basics to learn informatica, basic informatica interview question, basic etl concepts, basic etl testing concepts, learn informatica, how to learn informatica, tutorial informatica powercenter, transformation in informatica, transformation in informatica with example, informatica transformation, informatica training,
Views: 588 InformaticaTutorial

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In Informatica Developer, create a Data Processor transformation with a parser to transform a flat file source in PDF or text format to a flat file target in XML format. In this demo, we create a Data Processor transformation, create and configure a Script with a parser, preview the example source, defined the parser, preview the Data Processor transformation, and then add the Data Processor transformation to a mapping.
Views: 15714 Informatica Support

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Streaming data systems, so called Fast Data, promise accelerated access to information, leading to new innovations and competitive advantages. These systems, however, aren’t just faster versions of Big Data; they force architecture changes to meet new demands for reliability and dynamic scalability, more like microservices. This means new challenges for your organization. Whereas a batch job might run for hours, a stream processing application might run for weeks or months. This raises the bar for making these systems resilient against traffic spikes, hardware and network failures, and so forth. The good news is that there is a strong history of facing these demands in the world of microservices. In this webinar by Dr. Dean Wampler, VP of Fast Data Architecture at Lightbend, Inc., we will cut through the buzz around Fast Data and explore how to successfully exploit this new opportunity for innovation in how your organization leverages data. Specifically, Dean will review: - The business justification for transitioning from batch-oriented big data to stream-oriented fast data - The architectural and organizational changes that streaming systems require to meet their higher demands for reliability, resiliency, dynamic scalability, etc. - How some of these requirements can be met by leveraging what your organization already knows about microservice architectures
Views: 517 Lightbend

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Medical Record Text Analytics for Health Plans - Delivers the capability to read free form text in medical records such as chart notes, operatory notes, physician correspondence, admission notes, discharge notes, consult notes, nuclear medicine reports, pathology lab reports, and so forth to discover both content and context, then analyze the results and transform those findings into actionable, context based structured data. This data can then be used to provide a dashboard to streamline medical review of claims pended for additional information and be loaded into a data warehouse and combined with existing claims data to enhance insight into medical management, provider quality, pay for performance, wellness programs, etc.. As part of the process, the solution can also be used to assign Snomed CT, LOINC, ICD, & CPT/HCPCS coding to free form text in medical records. Randall Wilcox IBM Text Analytics Group: Health Care Principle [email protected]
Views: 12121 IBMTAG

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Views: 1193 InformaticaTutorial

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An overview of the National Fire Incident Reporting System (NFIRS) Data Warehouse application and the workflow for running a report.

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This video presents two simple functions in Microsoft Excel, that can be used to create meaningful Management Information System reports. The same functions and procedure can also be utilized for reconciling data.

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http://togotv.dbcls.jp/20110307.html#p01　 In this video, Goran Nenadic who is a Senior Lecturer (Associate Professor) in the School of Computer Science, University of Manchester and a group leader in the Manchester Interdisciplinary BioCenter talks about text mining from biomedical literature. The talk has been at Workshop on Parallel and Distributed Processing of Large Genome Data organized by GCOE Program: Deciphering Genome Sphere from Genome Big Bang.
Views: 1547 togotv

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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: 116917 Microsoft Power BI

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Views: 63 Tech Hacks

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http://www.fiberoptics4sale.com/c/Fiber-Optic-OTDR.html http://www.fiberoptics4sale.com An optical time-domain reflectometer (OTDR) is an optoelectronic instrument used to characterize an optical fiber. An OTDR injects a series of optical pulses into the fiber under test. It also extracts, from the same end of the fiber, light that is scattered (Rayleigh backscatter) or reflected back from points along the fiber. (This is equivalent to the way that an electronic time-domain reflectometer measures reflections caused by changes in the impedance of the cable under test.) The strength of the return pulses is measured and integrated as a function of time, and is plotted as a function of fiber length. An OTDR may be used for estimating the fiber's length and overall attenuation, including splice and mated-connector losses. It may also be used to locate faults, such as breaks, and to measure optical return loss. To measure the attenuation of multiple fibers, it is advisable to test from each end and then average the results, however this considerable extra work is contrary to the common claim that testing can be performed from only one end of the fiber. In addition to required specialized optics and electronics, OTDRs have significant computing ability and a graphical display, so they may provide significant test automation. However, proper instrument operation and interpretation of an OTDR trace still requires special technical training and experience. OTDRs are commonly used to characterize the loss and length of fibers as they go from initial manufacture, through to cabling, warehousing while wound on a drum, installation and then splicing. The last application of installation testing is more challenging, since this can be over extremely long distances, or multiple splices spaced at short distances, or fibers with different optical characteristics joined together. OTDR test results are often carefully stored in case of later fiber failure or warranty claims. Fiber failures can be very expensive, both in terms of the direct cost of repair, and consequential loss of service. OTDRs are also commonly used for fault finding on installed systems. In this case, reference to the installation OTDR trace is very useful, to determine where changes have occurred. Use of an OTDR for fault finding may require an experienced operator who is able to correctly judge the appropriate instrument settings to locate a problem accurately. This is particularly so in cases involving long distance, closely spaced splices or connectors, or PONs. OTDRs are available with a variety of fiber types and wavelengths, to match common applications. In general, OTDR testing at longer wavelengths, such as 1550 nm or 1625 nm, can be used to identify fiber attenuation caused by fiber problems, as opposed to the more common splice or connector losses. The optical dynamic range of an OTDR is limited by a combination of optical pulse output power, optical pulse width, input sensitivity, and signal integration time. Higher optical pulse output power, and better input sensitivity, combine directly to improve measuring range, and are usually fixed features of a particular instrument. However optical pulse width and signal integration time are user adjustable, and require trade-offs which make them application specific.
Views: 111998 FOSCO CONNECT

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Dr. Edward Balli discusses the use of data mining as insight for accounting. This is part 1 of 4 that were presented at the 2009 Salford Analytics and Data Mining Conference in San Diego. To view the complete series of conference videos, please visit http://www.salford-systems.com/video/conference.html.
Views: 72 Salford Systems

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Views: sulayman cham

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#SNMR#Lastmomenttuitions #lmt Video Credit Goes to Saurabh Solved Sum PDF : https://drive.google.com/open?id=17w9MR4R6OYpz8_lIsgSXX9vlCN4DnnCD Subscribe jaroor karna taki next importance ki notification aa jaye Other subject Course Analysis of Algoeithm : https://bit.ly/2L1HaJu COA lecture:https ://bit.ly/2DxZsfc Computer Graphics :https://bit.ly/2W56dg1 Operating system :https://bit.ly/2UWo6RG Comiler :https://bit.ly/2IVFsqu Distributed System :https://bit.ly/2vlummT Computer Networks :https://bit.ly/2W67za8 Machine Learning :https://bit.ly/2GHhq0X Discrete Mathematics :https://bit.ly/2UG5m3D DBMS Lectures :https://bit.ly/2VltsW3 Mobile Computing and Communication :https://bit.ly/2UREId8 DLDA Lecture :https://bit.ly/2XIbbQ2 Software Engineering :https://bit.ly/2J0L4j8 Placement Interview Series:http://tiny.cc/1kb55y Aptitude Lectures :https://bit.ly/2UUxup0 Python tutorial for Beginners :https://bit.ly/2UVuUPt SQL placement Series for Beginners :https://bit.ly/2XFN2c Motivation Student Life Series : http://tiny.cc/b8b55y
Views: 1625 Last moment tuitions

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Views: 198 NTLSTech

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Views: 8484 Simplilearn

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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Linear regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 44852 WekaMOOC

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Copy and merge data between Salesforce orgs and archive data sets for back up, reporting, or compliance.

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Cloud-based software tools to quickly evaluate large amounts of text, survey, and Twitter data. Combines data science methods with e-discovery text analytics tools to shorten a process that used to last weeks or months. Subscribe to AI is The New BI! channel: https://www.youtube.com/channel/UCQfNRKMf9DVZoUn_gzC-6VQ?sub_confirmation=1
Views: 208 Inside Analysis

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Views: 83445 edureka!

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Views: 185740 Augmented Startups

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It's all about the data, and a dedicated database makes your data collection easier and more secure. This basic walkthrough will give a quick overview of the merirts of a dedicated database.
Views: 798 Zerion Software

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http://echovera.ca/solutions/intelligent-ocr-for-netsuite-netsuite-ap-automation/ Intelligent Data Capture for NetSuite integrates NetSuite with ChronoScan, one of the world’s leading (and cost-effective) Intelligent OCR platforms. How are you capturing invoice information for NetSuite? Historically invoice data capture is performed in two ways: manually keying in data from paper invoices; or the invoices are scanned with OCR (Optical Character Recognition) software and then manually mapped in order to capture essential information from relevant fields in the document. Intelligent OCR changes the playing field dramatically. It intuitively performs field mapping and data collection, freeing up time and making the entire process more efficient. It’s “next generation” OCR! ChronoScan is Intelligent OCR for NetSuite that: • Processes invoices directly from email – eliminating the need to print • Scans paper invoices, capturing relevant invoice fields • Gets high marks for ease of use and an intuitive interface • Integrates seamlessly with NetSuite • Most affordable intelligent OCR on the market today What’s the Advantage of Intelligent OCR Over Regular OCR? With standard OCR software, much time is spent on field-mapping, as a staff member must give the OCR software instructions as to where data is located on each scanned invoice. Time is also spent on data collection, as a staff member has to manually select the data zones on all of the emailed or scanned supplier invoices the organization receives. Intelligent OCR technology dramatically reduces the manual steps involved in both field mapping and data collection. Intelligent OCR is capable of automatically and intuitively detecting/learning the particular supplier invoices based on their respective layouts, allowing the scanning and/or importing emailed invoices in more efficiently. Intelligent OCR software also allows the user to search typical values from any document, “tagging” it, using a built-in database. EchoVera Inc. provides Intelligent OCR for NetSuite, and Purchase to Pay for NetSuite to companies and organizations looking to reduce costs and increase the efficiency of their accounts payable operations. Customers worldwide.
Views: 1452 EchoVera

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Teori Basis Data Lanjut - Minggu 14 - Politeknik Elektronika Negeri Surabaya - Bab Data Warehousing dan Decision Support System

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SAP Lumira enables us to acquire non-traditional data sources by taking advantage of data access extensions. In this video, we’ll install the extension for MongoDB, which stores data in documents, and acquire a dataset based on bitcoin transactions.
Views: 1402 SAPAnalyticsTraining

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Successful data sharing requires that users of your data understand the data format, the data semantics, and the rules that govern your particular use of terms and values. Sharing often means the creation of “cross-walks” that transfer data from one schema to another using some or all of this information. However, cross-walks are time-consuming because the information that is provided is neither standardized nor machine-readable. Application profiles aim to make sharing data more efficient and more effective. They can also do much more than facilitate sharable data: APs can help metadata developers clarify and express design options; they can be a focus for consensus within a community; they can drive user interfaces; and they can be the basis for quality control. Machine-actionable APs could become a vital tool in the metadata toolbox and there is a clear need for standardization. Communities such as Dublin Core and the World Wide Web Consortium are among those working in this area.

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Most experienced data analysts and programmers already have the skills to get started. BigQuery is fully managed and lets you search through terabytes of data in seconds. It’s also cost effective: you can store gigabytes, terabytes, or even petabytes of data with no upfront payment, no administrative costs, and no licensing fees. In this webinar, we will: - Build several highly-effective analytics solutions with Google BigQuery - Provide a clear road map of BigQuery capabilities - Explain how to quickly find answers and examples online - Share how to best evaluate BigQuery for your use cases - Answer your questions about BigQuery Qwiklabs: https://goo.gle/2JgSTQv