In-Database Data Mining Using Oracle Advanced Analytics Option for Classificaton using Insurance Use Case
Views: 4966 Charles Berger
Market Basket Analysis presentation and demo using Oracle Advanced Analytics
Views: 10401 Charles Berger
Key Takeaways for the session : Breaking junk using formula and generate reports VBA to manipulate data in required format Data extraction from external files Who should attend? People from any domain who work on data in any form. Good for Engineers, Leads, Managers, Sales people, HR, MIS experts, Data scientists, IT Support, BPO, KPO etc. Feel free to write me at [email protected]
Views: 23731 xtremeExcel
Overview presentation & demonstration of Oracle Advanced Analytics Option.
Views: 7949 Charles Berger
For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html This video is about bioinformatics databases like NCBI, ENSEMBL, ClustalW, Swisprot, SIB, DDBJ, EMBL, PDB, CATH, SCOPE etc. Bioinformatics Listeni/ˌbaɪ.oʊˌɪnfərˈmætɪks/ is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications. In order to study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures. The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include: the development and implementation of tools that enable efficient access to, use and management of, various types of information. the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets. For example, methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein--protein interactions, genome-wide association studies, and the modeling of evolution. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Over the past few decades rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 89357 Shomu's Biology
Oracle Big Data Analytics Demo mining remote sensor data from HVACs for better customer service. Oracle Advanced Analytics's Data Mining GUI is used to mine data from remote devices to find problems and improve product customer service. In the scenario, Oracle's Big Data Appliance is positioned to be the initial data collector/aggregator and then the data that is loaded into the Oracle Database. We perform our data mining/predictive analytics on the data while it resides inside the Oracle Database thereby transforming the Database into an Analytical Database.
Views: 2660 Charles Berger
Speaker: Dejan Sarka Excel is “the” analytical tool in Microsoft’s suite for advanced analysts. Of course, you know that Excel 2013 includes PowerPivot and Power View add-ins out of the box. You also may have heard that you can use big data and Azure DataMarket data in Excel and that you can mash up data from different sources. However, you probably don’t know how to use PowerPivot data for data mining, how to combine big data with PowerPivot data, how to use data mining models in PowerPivot, or how to mash up data when you don’t have common identification. This session is not about introducing the cool new features; instead, it will focus on the most advanced part of Excel analytics: data mining with Excel.
Views: 281 PASStv
Emily Mower - Provost, Computer Science and Engineering at the University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Views: 1376 Michigan Engineering
This is Part 1 of my Fraud and Anomaly Detection using Oracle Advanced Analytics presentations and demos series. Hope you enjoy! www.twitter.com/CharlieDataMine
Views: 6019 Charles Berger
http://www.salford-systems.com/products/spm Data mining for beginners looking for short introduction videos on how to use advanced data mining software like experts. Regression, decision trees, tree ensembles, modern hybrid modeling, regularized regression and more! The Salford Predictive Modeling software suite incorporated all of the above, and Salford Systems offers data mining tutorials to all levels of data mining practitioners and statisticians.
Views: 131 Salford Systems
Deep Dhillon, former Chief Data Scientist at Alliance Health Networks (now at http://www.xyonix.com), presents a talk titled "Mining Unstructured Healthcare Data" to computational linguistics students at the University of Washington on May 8, 2013. Every day doctors, researchers and health care professionals publish their latest medical findings continuously adding to the world's formalized medical knowledge represented by a corpus of millions of peer reviewed research studies. Meanwhile, millions of patients suffering from various conditions, communicate with one another in online discussion forums across the web; they seek both social comfort and knowledge. Medify analyzes the unstructured text of these health care professionals and patients by performing a deep NLP based statistical and lexical rule based relation extraction ultimately culminating in a large, searchable index powering a rapidly growing site trafficked by doctors, health care professionals, and advanced patients. We discuss the system at a high level, demonstrate key functionality, and explore what it means to develop a system like this in the confines of a start up. In addition, we dive into details like ground truth gathering, efficacy assessment, model approaches, feature engineering, anaphora resolution and more. Need a custom machine learning solution like this one? Visit http://www.xyonix.com.
Views: 3725 zang0
Back Azimuth http://www.back-azimuth.com now offers its VOCDMS (Voice of the Consumer Data Management System) as part of its advanced keyword management services. Back Azimuth's VOCDMS is a cloud-based data-mining platform that aggregates keyword and social media data into a single web-based application enabling marketers to better understand their prospects while identifying performance gaps that are overlooked by other tools. Search Marketers have dreamed of a single database for managing all of their keyword data -- paid, organic and site search. It is now possible. Using the scale of the cloud and Back Azimuth's VOCDMS database solution, we are able to combine the data and then mine it in hundreds of different ways to help marketers find opportunities which were never possible before. In this video, Bill Hunt, founder and president, Back Azimuth, discusses his company's consulting for a huge online retailer, Yummie Tummie. 0:09 Bill Hunt, founder and president, Back Azimuth Consulting, discusses the ideal target customer who has more than a million keywords that can benefit from Back Azimuth advanced keyword management software or VOCD MS. These are large travel, computer, etail, and retail companies that get a large number of keyword queries that would bring people to their products. 0:22 Bill Hunt speaks to a group of digital marketers at SES. Bill asks the audience about their client-base and how many keywords they monitor and where the opportunities that are not being taken advantage of are. 0:47 Bill expands on the challenges faced by companies that have a million or more keywords and how there are hundreds of thousands of ways in which consumers will look for their product and each of these keywords has a different value. Some of the keyword queries will convert, some won't. The best thing VOCDMS can do for companies is to help them understand how to find those keywords that will convert and generate the most value for them. 01:11 Yummie Tummie (www.yummielife.com/) case study -- Bill begins by presenting a company product which could be referenced using different descriptions. These product names included girdle, shapewear, shaping underwear, shapewear intimates, and Spanx. The age differential is significant, according to Bill, because anyone over the age of 30 would describe the product as a girdle and anyone under the age of 30 would call it spanx or shapewear. The brand, in this case, wants to call it, Shapewear Intimate. Bill describes how the VOCDMS keyword management software discovered that 75% of the entire search related to shapewear came down to a simple formula: clothing type + shapewear. Bill says you have to match the interest and intent to what your prospective customer wants. 04:15 Bill describes how his company revamped Yummie Tummie's keyword research by rebuilding the company's entire website creating a whole new site taxonomy which translates into an experience that provides the user with progressive levels of information about shapewear. 07:14 Bill discusses how the VOCDMS solution mines big keyword data, by managing it in a single database with filters that look for the needle in a haystack. VOCDMS is used by large travel companies, electronic manufacturers, similar to an HP or Dell, that have large sets of data. Travelocity is a client that has tons of keyword volume from different destinations. It takes advantage of VOCDMS by targeting their product to the sets of keywords that their customer base uses for searching. 08:37 To learn more about how your company can benefit from advanced keyword management and VOCDMS, please visit: http://back-azimuth.com/contact Phone: 866-599-8225 If you are interested in a pilot project and want to see how Back Azimuth Consulting's Voice of the Consumer Data Management System can work for you, all that is required is access to your main data such as web analytics, paid search data, and any kind of segmentation or keyword work that you have already done previously.
Views: 799 Bill Hunt
Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 1: The data mining process 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: 15837 WekaMOOC
A keynote address from Strata + Hadoop World Europe 2014 in Barcelona, "Predictive Analytics in the Cloud: Predicting Football." Watch more from Strata Europe 2014: http://goo.gl/uqw6WS Visit the Strata website to learn more: http://strataconf.com/strataeu2014/ Subscribe for more from the conference! http://goo.gl/szEauh How can you turn raw data into predictions? How can you take advantage of both cloud scalability and state-of-the-art Open Source Software? This talk shows how we built a model that correctly predicted the outcome of 14 of 16 games in the World Cup using Google’s Cloud Platform and tools like iPython and StatsModels. I’ll also demonstrate new tools to integrate iPython with Google’s cloud and how you can use the same tools to make your own predictions. About Jordan Tigani (Google): Jordan Tigani has more than 15 years of professional software development experience, the last 4 of which have been spent building BigQuery. Prior to joining Google, Jordan worked at a number of star-crossed startups, where he learned to make data-based predictions. He is a co-author of Google BigQuery Analytics. When not analyzing soccer matches, he can often be found playing in one. Stay Connected to O'Reilly Media by Email - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia
Views: 87066 O'Reilly
This is the first chapter in the web lecture series of Prof. dr. Bart Baesens: Introduction to Database Management Systems. Prof. dr. Bart Baesens holds a PhD in Applied Economic Sciences from KU Leuven University (Belgium). He is currently an associate professor at KU Leuven, and a guest lecturer at the University of Southampton (United Kingdom). He has done extensive research on data mining and its applications. For more information, visit http://www.dataminingapps.com In this lecture, the fundamental concepts behind databases, database technology, database management systems and data models are explained. Discussed topics entail: applications, definitions, file based vs. databased data management approaches, the elements of database systems and the advantages of database design.
Views: 297824 Bart Baesens
Fast Data as a different approach to Big Data for managing large quantities of "in-flight" data that help organizations get a jump on those business-critical decisions. Difference between Big Data and Fast Data is comparable to the amount of time you wait downloading a movie from an online store and playing the dvd instantly. Data Mining as a process to extract info from a data set and transform it into an understandable structure in order to deliver predictive, advanced analytics to enterprises and operational environments. The combination of Fast Data and Data Mining are changing the "Rules"
Views: 890 Nino Guarnacci
What is partition and why use it? Creating a Partition, Partitioning method - ETIT 427 - ADBA - IP University Syllabus For Students of B.Tech, B.E, MCA, BCA, B.Sc., M.Sc., Courses - As Per IP University Syllabus and Other Engineering Courses
Views: 5948 Easy Engineering Classes
Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1515289 ExcelIsFun
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: 76509 Skillshare
The Excel Data Mining Addin can be used to build predictive models such as Decisions Trees within Excel. The Excel Data Mining Addin sends data to SQL Server Analysis Services (SSAS) where the models are built. The completed model is then rendered within Excel. I also have a comprehensive 60 minute T-SQL course available at Udemy : https://www.udemy.com/t-sql-for-data-analysts/?couponCode=ANALYTICS50%25OFF
Views: 73563 Steve Fox
Advances in data mining and knowledge discovery have transformed the way Web sites are designed. However, while visual presentation is an intrinsic part of the Web, traditional data mining techniques ignore render-time page structures and their attributes. This paper introduces design mining for the Web: using knowledge discovery techniques to understand design demographics, automate design curation, and support data-driven design tools. This idea is manifest in Webzeitgeist, a platform for large-scale design mining comprising a repository of over 100,000 Web pages and 100 million design elements. This paper describes the principles driving design mining, the implementation of the Webzeitgeist architecture, and the new class of data-driven design applications it enables.
Views: 1449 StanfordHCI
More Details - http://www.bisptrainings.com
Views: 4932 Amit Sharma
2013 - Mining Representative Movement Patterns through Compression NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. The 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), Goal Coast, Australia, April 2013. (acceptance rate: 11.3%) 2012 - Mining Time Relaxed Gradual Moving Object Clusters NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2012), Redondo Beach, California, November 2012. [pdf] [demo] [code] (acceptance rate: 22%) 2012 - GeT_Move: An Efficient and Unifying Spatio-Temporal Pattern Mining Algorithm for Moving Objects NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 11th International Symposium on Intelligent Data Analysis (IDA 2012), Helsinki, Finland, October 2012. 2012 - Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Pattern Mining System NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2012), Demo Paper, Bristol, UK, September 2012.
Views: 499 nhathai phan
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: 56505 Michael Lamont
For more great presentations, join us for Breakpoint 2013 (October 24 & 25) and Ruxcon 2013 (October 26 & 27) in Melbourne, Australia. Further information and registration available at www.ruxconbreakpoint.com and www.ruxcon.org.au
Views: 737 Ruxcon Mc'Gavin
Use the Excel Data Mining add-in for SQL 2012 Analysis Services. See how simple it is to build a predictive model that forecasts sales or other values based on historical data.
Views: 12660 Edward Kench
I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 423 TheKunnu89
In this webinar Chris covers; The difference between Allowed Sets and Denied Sets in dimension security Handling security-related errors in your MDX calculations. The different ways of implementing dynamic security. Why you should avoid cell security, and how (in some cases) you can replace it with dimension security.
Views: 2231 SQL Relay
Junling Hu presents a high level overview of data mining at the "Data Mining Case Study" meetup at the HackerDojo in Mountain View, Ca on Aug 17th 2013.
Views: 1510 Stoney Vintson
Business users can be empowered to do more sophisticated analysis without resorting to code. Data science teams can develop and test gold-standard simulations and machine learning algorithms while making them directly available on the systems used by end users. In addition to querying data with traditional relational functionality, users could also call a Monte Carlo simulation, or other custom algorithms, straight from their BI dashboard.
Views: 285 Kinetica
Вебинар посвящен современным подходам и стратегиям применения методов Data Mining для решения актуальных задач в различных областях: бизнесе/маркетинге, финансах, банковской области, телекоммуникациях и др. http://www.statsoft.ru/products/STATISTICA_Data_Miner/
Views: 2051 StatSoftRussia
Graduate student Jing discusses her data mining term project which uses the Apriori algorithm (market basket analysis) to mine association rules from a set of database transactions.
Views: 14563 CSDepartment St. Joes
Presenters: Kevin Kline Aaron Bertrand
Views: 249360 PASStv
Database systems organize and retrieve information, allowing the user to access the desired information easily and efficiently. Topics include: relational data model; SQL; ER modeling; relational algebra; data normalization; transactions; objects in the database; data security and integrity; data warehousing, OLAP, and data mining; distributed databases; and study of a specific commercial database system. (4 units) Prerequisite: CS 401 or consent of the department faculty. Video By Professor Premchand Nair.
Views: 971 mumcompro
Organizations increasingly rely on business intelligence and data mining systems to provide them the relevant information and insights to support decision-making. This course covers the fundamental concepts and tools for managing and mining data to generate new insights. Topics include design and development of data warehouses; and data mining tools, including statistical and machine learning techniques, to identify new patterns. Popular data mining systems will be used to develop practical skills. (4 credits) Prerequisite: Consent of the faculty. Video by Professor Anil Maheshwari.
Views: 822 mumcompro