List of SQL commands for Commonly Used Excel Operations Introduction Learning SQL after Excel couldn’t be simpler! I’ve spent more than a decade working on Excel. Yet, there is so much to learn. If you dislike coding, excel could be your rescue into data science world (to some extent). Once you understand Excel operations, learning SQL is very easy. Why can’t you use Excel for serious data science work? Now at this stage, you might ask, why can’t I use excel for all my work. For large datasets, excel is not effective. Moving to SQL would address point 1 and point 2 to some extent. If you don’t know SQL yet and have worked in Excel, you can get started right now. Related : Basics of SQL and RDBMS for Beginners List of Common Excel Operations Here is the list of commonly used excel operation. View DataSort DataFilter DataDelete RecordsAdd RecordsUpdate Data in Existing RecordShow Unique ValuesWrite an expression to generate new columnLookUp data from another tablePivot Table 1. In excel, we can view all the records directly. Syntax: Exercise: A. B.
Data Mining Research - www.dataminingblog.com | Data Mining Blogs If you're new here, you may want to subscribe to my RSS feed. Thanks for visiting! I posted an earlier version of this data mining blog list in a previously on DMR. Here is an updated version (blogs recently added to the list have the logo “new”). I will keep this version up-to-date. Abbott Analytics: both industry and research oriented posts covering any topic related to data mining (Will Dwinnell and Dean Abbott)A Blog by Tim Manns: as defined in it’s subtitle, this blog deals with “data mining, analysing terabyte data warehouses, using SPSS Clementine, telecommunications, and other stuff” (Tim Manns).AI, Data mining, Machine learning and other things (Markus Breitenbach): Markus writes about machine learning with a focus on statistics, security and AI.anuradha@NumbersSpeak: A blog on analytics applications, statistics and data mining (Anuradha Sharma).Blog by bruno: This blog covers a very large number of topics including web data analysis and data visualization. Ryan Rosario
Data Mining Algorithms In R In general terms, Data Mining comprises techniques and algorithms, for determining interesting patterns from large datasets. There are currently hundreds (or even more) algorithms that perform tasks such as frequent pattern mining, clustering, and classification, among others. Understanding how these algorithms work and how to use them effectively is a continuous challenge faced by data mining analysts, researchers, and practitioners, in particular because the algorithm behavior and patterns it provides may change significantly as a function of its parameters. In practice, most of the data mining literature is too abstract regarding the actual use of the algorithms and parameter tuning is usually a frustrating task. On the other hand, there is a large number of implementations available, such as those in the R project, but their documentation focus mainly on implementation details without providing a good discussion about parameter-related trade-offs associated with each of them.
Home Page for ISO/IEC 11179 Information Technology -- Metadata registries Last Update: 2015-11-05 ISO/IEC 11179, Information Technology -- Metadata registries (MDR) The 11179 standard is a multipart standard that includes the following parts: · Part 1: Framework · Part 2: Classification · Part 3: Registry metamodel and basic attributes · Part 4: Formulation of data definitions · Part 5: Naming and identification principles · Part 6: Registration · Part 7: Datasets 11179-1: Framework This part of ISO/IEC 11179 introduces and discusses fundamental ideas of data elements, value domains, data element concepts, conceptual domains, and classification schemes essential to the understanding of this set of standards and provides the context for associating the individual parts of ISO/IEC 11179. Project Editor: Dan GILLMAN Edition 3 – under development: Link to ISO Project Portal Open Issues with Edition 2 are posted on the WG2 Issue Forum. Published Editions: Return to top of page. 11179-2: Classification Project Editorss: Frank FARANCE and Tae-Sul SEO Rationale for 3rd edition:
PigTools - Apache Pig UDF Collections. DataFu DataFu is Linkedin's collection of Pig UDFs, which has become an Apache Incubator project. ( Elephant-Bird Twitter's library of LZO and/or Protocol Buffer-related Hadoop InputFormats, OutputFormats, Writables, Pig LoadFuncs, HBase miscellanea, etc. RPM and Debian packages for Elephant Bird can be found at Pygmalion A project to facilitate using Pig with Apache Cassandra. Tools that help run Pig workflows Amazon Amazon Elastic MapReduce makes it easy to launch Pig in interactive or batch mode in AWS. 'hamake' utility allows you to automate incremental processing of datasets stored on HDFS using Hadoop tasks written in Java or using PigLatin scripts. Mortar Data Mortar Framework Piglet PigPy Eclipse
Weka 3 - Data Mining with Open Source Machine Learning Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. Weka is open source software issued under the GNU General Public License. Yes, it is possible to apply Weka to big data! Data Mining with Weka is a 5 week MOOC, which was held first in late 2013.
The Difference Between Big Data and a Lot of Data Bernard Marr The term “big data” has been around for a while now, but I still come across people who make the same basic mistake when someone asks them to explain what exactly it is. The problem, as I have pointed out in the past, is due to the name. Big data was never meant to be purely about the size of the data. Right from the start, when the first attempts were made to codify the “rules” of big data, this was the case. Gartner’s famous “3 V’s” of big data were, in fact, minted to make this very point. So, from the beginning, big data should have more accurately been labelled “big, fast and varied data” – although of course that doesn’t sound so catchy! So, the problem is this: When clients approach me to work with them, they often say, “We already do big data.” What they have is a lot of data. “Variety” in particular is a very important element of big data. A lot of data, on their own, are worthless. There’s nothing at all wrong with collecting a lot of data.
HADOOP, HIVE, Map Reduce avec PHP : part 1 Lorsque l’on commence à débattre sur le «BIG DATA», on finit toujours par discuter du stockage. «Hadoop», de par son architecture et son fonctionnement, n’impose aucune contrainte technique sur le stockage de la donnée. Intégrant nativement le concept de Map & Reduce, «Hadoop» est un candidat sérieux pour les besoins de stockage massif et d’extraction qu’impose le «BIG DATA». Architecture technique Hadoop Le schéma ci-dessus décrit l’architecture technique d’une entreprise de e-commerce vendant des produits alimentaires pour animaux. installation d’«Hadoop»,découverte et manipulation d’«HDFS»,réalisation de Map et de Reduce en PHP avec «Hadoop streaming»,découverte de «HIVE», Installation du framework HADOOP Apache «Hadoop» est un framework écrit en JAVA qui permet entre autre, de distribuer au sein d’un cluster, des taches de type Map Reduce et d’y stocker le résultat final. Un nombre important de projets OpenSources s’appuyant sur le framework ont vu le jour : Service SSHd
Octave GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is distributed under the terms of the GNU General Public License. Version 4.0.0 has been released and is now available for download. An official Windows binary installer is also available from A list of important user-visible changes is availble at by selecting the Release Notes item in the News menu of the GUI, or by typing news at the Octave command prompt. Thanks to the many people who contributed to this release!