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Data Mining with Data Applied
 
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Data Applied (http://www.data-applied.com) revolutionizes data-driven decision making by integrating rich analytics, data mining, and information visualization capabilities - all using a zero footprint Web interface, collaboration features, and a secure XML Web API. By extracting valuable knowledge from data in domains as varied as Sales, Marketing, Engineering, Social Sciences or Non-Profit, we help organizations make better data-driven decisions and improve efficiency. See how we can help you get more from your data.
Views: 4420 Data Applied
Data-mining Definition - What Does Data-mining Mean?
 
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Go to http://www.corporatevocabulary.com for the complete lesson on Data-mining and a full course to give you the vocabulary and communication skills of a six-figure earner. In this video we teach you the definition of Data-mining.
Views: 3316 ereflect
Nick Morris Applied Data Mining
 
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Nick Morris's Video presentation for this Applied Data Mining Class at Rockhurst U
Views: 7 Nick Morris
2018 Crypto Valley Conference: Data mining for detecting Bitcoin Ponzi schemes
 
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The 2018 Crypto Valley Conference in collaboration with Lucerne University of Applied Sciences and Arts has invited leading speakers, researchers and academics to share their latest discoveries in the domain of blockchain technology. This annual event includes a business and academic track. The scientific publications, presented at the conference can be found on IEEE Xplore Digital Library.
Views: 48 Crypto Valley
Veronika  Belokhvostova: Applied Data Strategy
 
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Veronika Belokhvostova is VP, Analytics and Strategy at Hotwire. Held at the Haas School of Business, University of California, Berkeley, the Data Science & Strategy Lecture Series examines the evolving role of "big data" and analytics in managerial decision-making. In this playlist, lecture series host Prof. Greg La Blanc interviews industry executives and practitioners on key topics in data science, including data mining, machine learning, visualization, advanced statistics and more. For more information please visit: http://businessinnovation.berkeley.edu/data-science-strategy/lecture-series/.
Views: 776 Berkeley Haas
Scalability and Efficiency on Data Mining Applied to...
 
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Google Tech Talks August 16, 2007 ABSTRACT The Internet went well beyond a technology artefact, increasingly becoming a social interaction tool. These interactions are usually complex and hard to analyze automatically, demanding the research and development of novel data mining techniques that handle the individual characteristics of each application scenario. Notice that these data mining techniques, similarly to other machine learning techniques, are intensive in terms of both computation and I/O, motivating the development of new paradigms, programming environments, and parallel algorithms that support scalable and efficient applications. In this talk we present some results that justify not...
Views: 2428 GoogleTechTalks
Veronika Dulíková, Radek Mařík - Data Mining Applied to Ancient Egypt Data in the Old Kingdom
 
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Veronika Dulíková – Radek Mařík Data Mining Applied to Ancient Egypt Data in the Old Kingdom
Views: 140 CONPRA
Introduction to Text and Data Mining
 
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Heard about Text and Data Mining (TDM) and wondering if it might be a good fit for your research? Find out what text and data mining is and how it can usefully be applied in a research context. Also learn about data sources for text and data mining projects and support, tools, and resources for learning more.
Views: 86 UniSydneyLibrary
Big Data: Mining Football Statistics
 
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Data Mining Final Project for Big Data INSY 4970 at Auburn University
Views: 34131 wwl0002
How data analytics can be applied in internal audit
 
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Janet Lewell and Terry Hatherell discuss the role of data analytics in internal audit. With the volume of data in business growing at unprecedented rates, companies rely on internal audit to leverage that information and data to provide more insight into their business. The more advanced, highly effective internal audit departments are more evolved in their use of data analytics. They understand it is not limited to just testing, but can be used for risk identification, planning and enhanced reporting. Learn how internal audit must look beyond the past and become predictive about the future. Also, be sure to engage with Deloitte Canada on: http://bit.ly/TwitterDeloitte http://on.fb.me/FacebookDeloitte http://linkd.in/LinkedInDeloitte iDeas Blog http://ow.ly/8XhNO
Views: 18905 Deloitte Canada
The Best Way to Visualize a Dataset Easily
 
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In this video, we'll visualize a dataset of body metrics collected by giving people a fitness tracking device. We'll go over the steps necessary to preprocess the data, then use a technique called T-SNE to reduce the dimensionality of our data so we can visualize it. Code + challenge for this video: https://github.com/llSourcell/visualize_dataset_demo Keagan's winning code: https://github.com/WeldFire/prepare_dataset_challenge Vishal's runner-up code: https://github.com/erilyth/Pokemon-Type-Classification-Challenge Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Live T-SNE demo in the browser: http://cs.stanford.edu/people/karpathy/tsnejs/ More learning resources: https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm https://indico.io/blog/visualizing-with-t-sne/ http://blog.applied.ai/visualising-high-dimensional-data/ http://machinelearningmastery.com/visualize-machine-learning-data-python-pandas/ 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/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 223682 Siraj Raval
Artificial Intelligence Vs Machine Learning Vs Data science Vs Deep learning
 
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For More information Please visit https://www.appliedaicourse.com
Views: 253044 Applied AI Course
Scalability and Efficiency on Data Mining Applied to Internet Applications
 
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Google Tech Talks August 16, 2007 ABSTRACT The Internet went well beyond a technology artefact, increasingly becoming a social interaction tool. These interactions are usually complex and hard to analyze automatically, demanding the research and development of novel data mining techniques that handle the individual characteristics of each application scenario. Notice that these data mining techniques, similarly to other machine learning techniques, are intensive in terms of both computation and I/O, motivating the development of new paradigms, programming environments, and parallel algorithms that support scalable and efficient applications. In this talk we present some results that justify not only the need for developing these new techniques, as well as their parallelization. Wagner Meira Jr. obtained his PhD from the University of Rochester in 1997 and is currently Associate Professor at the Computer Science Department at Universidade Federal de Minas Gerais, Brazil. His research focuses on scalability and efficiency of large scale parallel and distributed systems, from massively parallel to Internet-based platforms, and on data mining algorithms, their parallelization, and application to areas such as information retrieval, bioinformatics, and e-governance. Google engEDU Speaker: Wagner Meira Jr
Views: 388 GoogleTalksArchive
Bruno Goncalves, Anastasios Noulas: Mining Georeferenced Data
 
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PyData NYC 2015 The democratization of GPS enabled devices has led to a surge of interest in the availability of high quality geocoded datasets. This data poses both opportunities and challenges for the study of social behavior. The goal of this tutorial is to introduce its attendants to the state-of-the-art in the mining and analysis in this new world of spatial data with a special focus on the real world. In this tutorial we will provide an overview of workflows for location rich data, from data collection to analysis and visualization using Python tools. In particular: Introduction to location rich data: In this part tutorial attendees will be provided with an overview perspective on location-based technologies, datasets, applications and services Online Data Collection: A brief introductions to the APIs of Twitter, Foursquare, Uber and AirBnB using Python (using urllib2, requests, BeautifulSoup). The focus will be on highlighting their similarities and differences and how they provide different perspectives on user behavior and urban activity. A special reference will be provided on the availability of Open Datasets with a notable example being the NYC Yellow Taxi dataset (NYC Taxy) Data analysis and Measurement: Using data collected using the APIs listed above we will perform several simple analyses to illustrate not only different techniques and libraries (geopy, shapely, data science toolkit, etc) but also the different kinds of insights that are possible to obtain using this kind of data, particularly on the study of population demographics, human mobility, urban activity and neighborhood modeling as well as spatial economics. Applied Data Mining and Machine Learning: In this part of the tutorial we will focus on exploiting the datasets collected in the previous part to solve interesting real world problems. After a brief introduction on python’s machine learning library, scikit-learn, we will formulate three optimization problems: i) predict the best area in New York City for opening a Starbucks using Foursquare check-in data, ii) predict the price of an Airbnb listing and iii) predict the average Uber surge multiplier of an area in New York City. Visualization: Finally, we introduce some simple techniques for mapping location data and placing it in a geographical context using matplotlib Basemap and py.processing. Slides available here: http://www.slideshare.net/bgoncalves/mining-georeferenced-data Code here: https://github.com/bmtgoncalves/Mining-Georeferenced-Data
Views: 1200 PyData
Data Mining - Foundations of Learning to Rank: Needs & Challenges | Lectures On-Demand
 
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Ambuj Tewari - EECS 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: 3761 Michigan Engineering
Reasons to study the Master in Data mining applied to the Medicine
 
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Discover the reasons to study the Master in Data Mining applied to the Medicine of the hand of his academic coordinators. It is a unique formative program for professionals of the Health that they want to interpret and to extract profit of the information of his patients with the help of the technology.
A Literature Review on Data Mining Techniques applied in Health Care Decision Making
 
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Literature Review on Data Mining Techniques applied in Health Care Decision Making
Views: 1286 mahesh l
Association Mining with Data Applied
 
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Data Applied (http://www.data-applied.com) revolutionizes data-driven decision making by integrating rich analytics, data mining, and information visualization capabilities - all using a zero footprint Web interface, collaboration features, and a secure XML Web API. By extracting valuable knowledge from data in domains as varied as Sales, Marketing, Engineering, Social Sciences or Non-Profit, we help organizations make better data-driven decisions and improve efficiency. See how we can help you get more from your data.
Views: 645 Data Applied
What is Data Mining? - February 19, 2008
 
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Association Labratory President and CEO Dean West discusses Data Mining and how it can be applied to associations.
Views: 46335 Association Forum
GEOBIA2012 - Data mining techniques and GEOBIA applied to land cover mapping
 
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GEOBIA2012 - Rio de Janeiro, Brazil Data mining techniques and GEOBIA applied to land cover mapping C. N. Francisco (a), C. M. Almeid (a,b) (a) Dept. Geoenvironmental Analysis, Fluminense Federal University, Brazil (b) National Institute for Space Research, Division for Remote Sensing, Brazil http://www.inpe.br/geobia2012/
Views: 204 GEOBIAinRIO
Knowledge Mining: use AI to search on your data, regardless of format
 
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Join Liam Cavanagh, PM on the Applied AI & Search team, and learn about the latest technologies and use cases intelligent search. For all the sessions: https://channel9.msdn.com/Events/Cognitive-Services/Cognitive-Services-Live
Views: 1238 Microsoft Developer
Data Analyst Job Description | What 4 Skills Will You Need To Be A Data Analyst?
 
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In this video we are going to define the job description of a data analyst, what a data analyst does, and the best online course to become a data analyst. ► Full Playlist Explaining Data Jargon ( http://bit.ly/2mB4G0N ) ► Top 4 Best Laptops for Data Analysts ( https://youtu.be/Vtk50Um_yxA ) ► Break Into the Data Industry with the best data analytics online learning resources from Edureka! ( http://bit.ly/2yCbsac ) --- affiliate link to help support this channel!^ Currently the average pay for a data analyst is $76,419 on the button, according to glassdoor I receive a lot of questions about what it takes to become a data analyst and what is a data analyst. Clearing up what a data analyst does everyday and what that description means to someone looking to enter the data science industry What will you actually be asked to do on the day to day as a data analyst. ► Top 4 Responsibilities in the Daily Life of a Data Analyst: 1 ) Mathematics Although mathematics only makes up about 20% of the day to day life of a data analyst. It is still important to have a strong understanding of the foundations of mathematics. - Addition - Subtraction - Multiplication - Division - Most Importantly --- Statistics Data analytics is all about statistics. Most of the statistics will be handled by the tools you are working with, but in order to be a great data analyst it is best to know why the tools are producing specific results. A strong understanding of statistics will be useful to you. 2 ) Computer Programming You must be able to work proficiently in one or more computer programming languages. This make up for roughly 60%-70% of your daily work. in order to analyze data it must be queried (drawn) from a large data warehouse. You will use computer programming languages such as SQL, Python, and R to query data. Before we move on let me define the term Query, if it does not resonate with you. You need strong computer programming skills in order to accomplish this task. As a data analyst you will do a lot of drawing and analyzing data. ► For more info on databases, SQL, and other jargon check out our Video Series on Data Jargon ( https://www.youtube.com/playlist?list=PL_9qmWdi19yDhnzqVCAhA4ALqDoqjeUOr ) 3 ) Know the Tools of the Trade Once you query data from the database onto your workspace you will begin to utilize data analytics tools to process, scrub, and analyze data (data Jargon explained on our Video series ^^^). You will be able to perform these tasks by using tools like Hadoop, Open Refine, Tableau, Apache Spark, etc... As you process the data you will begin to see connections between the data sets. You will see some of the following errors and you will want to remove these in order to ensure that your data analysis is accurate: - Duplicated data - Improperly formatted data - Incomplete data - Inaccurate data - This data will corrupt your findings and could possibly lose you client or employer millions of dollars. Make sure you know how to use those data analytics tools WELL! 4 ) Communicate and Present Insights Data Analyst will also be called upon to clearly and consciously present your research to clients, managers, or executives. Ok, now I know you are curious if you are capable of learning all of these crucial skills. Yes, you can, but there is a clause. You have to learn from the best. The guys over at Edureka.co are the leading professionals in the big data training industry. Based out of India, home to over 101,000 individuals in the data science industry (at the time of this writing). They are eager to make a way for themselves in the new digital economy. They are on the cutting edge of data analytics and eager to teach it to anyone worldwide. Testimonies of increased salaries, new employment, and 597,089 (updated) satisfied learners make edureka the best choice to learn the skills you need in the data industry. Question is will you actually do it. Imagine deregulating yourself for the data industry. Right now, it is a black hole, you don't know what's inside, but it is screaming opportunity from the darkness. TURN ON THE LIGHT and break into the data industry. A future proof opportunity for the next decade and beyond. ► Edureka Big Data Masters Program ( http://bit.ly/2yCbsac ) affiliate link^ ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Computer ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel!
Views: 114765 Ben G Kaiser
Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 149214 SciShow
How to become a Data Analyst in India - Course and career
 
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This video discuss How to become a data analyst in India. For more videos on Jobs &Careers :https://www.youtube.com/channel/UCEFTTJFLp4GipA7BLZNTXvA?view_as=subscriber For aptitude classes :https://www.youtube.com/watch?v=lxm6ez2cx6Y&list=PLjLhUHPsqNYnM1DmZhIbtd9wNhPO1HGPT Every business collects data such as sales figures, market research, logistics, or transportation costs. A data analyst's job is to take that data and analyse it to help companies make better business decisions. Some examples of a data analyst basic job functions include: 1) estimating market shares; 2) establishing a price of new materials for the market; 3) reducing transportation costs; 4) timing of sales and 5) figuring out when to hire or reduce the workforce.Data analysts are responsible for collecting, manipulating, and analyzing data. How To Get There? By obtaining a university degree, learning important analytical skills, and gaining valuable work experience, you can become a successful data analyst. A bachelor's degree is needed for most entry-level jobs, and a master's degree will be needed for many upper-level jobs. To become an initial level data analyst, you’ll have to earn a degree in a subject such as mathematics, statistics, economics, marketing, finance, or computer science. Higher level data analyst jobs may require a master’s or doctoral degree, and they usually guarantee higher pay. Individuals looking for data analyst jobs must be knowledgeable in computer programs such as Microsoft Excel, Microsoft Access, SharePoint, and SQL databases. Data analysts also must have good communication skills, as they must have an open line of communication with the companies with which they work. Lets see some of the Best courses on Analytics offered in India. 1. Advanced Analytics for Management – IIM This program enables practitioners, managers, and decision-makers to use advanced analytics for better decision-making 2. Analytics Essentials – IIIT, Bangalore “Analytics Essentials”is a 3 months week-end program by International Institute of Information Technology Bangalore (IIITB)providing a foundational certification course in Business Analytics 3. Business Analytics and Intelligence (BAI) – IIM Bangalore This course provides in-depth knowledge of handling data and Business Analytics’ tools that can be used for fact-based decision-making. The participants will be able to analyse and solve problems from different industries such as manufacturing, service, retail, software, banking and finance, sports, pharmaceutical, aerospace etc. 4. Certificate Program in Business Analytics – ISB, Hyderabad A combination of classroom and Technology aided learning platform, .Participants will typically be on campus for a 5 day schedule of classroom learning every alternate month for a span of 12 months, which would ideally be planned to include a weekend. 5. Data Analysis Online courses – SRM University SRM University offers part time online courses in data analysis in collaboration with Coursera, edX, Udacity. 6. Executive Program in Business Analytics – IIM Calcutta This executive 1 year long distance program is designed to expose participants to the tools and techniques of analytics. The program covers topics such as Data Mining, Soft Computing, Design of Experiments, Survey Sampling, Statistical Inference, Investment Management, Financial Modelling, Advanced marketing Research etc. 7. Executive Program in Business Analytics and Business Intelligence – IIM Ranchi Course duration is 3 months. Classes will be conducted by eminent professors and industry experts in the weekends in Mumbai/ Kolkata /Delhi /Bengaluru and in addition to these, there will be one-week learning in IIM Ranchi. 8. Jigsaw Academy courses Jigsaw Academy provides some online analytics courses.Their courses include; Foundation Course in Analytics Data Science Certification Human Resources (HR) Analytics Course Big Data Analytics Using Hadoop and R Advanced Certification in Retail Analytics Advanced Course in Financial Analytics Analytics with R Great Lakes PG Course in Business Analytics 9. M. Tech. Computer Science and Engineering with Specialization in Big Data Analytics – VIT VIT offers full time course in Big Data analysis to promote an academic career for further research in theoretical as well as applied aspects of Big Data Analytics 10. M.Tech (Database Systems) – SRM University SRM University offers a two year full time course in database systems where the students are exposed to theoretical concepts complemented by related practical experiments. 11. M.Tech Computer Engineering and Predictive Analytics – Crescent Engineering College Salary The Salary of Data analysts depends on job responsibilities. An entry-level data analyst with basic technical tools might be looking at anything from Rs. 5 lakhs to 12 lakhs per year. A senior data analyst with the skills of a data scientist can command a high price. #dataanalyst #careeroptions #datascience
Introduction to Data Science with R - Data Analysis Part 1
 
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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: 993141 David Langer
Facebook Friend Recommendation using Graph Mining @Applied AI Course/ AI Case Study
 
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for more details please visit this link https://www.appliedaicourse.com/courses/facebook-friend-recommedation
Views: 3133 Applied AI Course
Statistical Aspects of Data Mining (Stats 202) Day 5
 
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Google Tech Talks July 10, 2007 ABSTRACT Lecture 5 This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 23777 GoogleTechTalks
Super Pivots With Data Applied
 
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Data Applied (http://www.data-applied.com) revolutionizes data-driven decision making by integrating rich analytics, data mining, and information visualization capabilities - all using a zero footprint Web interface, collaboration features, and a secure XML Web API. By extracting valuable knowledge from data in domains as varied as Sales, Marketing, Engineering, Social Sciences or Non-Profit, we help organizations make better data-driven decisions and improve efficiency. See how we can help you get more from your data.
Views: 338 Data Applied
Machine Learning & Artificial Intelligence: Crash Course Computer Science #34
 
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So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 428017 CrashCourse
Data Mining PubMed using ai-one's Analyst-Toolbox
 
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Dr. Darius Schneider provides a testimonial on the use of Analyst-Toolbox to shorten literature review times of PubMed by more than 95%. Analyst-Toolbox is a data mining tool that uses ai-one's Nathan technology to find relevant documents in big data sets. It works where keyword searches fail by automatically detecting "what matters most" without any training.
Views: 1217 ai-one
#bbuzz 2015: Andrew Clegg - Signatures, patterns and trends: Timeseries data mining at Etsy
 
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Find more information here: http://berlinbuzzwords.de/session/signatures-patterns-and-trends-timeseries-data-mining-etsy Etsy loves metrics. Everything that happens in our data centres gets recorded, graphed and stored. But with over a million metrics flowing in constantly, it’s hard for any team to keep on top of all that information. Graphing everything doesn’t scale, and traditional alerting methods based on thresholds become very prone to false positives. That’s why we started Kale, an open-source software suite for pattern mining and anomaly detection in operational data streams. These are big topics with decades of research, but many of the methods in the literature are ineffective on terabytes of noisy data with unusual statistical characteristics, and techniques that require extensive manual analysis are unsuitable when your ops teams have service levels to maintain. In this talk I’ll briefly cover the main challenges that traditional statistical methods face in this environment, and introduce some pragmatic alternatives that scale well and are easy to implement (and automate) on Elasticsearch and similar platforms. I’ll talk about the stumbling blocks we encountered with the first release of Kale, and the resulting architectural changes coming in version 2.0. And I’ll go into a little technical detail on the algorithms we use for fingerprinting and searching metrics, and detecting different kinds of unusual activity. These techniques have potential applications in clustering, outlier detection, similarity search and supervised learning, and they are not limited to the data centre but can be applied to any high-volume timeseries data. Kale version 1 is described here: https://codeascraft.com/2013/06/11/introducing-kale/ Version 2 has the same goals but a very different architecture and suite of tools. Come along if you'd like to learn more.
GOTO 2016 • Applied Data Science & Engineering for Local Weather Forecasts • Nikhil Podduturi
 
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This presentation was recorded at GOTO Berlin 2016 http://gotober.com Nikhil Podduturi - Senior Data Engineer/Data Scientist at MeteoGroup ABSTRACT Progress in weather forecasting and in climate modelling over the past 50 years has been dramatic. Due to these dramatic improvements the data being generated increased exponentially. While the first computer ENIAC (Electronic Numerical Integrator and Computer) took 24 hours to make a 24 hour integration weather forecast, right now at MeteoGroup we generate weather forecasts for next 10 days on the fly from the browser. At the core of [...] Download slides and read the full abstract here: https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7999 https://twitter.com/gotober https://www.facebook.com/GOTOConference http://gotocon.com
Views: 1323 GOTO Conferences
Mathematics of Data Science - Data Science is Everywhere
 
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Data science - what's under the hood? This animation, from the SIAM Journal on Mathematics of Data Science, explains that data science really is EVERYWHERE! SIAM Journal on Mathematics of Data Science (SIMODS) publishes work that advances mathematical, statistical, and computational methods in the context of data and information sciences. We invite papers that present significant advances in this context, including applications to science, engineering, business, and medicine. --- FULL MANUSCRIPT: Right now, you’re a few clicks away from streaming a 4K video tour of a far-away city, and exploring a 3D map of it in virtual reality. If you want to actually visit the city, your phone can arrange for a car — maybe even a self-driving car — to pick you up just as you land. While it’s shuttling you around, apps can suggest hotels and sites to visit. We are living in the age of data science. Data science is everywhere, but how does it actually work? When the data analysts, scientists, and engineers who build these applications run up against the limits of what’s currently possible, how do they make the next breakthrough? The Society for Industrial and Applied Mathematics has a new journal for mathematicians, computer scientists, geneticists, neuroscientists, economists and anyone who works with big data: the SIAM Journal on Mathematics of Data Science, known as SIMODS. Through SIMODS, researchers are popping the the hood and tinkering with the engine that makes these applications work, and work better: applied mathematics, and the related domains of computer science, statistics, signal processing, and network science. The compression techniques that allow you to stream a 4K movie are in a constant race with growing file sizes. In the future, techniques like matrix sketching can be used to efficiently discover the underlying low-dimensional manifold and achieve even greater compression rates. This will make your movies stream faster and with better image quality. Deep learning techniques use stochastic optimization for quick and accurate translations. Even more powerful techniques will be necessary to handle the technical language found in specialized categories of speech, like those in law, medicine, and science. What about unsupervised learning, where there are no categories at all? Would you trust your computer to organize the photos from your trip, with no instructions on what folders to make? What about images of brain scans, and your computer could find never-before-seen patterns and correlations that human neuroscientists would never think to look for? Applied math techniques like clustering can make these organizational tasks even better, allowing for applications that seem like science fiction today. Looking forward, imagine machine learning methods that can keep your data completely private, explain their decisions while offering customized suggestions, and be robust to new situations. Can data science move us forward in terms of fairness and diversity? What about using algorithms to achieve long-term goals? Computer scientists and engineers are inventing the future every day, and applied mathematics gives them the tools they need to keep moving forward. SIMODS is looking for interdisciplinary work that pushes the boundaries of data science and takes the field in new directions.
Information Synthesis and Data Mining Part 1
 
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Professor Cathy Blake presents principles and techniques for information synthesis and data mining. The ABC model of synthesis is described and the METIS system serves as a workbench to extract facts that scientists verify while applying the ABC model. Information summarization techniques that augment information synthesis are also discussed.
Views: 6751 UNC-Chapel Hill
Statistics intro: Mean, median, and mode | Data and statistics | 6th grade | Khan Academy
 
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This is a fantastic intro to the basics of statistics. Our focus here is to help you understand the core concepts of arithmetic mean, median, and mode. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/e/calculating-the-mean?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/v/mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/histograms/v/interpreting-histograms?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy‰Ûªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1913663 Khan Academy
Team Sea Farers: Data mining atomically resolved images for material properties
 
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Yawei Hui, Yaohua Liu In this response to the data challenges presented by the Smoky Mountain Computational Science and Engineering Conference, we try to answer the specific questions raised in Challenge #3 which “is driven by efforts in Scanning Transmission Electron Microscopy to expedite materials data analysis, and generate insight into physics and chemistry of 2D materials irradiated by the electron beam.” The STEM data sets provided for this challenge are frames of STEM images which record the intensity map of the hexagonal MoSe2 monolayer with significant defects and dynamic structural re-arrangement. Along the path to find solutions, we considered several algorithms for each question/task. In the following sections, we lay out our best solutions and give a brief discussion at the end about how to improve our result given further research time and resources. All our solutions are implemented in R with certain utility libraries preloaded. Among them there are several critical packages such as “imager”, “dbscan” and “plot3D”, and their functionalities will be explained according to where they are applied.
Views: 50 SMC Data Challenge
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
 
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This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify. Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Views: 8061 Talks at Google
Searching and Mining Open Source Code from the Web
 
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Google Tech Talks June, 4 2008 ABSTRACT Various data mining techniques have been applied to mine source code repositories. However, relying only on one or several local source code repositories may not provide sufficient, relevant data samples (e.g., usage of a certain API call) for mining tasks such as code reuse and defect detection. The recent availability of code search engines allows the mining scope to be scaled to billions of lines of open source code available from the Web, and thus increases the chance of getting sufficient, relevant data samples for mining. This talk will discuss the mining opportunities and challenges based on searching open source code from the Web and present new approaches that mine open source code searched from the Web to assist code reuse and defect detection Speaker: Tao Xie Tao Xie is an Assistant Professor in the Department of Computer Science at North Carolina State University. He received his Ph.D. in Computer Science from the University of Washington in 2005. He leads the Automated Software Engineering Research Group at North Carolina State University. His research centers around two major themes: automated software testing and mining software engineering data. He has served on a number of conference program committees including ISSTA 2008/2009, ASE 2006/2007(Expert-Review Panel)/2008, ICST 2008, AOSD 2007, and ICSM 2007/2008. Besides doing research, he has contributed to understanding the software engineering research community by building community webs such as Software Engineering Academic Genealogy and Software Engineering Conference Map.
Views: 5833 GoogleTechTalks
Team 17 - Data Mining
 
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IS Project Section 1
Views: 8361 nooreen123
What is machine learning and how to learn it ?
 
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http://www.LearnCodeOnline.in Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data mining. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com
Views: 798693 Hitesh Choudhary

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