Search results “Data mining with big data pdf viewer”
Import Data and Analyze with MATLAB
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: 326505 APMonitor.com
Extract Structured Data from unstructured Text (Text Mining Using R)
A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 9377 Stat Pharm
Introduction to Data Science with R - Data Analysis Part 1
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 810287 David Langer
The Best Way to Prepare a Dataset Easily
In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating. The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf Please subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Views: 132718 Siraj Raval
Here's a list of 10 must read book on Data Science & Machine Learning. Foundations of DATA SCIENCE Book www.cs.cornell.edu/jeh/book.pdf Understanding Machine Learning Book www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf The Elements of Statistical Learning Book web.stanford.edu/~hastie/Papers/ESLII.pdf An Introduction to Statistical Learning Book www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf Mining of Massive Data Sets Book infolab.stanford.edu/~ullman/mmds/book.pdf
Views: 1065 DATA SCIENCE
Convert PDF to Text in Hadoop/BigData
Process complex data type in Hadoop. Convert millions of PDF files into text file in Hadoop Ecosystem.
Views: 934 Vijay Garg
Data Analytics for Beginners | Introduction to Data Analytics | Data Analytics Tutorial
Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm_medium=VM&utm_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing Survey Data • What is Business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems • Coding, coding tip • Data Cleaning • Univariate Data Analysis • Statistics Describing a continuous variable distribution • Standard deviation • Distribution and percentiles • Analysis of categorical data • Observed Vs Expected Distribution • Identifying and solving business use cases • Recognizing, defining, structuring and analyzing the problem • Interpreting results and making the decision • Case Study Get started with Data Analytics with this tutorial. Happy Learning For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 184310 ACADGILD
Data Mining  Association Rule - Basic Concepts
short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Follow us on https://t.me/Learnerspage Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. BIGDATA in TELUGU https://youtu.be/jdPhsYZU_5E?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. Data science & Big data in Telugu https://youtu.be/5XQ3lmPVV8M?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD). Tableau in Telugu https://youtu.be/iPvwRyeAGYA?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX In 2020 the world will generate 50 times the amount of data as in 2011. And 75 times the number of information sources (IDC, 2011). Within these data are huge, unparalleled opportunities for human advancement. But to turn opportunities into reality, people need the power of data at their fingertips. Tableau is building software to deliver exactly that. Big Data Tool R Installation in Telugu https://youtu.be/hdTLyC-KL_I?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX https://cran.r-project.org/bin/windows/base/ https://www.rstudio.com/products/rstudio/download/ R is a programming language and free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Tableau in Telugu:How to Creat Groups in Charts https://youtu.be/i1z1lGJvQQU?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Data Warehouse in Telugu https://youtu.be/xFLE1_V7u6M Business Intelligence is a technology based on customer and profit-oriented models that reduce operating costs and provide increased profitability by improving productivity, sales, service and helps to make decision-making capabilities at no time. Business Intelligence Models are based on multidimensional analysis and key performance indicators (KPI) of an enterprise R Programming in Telugu:How to Write CVS files and Extract Data from data.[Lesson-3] https://youtu.be/oeh9fyru9-o?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX This video is about: How to write CSV file in R . How to remove the columns from data set or data frame in R R Programming Tutorial in Telugu: How to Read data in R [Lesson2] https://youtu.be/CL0RG4NTuq4?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX In This R Tutorial you will find clear way, how to read the CVS files in R Studio. How to use commands SETWD(), READ.CSV(), HEAD(), TAIL(),VIEW() FETCH DATA FROM SQL TO EXCEL https://youtu.be/IqukX_hKEnE?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Tableau in Telugu: Tableau Colors https://youtu.be/fHvg0irp1ds?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Pareto Chart Analysis https://youtu.be/TPZaIX4S1TU Pareto Analysis is a statistical technique in decision-making used for the selection of a limited number of tasks that produce significant overall effect. It uses the Pareto Principle (also known as the 80/20 rule) the idea that by doing 20% of the work you can generate 80% of the benefit of doing the entire job. Population Pyramid Chart https://youtu.be/poWV5VsideI Download the file in below link https://drive.google.com/file/d/1eWu8zXxh1QRFQj4OJAkG_S7AuHIDqY04/view A population pyramid also called an "age pyramid" is a graphical illustration that shows the distribution of various age groups in a population
Views: 15635 Learners Page
A.I. Is Monitoring You Right Now and Here’s How It's Using Your Data
There's wisdom in crowds, and scientists are applying artificial intelligence and machine learning to better predict global crises and outbreaks. You Could Live On One Of These Moons With an Oxygen Mask and Heavy Jacket https://www.youtube.com/watch?v=9t0Cziw6AbI Subscribe! https://www.youtube.com/user/DNewsChannel Read More: Identifying Behaviors in Crowd Scenes Using Stability Analysis for Dynamical Systems http://crcv.ucf.edu/papers/pamiLatest.pdf “A method is proposed for identifying five crowd behaviors (bottlenecks, fountainheads, lanes, arches, and blocking) in visual scenes.” Tracking in High Density Crowds Data Set http://crcv.ucf.edu/data/tracking.php “The Static Floor Field is aimed at capturing attractive and constant properties of the scene. These properties include preferred areas, such as dominant paths often taken by the crowd as it moves through the scene, and preferred exit locations.” Can Crowds Predict the Future? https://www.smithsonianmag.com/smart-news/can-crowds-predict-the-future-180948116/ “The Good Judgement Project is using the IARPA game as “a vehicle for social-science research to determine the most effective means of eliciting and aggregating geopolitical forecasts from a widely dispersed forecaster pool.” ____________________ Seeker inspires us to see the world through the lens of science and evokes a sense of curiosity, optimism and adventure. Visit the Seeker website https://www.seeker.com/ Subscribe now! https://www.youtube.com/user/DNewsChannel Seeker on Twitter http://twitter.com/seeker Seeker on Facebook https://www.facebook.com/SeekerMedia/ Seeker http://www.seeker.com/
Views: 140390 Seeker
Data Mining with Weka (5.3: Data mining and ethics)
Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 3: Data mining and ethics http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 10272 WekaMOOC
How to Build a Text Mining, Machine Learning Document Classification System in R!
We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 156583 Timothy DAuria
Weka Data Mining Tutorial for First Time & Beginner Users
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: 416686 Brandon Weinberg
Data Access in KNIME: File Reader
This video shows how to read text files. Example workflows on how to use the Table Reader node can be found on the EXAMPLES server within the KNIME Analytics Platform (www.knime.org) under 01_Data_Access/01_Common_Type_Files Previous: - "Annotations and comments" https://youtu.be/AHURYB_O8sA Next: - How to read a .table formatted files https://youtu.be/tid1qi2HAOo
Views: 4026 KNIMETV
R Programming Tutorial-22-How to Read Txt Files ( हिन्दी)
Programming in Hindi ( हिन्दी) R is the language of big data—a statistical programming language that helps describe, mine, and test relationships between large amounts of data.. This course will teach you to program the R language from the ground up. You will learn everything from the very fundamentals of programming right through to the complexities of charts...... Learn Shiny:- https://www.youtube.com/playlist?list=PLgPJX9sVy92yImPWgBojfdTx8pkU03Q2H Best Book For R 1..https://dl.flipkart.com/dl/beginning-r-statistical-programming-language-1/p/itmegmt3qxdzfwgz?iid=95e191f0-6122-42d6-b4a4-c944b7edb0fe.9788126541201.SEARCH&srno=s_1_4&lid=LSTBOK9788126541201BZ19LR&fm=SEARCH&qH=abbc816787956bfb&pid=9788126541201&affid=vijaymanr 2... https://dl.flipkart.com/dl/beginning-r-introduction-statistical-programming/p/itmefqfarxq6ybke?iid=f04ae3d3-05f3-4336-b31a-496861cfad22.9781430245544.SEARCH&srno=s_1_8&lid=LSTBOK9781430245544ZT0WTF&fm=SEARCH&qH=abbc816787956bfb&pid=9781430245544&affid=vijaymanr Please support us by Paytm:--9634533596 Learning MySQL - (https://dl.flipkart.com/dl//learning-mysql/p/itmdz6zetdmpxe8g?pid=9780596008642&affid=vijaymanr) Best XHTML And CSS Tutorials:--https://www.youtube.com/playlist?list=PLgPJX9sVy92w1pmbv9S1G6jdyCuMDFVek Best android app development Tutorials:--https://www.youtube.com/playlist?list=PLgPJX9sVy92zmA9YedYtbnOfLP8DH6Ihd Best Python tutorial:-https://www.youtube.com/playlist?list=PLgPJX9sVy92xVxrM7YJRZ2TUXPgWYyfVr Best Java tutorial:----https://www.youtube.com/playlist?list=PLgPJX9sVy92zxE2XRZenJ-rjHnt_Vqr2f Best C++ tutorial :-----https://www.youtube.com/playlist?list=PLgPJX9sVy92wA4SkNpy8-3vcPg9zpLthG Best C tutorial:---------https://www.youtube.com/playlist?list=PLgPJX9sVy92xk-c6iNqobhPqEokLiCGnp Please support us by Paytm:--9634533596 Learn MongoDB :---- https://www.youtube.com/playlist?list=PLgPJX9sVy92xUxpTFgAOSBHdBwIdxav39 Learn Magento 2 :----- https://www.youtube.com/playlist?list=PLgPJX9sVy92x0IfsB1iIXt286LSn1kQ_j Learn Laravel 5.4 :----https://www.youtube.com/playlist?list=PLgPJX9sVy92y5riB65d_Os5iZIs2wk_T3 Learning PHP:--https://www.youtube.com/playlist?list=PLgPJX9sVy92yA5dP9pSHzuQpyEwBRuvU8 ---------------------------------------------------------------------- Laptop I used :--- http://fkrt.it/Gq49vLuuuN Mic I used :--- http://fkrt.it/Gi2DyLuuuN ----------------------------------------------------------------------
Views: 2374 CS Geeks
Tutorial #2 - Platforms for Big Data Analytics
Platforms for Big Data Analytics with Dr. Chandan Reddy, Wayne State Tutorial Information: http://dmkd.cs.wayne.edu/TUTORIAL/Bigdata/ The paper is available at: http://dmkd.cs.wayne.edu/Papers/JBD14.pdf A Survey on Platforms for Big Data Analytics, Journal of Big Data, 2014
The beauty of data visualization - David McCandless
View full lesson: http://ed.ted.com/lessons/david-mccandless-the-beauty-of-data-visualization David McCandless turns complex data sets, like worldwide military spending, media buzz, and Facebook status updates, into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut -- and it may just change the way we see the world. Talk by David McCandless.
Views: 530346 TED-Ed
Excel Magic Trick 1336: Power Query: Import Big Data Text Files: Connection Only or Data Model?
Download File: http://people.highline.edu/mgirvin/excelisfun.htm See how to use Import 10 Text Files and Append (combine) then into a single Proper Data Set before making a PivotTable Report. Compare and Contrast whether we should use Connection Only or Data Model to store the data. 1. (00:18) Introduction & Look at Text Files that Contain 7 Million Transactional Records 2. (01:43) Power Query (Get & Transform) Import From Folder to append (combine) 10 Text Files that contain 7 Millions transactional records. 3. (05:07) Load Data as Connection Only and Make PivotTable 4. (08:17) Load Data into Data Model and Make PivotTable. 5. (10:46) Summary
Views: 21717 ExcelIsFun
The Connected Vehicle: How Analytics Drives Telematics Value
http://www.sas.com/automotive Learn how SAS' Internet of Things technology is turning mundane telematics trouble codes into real value in the automotive and trucking industries. When everything is connected, we need answers, we need the Analytics of Things. SAS AUTOMOTIVE SOLUTIONS Drive better decisions with the world’s best analytics. SAS has automotive solutions for: * Sales & Marketing * Product & Process Quality * Aftermarket Service * Credit & Finance * Supply & Demand Planning * And more... LEARN MORE ABOUT SAS SOLUTIONS FOR AUTOMOTIVE http://www.sas.com/en_us/industry/automotive.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 16820 SAS Software
The best stats you've ever seen | Hans Rosling
http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2654188 TED
What is a HashTable Data Structure - Introduction to Hash Tables , Part 0
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: 702433 Paul Programming
Strategy Beyond the Hockey Stick
Mining the data from thousands of large companies, McKinsey Partners Chris Bradley, Martin Hirt and Sven Smit open the windows of the strategy room, and bring an "outside view." This is not another by-the-book approach to strategy. It's not another trudge through frameworks or small-scale case studies promising a secret formula for success. It's an irreverent, fact-driven, and humorous take on the real world of strategic decision making http://www.mckinsey.com/strategybeyondthehockeystick Reserve your copy Amazon - http://amzn.to/2lusnak Barnes and Noble - http://bit.ly/2zQcwZ3 Indiebound - http://bit.ly/2Cqbrfy 800-CEO-read - http://bit.ly/2lp0dyf
Views: 6781 McKinsey & Company
K mean clustering algorithm with solve example
Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 224130 Last moment tuitions
Importing Data into R - How to import csv and text files into R
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: 34320 DataCamp
IDEA Data Analysis Software
IDEA Data Analysis Software Understanding data is critical to your business. However, when your data is stored in different locations and in a variety of formats, efficiently importing and analyzing all of that data can be challenging. CaseWare IDEA Data Analytics helps you solve the data challenge. IDEA is an easy to use, data analysis platform, that allows audit, finance and compliance professionals to view all their disparate data as though they were one. Whether you're assessing internal controls, conducting operational audits or identifying potential fraud, IDEA ensures complete visibility of every facet of data that affects your business. IDEA enables you to effortlessly import an infinite amount of data from virtually any source, including SAP with step-by-step assistance. Additionally, you can import PDFs and plain text files without changing the source data as the imports are read-only. IDEA offers easy to use data analytics features that require no programming knowledge. You can join, correlate, and compare databases from disparate sources, and use advanced Benford's Law analysis to better combat fraud, providing a streamlined experience that outperform spreadsheets and other audit software. The IDEA interactive audit trail provides a graphical history of the actions you perform, documenting your analysis procedures for future use. Save time and get results quickly with Smart Analyzer. A collection of ready-made analytics designed for business areas such as general ledger, fixed assets, inventory, accounts payables, and receivables. For collaborative analytics, IDEA Server enables team members to move files seamlessly between personal and shared projects, regardless of team member location or the data volume or variety. IDEA's collaborative analytics platform can scale to support large teams with the added bonus of a more secure environment for sensitive data. Minimize risk and maximize your peace of mind. IDEA Data Analytics Platform from CaseWare offers a comprehensive view of your data so you can see the big picture, identify patterns, and explore transactional data in depth. Request a demo of IDEA today. SUBSCRIBE: http://bit.ly/29BfMuV About CaseWare Analytics: CaseWare Analytics is home to IDEA® Data Analysis and CaseWare Monitor. Our software solutions are built on a foundation of industry best practices and domain expertise enabling audit, compliance and finance professionals to assess risk, accumulate audit evidence, uncover trends, identify issues and provide the necessary intelligence to make informed decisions, ensure compliance and improve business processes. WEBSITE: http://bit.ly/1fbul6J BLOG: http://bit.ly/1i7s1vu TWITTER: http://bit.ly/29BTBan
Views: 25081 CaseWare Analytics
Meta Brown: Data Mining for Dummies #DataTalk
In our weekly #DataTalk, we had a chance to talk with Meta Brown about her work in data science and her latest book: Data Mining for Dummies. You can learn more about her by going to her website: http://www.metabrown.com/ You can read a full transcription of this video by going to: http://ex.pn/metabrown You can learn about upcoming #DataTalk events and tweetchats: http://experian.com/datatalk
Views: 1416 Experian
Data Mining with Weka (1.6: Visualizing your data)
Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 61310 WekaMOOC
Data Mining with Weka (1.3: Exploring datasets)
Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 3: Exploring datasets http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 73260 WekaMOOC
Visualizing Unstructured Data with Tableau, Featuring Bill Inmon
How do you turn unstructured data into a visualization? Over 80% of the data in a corporation is textual, including emails, contracts, chat sessions, social media, account notes and much more. In this session, Bill Inmon--known as the Father of Data Warehousing--will discuss "textual disambiguation," a new approach for transforming unstructured data into a database that can be used in Tableau visualizations. Textual disambiguation leverages several advanced techniques to create a state-of-the-art method for leveraging and understanding an organization's unstructured data.
Views: 4996 Tableau Software
More Data Mining with Weka (3.6: Evaluating clusters)
More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Evaluating clusters http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 19253 WekaMOOC
Data Mining with Weka (3.2: Overfitting)
Data Mining with Weka: online course from the University of Waikato Class X - Lesson X: Overfitting http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 25114 WekaMOOC
Data Mining with Weka (2.5: Cross-validation)
Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Cross-validation http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 36516 WekaMOOC
Advanced Data Mining with Weka (4.6: Application: Image classification)
Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Application: Image classification http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 6046 WekaMOOC
Scanner: Efficient Video Analysis at Scale (SIGGRAPH 2018)
http://scanner.run/ http://graphics.stanford.edu/papers/scanner/scanner_sig18.pdf Scanner is a system for developing applications that efficiently process large video datasets. Scanner applications can run on a multi-core laptop, a server packed with multiple GPUs, or a large number of machines in the cloud. Scanner has been used for: * Labeling and data mining large video collections: Scanner is in use at Stanford University as the compute engine for visual data mining applications that detect people, commercials, human poses, etc. in datasets as big as 70,000 hours of TV news (12 billion frames, 20 TB) or 600 feature length movies (106 million frames). * VR Video synthesis: scaling the Surround 360 VR video stitching software, which processes fourteen 2048x2048 input videos to produce 8k stereo video output. To learn more about Scanner, see the documentation below or read the SIGGRAPH 2018 Technical Paper: “Scanner: Efficient Video Analysis at Scale” by Poms, Crichton, Hanrahan, and Fatahalian.
Views: 1600 Will Crichton
Scrape Websites with Python + Beautiful Soup 4 + Requests -- Coding with Python
Coding with Python -- Scrape Websites with Python + Beautiful Soup + Python Requests Scraping websites for data is often a great way to do research on any given idea. This tutorial takes you through the steps of using the Python libraries Beautiful Soup 4 (http://www.crummy.com/software/BeautifulSoup/bs4/doc/#) and Python Requests (http://docs.python-requests.org/en/latest/). Reference code available under "Actions" here: https://codingforentrepreneurs.com/projects/coding-python/scrape-beautiful-soup/ Coding for Python is a series of videos designed to help you better understand how to use python. Assumes basic knowledge of python. View all my videos: http://bit.ly/1a4Ienh Join our Newsletter: http://eepurl.com/NmMcr A few ways to learn Django, Python, Jquery, and more: Coding For Entrepreneurs: https://codingforentrepreneurs.com (includes free projects and free setup guides. All premium content is just $25/mo). Includes implementing Twitter Bootstrap 3, Stripe.com, django, south, pip, django registration, virtual environments, deployment, basic jquery, ajax, and much more. On Udemy: Bestselling Udemy Coding for Entrepreneurs Course: https://www.udemy.com/coding-for-entrepreneurs/?couponCode=youtubecfe49 (reg $99, this link $49) MatchMaker and Geolocator Course: https://www.udemy.com/coding-for-entrepreneurs-matchmaker-geolocator/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Marketplace & Dail Deals Course: https://www.udemy.com/coding-for-entrepreneurs-marketplace-daily-deals/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Free Udemy Course (80k+ students): https://www.udemy.com/coding-for-entrepreneurs-basic/ Fun Fact! This Course was Funded on Kickstarter: http://www.kickstarter.com/projects/jmitchel3/coding-for-entrepreneurs
Views: 391477 CodingEntrepreneurs
Advanced Data Mining with Weka (2.4: MOA classifiers and streams)
Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: MOA classifiers and streams http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2654 WekaMOOC
Asteroid Mining Will Revolutionize Our Future Economy
Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: https://to.pbs.org/PBSDSDonate Don’t miss our next video! SUBSCRIBE! ►► http://bit.ly/iotbs_sub Read “Soonish” by Kelly and Zach Weinersmith! https://smbc-comics.com/soonish/ ↓↓↓ More info and sources below ↓↓↓ Try 23andMe at: http://www.23andme.com/okay Asteroid mining sounds like something out of a bad space movie, but harvesting materials from space rocks might be our ticket to building space colonies or living on Mars. Most of Earth’s precious and rare metals are locked too far in the crust for us to get at them, and launching them to space is too expensive for us to supply large colonies off Earth or explore far off worlds. How do we get our hands on the planetary resources inside asteroids? Let’s find out! Special thanks to Kelly and Zach Weinersmith for working with us on this video. Check out their new book “Soonish” for a hilarious and science-filled look at future technologies: https://smbc-comics.com/soonish/ SOURCES/EXTRAS: Badescu, Viorel, ed. Asteroids: Prospective energy and material resources. Springer Science & Business Media, 2013. Crawford, Ian A. "Asteroids in the service of humanity." arXiv preprint arXiv:1306.2678 (2013). Kleine, Thorsten. "Geoscience: Earth's patchy late veneer." Nature 477.7363 (2011): 168-169. ----------- FOLLOW US: Merch: https://store.dftba.com/collections/its-okay-to-be-smart Facebook: http://www.facebook.com/itsokaytobesmart Twitter: @okaytobesmart @DrJoeHanson Tumblr: http://www.itsokaytobesmart.com Instagram: @DrJoeHanson Snapchat: YoDrJoe ----------- It’s Okay To Be Smart is hosted by Joe Hanson, Ph.D. Director: Joe Nicolosi Writer: Dr. Shaena Montanari Editor/animator: Andrew Orsak Producer: Stephanie Noone and Amanda Fox Produced by PBS Digital Studios Music via APM Stock images from Shutterstock http://www.shutterstock.com -----------
Views: 249417 It's Okay To Be Smart
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough
In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
Views: 4158 MapR Technologies
Data Lakes in der Praxis: Grundlagen, Möglichkeiten, Erfahrungen | AWS Transformation Day
The flexibility of AWS enables you to tailor your approach for architecting a Data Lake. AWS provides you with secure infrastructure and offers a number of scalable, cost-effective storage, data management, and analytics services to turn heterogeneous data into new meaningful insights. Hosting your Data Lake on AWS gives you access to the most complete platform for Big Data. Furthermore, a Data Lake isn’t meant to be replace your existing Data Warehouses, but rather complement them. If you’re already using a Data Warehouse, or are looking to implement one, a Data Lake can be used as a source for both structured and unstructured data, which can be easily converted into a well-defined schema before ingesting it into your Data Warehouse. A Data Lake can also be used for ad hoc analytics with unstructured or unknown datasets, so you can quickly explore and discover new insights without the need to convert them into a well-defined schema. In this talk, we will cover best practices and reference architecture and cover the key tenants of a Data Lake such as: (a) Decoupling data from compute, (b) Collecting and storing any type of data, at any scale and at low costs, (c) Securing and protecting all of data stored in the central repository, (d) Searching and finding the relevant data in the central repository, (e) Quickly and easily performing new types of data analysis on datasets and (f) Querying the data by defining the data’s structure at the time of use (schema on read). Download the slides here: http://aws-de-media.s3.amazonaws.com/images/TransformationDay/TDay_Slides/P3_TDay.pdf
Python Certification | Data Science with Python Certification | Python Online Training | Edureka
***** Python for Data Science Training : https://www.edureka.co/python ***** This Edureka video on "Python Certification" will give you a complete insight of Python Certification, how to crack it and hands-on knowledge on various real-time projects offered by Edureka Python Certification Training. This video will help you to learn following topics: 1. Why go for Python Certification? 2. Advantages of Python 3. Career Opportunities 4. Python Job Profiles 5. Edureka: Python Projects Subscribe to our channel to get video updates. Hit the subscribe button above. Check out our Python Training Playlist: https://goo.gl/Na1p9G #PythonCertification #PythonForDataScience #PythonForBeginners #PythonOnlineTraining How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 7069 edureka!
How Twitter users connect w/TV. What's specifically wrong with Big Data. (RBDR 9.30.2015)
Today on RBDR: 1) Twitter reveals results of a study that shows a new dramatic link between television viewing and tweeting. (Story link: http://fortune.com/2015/09/21/here-are-some-tv-tips-from-twitter-everyones-favorite-second-screen/) 2) If you have a strong and positive belief about the current state of Big Data, you will want to think again after you hear our interview with Slater Victoroff, CEO of indico Data Solutions, on RBDR. (Story link: http://techcrunch.com/2015/09/10/big-data-doesnt-exist/#.nzwjs5:Q70F) RBDR is sponsored by KL Communications, a collaborative research agency specializing in customer co-creation through its proprietary CrowdWeaving service. KL is pleased to provide a white paper with insights into the unique ability of its Crowdweaving co-creation service to attract greater participation from particularly creative individuals. Visit http://klcommunications.com and http://klcommunications.com/whitepaper.pdf. Don't spend your time "searching" for today's RBDR video. Subscribe to receive a personal email as soon as the new RBDR is uploaded. Click here: http://ow.ly/CfFWE
Amzone Data Scraping Demo
Amzone Data Scraping Demo ÿou can get all catgorey data from amzone without any blocking . you can see this all data in gried view. you can get all data of Product like Product Name, Price, SKu,Product Information , Product Descriptions you can download all data in excel,csv, and pdf format i can make this type custom software for any website . like LinkedIn , Amzone ,kingCountry , 99 Acress etc. you can contact me on [email protected] and [email protected] conatct no : +48 739503364 (For Call) +91 9427303855 (Whats App) Subscribe Now: https://goo.gl/QNnRy1 🔔 Stay updated! Keywords data Scraping,data cloning,Ecommerce Data,Product Cloning,website Scraping,scraping,scrapping a car,scraping data,scraping data from websites,scraping data from linkedin,scraping data from amazon,data scraping,data scraping business,scraping big data,best data scraping,scraping data excel,scraping real estate data,e commerce data scraping,scraping data in chrome,web data scraping jobs,scraping data mining,Programming,web scraping with python
Views: 2282 darshit shah
Life Inside a Secret Chinese Bitcoin Mine
Subscribe to Motherboard Radio today! http://apple.co/1DWdc9d In October of last year Motherboard gained access to a massive, secretive Bitcoin mine housed within a repurposed factory in the Liaoning Province in rural northeast China. This is the infrastructure that keeps the digital currency’s decentralized network up and running, and its operators are profiting big time. The mine we visited is just one of six sites owned by a secretive group of four people, part of a colossal mining operation that, as of our visit, cumulatively generated 4,050 bitcoins a month, equivalent to a monthly gross of $1.5 million. Read more on Motherboard - http://bit.ly/Chinese-Bitcoin-Mine Up Next: The Beaver Slayers of Patagonia - http://bit.ly/Beaver-Slayers Subscribe to MOTHERBOARD: http://bit.ly/Subscribe-To-MOTHERBOARD Follow MOTHERBOARD Facebook: http://www.facebook.com/motherboardtv Twitter: http://twitter.com/motherboard Tumblr: http://motherboardtv.tumblr.com/ Instagram: http://instagram.com/motherboardtv More videos from the VICE network: https://www.fb.com/vicevideos
Views: 4115541 Motherboard
Data Mining Research Projects | Data Mining Research Thesis | Data Mining Research Code Projects
Contact Best Matlab Simulation Projects Visit us: http://matlabsimulation.com/
Views: 24 matlab simulation
Java Data Science Solutions - Analyzing Data : Parsing and Extracting Data | packtpub.com
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2uy2NVK]. This section familiarizes us with different data file types, such as PDF, ASCII, CSV, TSV, XML, and JSON. It also covers extracting web data. • Parsing Comma-Separated and Tab-Separated Value Files Using Univocity • Parsing XML Files Using JDOM • Writing JSON Files Using JSON.Simple • Reading JSON Files Using JSON.Simple • Extracting Web Data from a URL Using Jsoup • Extracting Web Data from a Website Using Selenium Web Driver • Reading Table Data from a MySQL Database For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 652 Packt Video
Introduction to Text Analytics with R: Our First Model
This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: - Tokenization, stemming, and n-grams - The bag-of-words and vector space models - Feature engineering for textual data (e.g. cosine similarity between documents) - Feature extraction using singular value decomposition (SVD) - Training classification models using textual data - Evaluating accuracy of the trained classification models Part 4 of this video series includes specific coverage of: - Correcting column names derived from tokenization to ensure smooth model training. - Using caret to set up stratified cross validation. - Using the doSNOW package to accelerate caret machine learning training by using multiple CPUs in parallel. - Using caret to train single decision trees on text features and tune the trained model for optimal accuracy. - Evaluating the results of the cross validation process. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. 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/2s7hrnH See what our past attendees are saying here: http://bit.ly/2sFalYG -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.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: 12256 Data Science Dojo
Introduction to HDF5 | Quincey Koziol, The HDF Group
The slide deck for this presentation can be viewed here: http://extremecomputingtraining.anl.gov/files/2014/01/QKHDF5-Intro-v2.pdf Presented at the Argonne Training Program on Extreme-Scale Computing, Summer 2014. For more information, visit: http://extremecomputingtraining.anl.gov/
Views: 7367 ANL Training