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Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 69957 edureka!
Data Mining Case Study meetup:  Data Mining Overview
 
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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: 1539 Stoney Vintson
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: 147265 SciShow
Case study method
 
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Subject:Anthropology Paper: Research Methods and Field work
Views: 45397 Vidya-mitra
Text Mining Example Using RapidMiner
 
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Explains how text mining can be performed on a set of unstructured data
Views: 15003 Gautam Shah
R tutorial: What is text mining?
 
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Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 26172 DataCamp
Exploratory Data Analysis
 
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An introduction to exploratory data analysis that includes discussion of descriptive statistics, graphs, outliers, and robust statistics.
Views: 31963 Prof. Patrick Meyer
R tutorial: Getting started with text mining?
 
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Learn more about text mining with R: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Boom, we’re back! You used bag of words text mining to make the frequent words plot. You can tell you used bag of words and not semantic parsing because you didn’t make a plot with only proper nouns. The function didn’t care about word type. In this section we are going to build our first corpus from 1000 tweets mentioning coffee. A corpus is a collection of documents. In this case, you use read.csv to bring in the file and create coffee_tweets from the text column. coffee_tweets isn’t a corpus yet though. You have to specify it as your text source so the tm package can then change its class to corpus. There are many ways to specify the source or sources for your corpora. In this next section, you will build a corpus from both a vector and a data frame because they are both pretty common.
Views: 5219 DataCamp
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: 969681 David Langer
I will do web scraping and data mining for lead generation
 
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Hi Welcome to my Gig. Here I'm expert in Data Mining, web Scraping, web crawling, Email extraction, Data Entry, Data Conversion and so on. I have lots of experience in this field. So just send your requirements to me before place the order. Here is my working area for this gig: Website scraping Data Mining Yellowpages scraping Business Lead generation Social Media Scrape Email list extraction Big database scraping/collection Use proxies for scraping Download images & content Extract data from PDF Data Entry, Copy Paste, CSV, PNG, PDF, EXCEL, OCR file conversion Please knock before place the order so that we can mutually accept the cost and delivery schedule of the project. Thanks place Order Here:https://www.fiverr.com/foysal123/do-web-scraping-and-data-mining-for-lead-generation
Views: 135 Foysal Rahaman
Naive Bayes Classifier Algorithm Example Data Mining | Bayesian Classification | Machine Learning
 
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naive Bayes classifiers in data mining or machine learning are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s,and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis. for more refer to https://en.wikipedia.org/wiki/Naive_Bayes_classifier naive bayes classifier example for play-tennis Download PDF of the sum on below link https://britsol.blogspot.in/2017/11/naive-bayes-classifier-example-pdf.html *****************************************************NOTE********************************************************************************* The steps explained in this video is correct but please don't refer the given sum from the book mentioned in this video coz the solution for this problem might be wrong due to printing mistake. **************************************************************************************************************************************** All data mining algorithm videos Data mining algorithms Playlist: http://www.youtube.com/playlist?list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr ******************************************************************** book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar *********************************************
Views: 41816 fun 2 code
Predicting the Winning Team with Machine Learning
 
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Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams Please Subscribe! And like. And comment. More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ 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: 93706 Siraj Raval
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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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: 164805 Timothy DAuria
How Big Data Is Used In Amazon Recommendation Systems | Big Data Application & Example | Simplilearn
 
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This Big Data Video will help you understand how Amazon is using Big Data is ued in their recommendation syatems. You will understand the importance of Big Data using case study. Recommendation systems have impacted or even redefined our lives in many ways. One example of this impact is how our online shopping experience is being redefined. As we browse through products, the Recommendation system offer recommendations of products we might be interested in. Regardless of the perspectives, business or consumer, Recommendation systems have been immensely beneficial. And big data is the driving force behind Recommendation systems. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 29777 Simplilearn
The Best Way to Prepare a Dataset Easily
 
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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/ 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: 176058 Siraj Raval
Predictive Maintenance with MATLAB A Prognostics Case Study
 
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See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r Companies that make industrial equipment are storing large amounts of machine data, with the notion that they will be able to extract value from it in the future. However, using this data to build accurate and robust models that can be used for prediction requires a rare combination of equipment expertise and statistical know-how. In this webinar we will use machine learning techniques in MATLAB to estimate remaining useful life of equipment. Using data from a real world example, we will explore importing, pre-processing, and labeling data, as well as selecting features, and training and comparing multiple machine learning models. We will show how MATLAB is used to build prognostics algorithms and take them into production, enabling companies to improve the reliability of their equipment and build new predictive maintenance services.
Views: 14168 MATLAB
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 134553 nptelhrd
Sampling & its 8 Types: Research Methodology
 
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Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling Research Methodology Population & Sample Systematic Sampling Cluster Sampling Non Probability Sampling Convenience Sampling Purposeful Sampling Extreme, Typical, Critical, or Deviant Case: Rare Intensity: Depicts interest strongly Maximum Variation: range of nationality, profession Homogeneous: similar sampling groups Stratified Purposeful: Across subcategories Mixed: Multistage which combines different sampling Sampling Politically Important Cases Purposeful Sampling Purposeful Random: If sample is larger than what can be handled & help to reduce sample size Opportunistic Sampling: Take advantage of new opportunity Confirming (support) and Disconfirming (against) Cases Theory Based or Operational Construct: interaction b/w human & environment Criterion: All above 6 feet tall Purposive: subset of large population – high level business Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow Advantages of Sampling Increases validity of research Ability to generalize results to larger population Cuts the cost of data collection Allows speedy work with less effort Better organization Greater brevity Allows comprehensive and accurate data collection Reduces non sampling error. Sampling error is however added. Population & Sample @2:25 Sampling @6:30 Systematic Sampling @9:25 Cluster Sampling @ 11:22 Non Probability Sampling @13:10 Convenience Sampling @15:02 Purposeful Sampling @16:16 Advantages of Sampling @22:34 #Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm types of sampling types of sampling pdf probability sampling types of sampling in hindi random sampling cluster sampling non probability sampling systematic sampling
Views: 351360 Examrace
Data Analysis with Python : Exercise – Titanic Survivor Analysis | packtpub.com
 
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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/2qyTs1d]. This video introduces the Titanic disaster data set and discusses some exploratory analysis on the data. The aim of this video is to recap what you learned so far on a real data set, as well as show-case some data visualization examples. • Download the data set and understand the data structure • Extract some summary statistics from the data set • Visualize the data and find correlations between variables For the latest Application development video tutorials, please visit http://bit.ly/1VACBzh Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 26568 Packt Video
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 457011 Brandon Weinberg
Quartile - Decile - Percentile
 
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partitional values... quartile, decile and percentile... follow me on instagram : Yasser.98555159292
Views: 267982 Yasser Khan
SPSS for Beginners 1: Introduction
 
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Updated video 2018: SPSS for Beginners - Introduction https://youtu.be/_zFBUfZEBWQ This video provides an introduction to SPSS/PASW. It shows how to navigate between Data View and Variable View, and shows how to modify properties of variables.
Views: 1500444 Research By Design
Introduction To Cluster Analysis
 
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This is short tutorial for What it is? (What do we mean by a cluster?) How it is different from decision tree? What is distance and linkage function? What is hierarchical clustering? What is scree plot & dendogram? What is non hierarchical clustering (k-means)? How to learn it in detail (step by step)? --------------------------------- Read in great detail along with Excel output, computation and SAS code ---------------------------------- https://www.udemy.com/cluster-analysis-motivation-theory-practical-application/?couponCode=FB_CA_001
Views: 134543 Gopal Malakar
Scales of Measurement - Nominal, Ordinal, Interval, Ratio (Part 1) - Introductory Statistics
 
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This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 357015 Quantitative Specialists
Data Analysis with Python for Excel Users
 
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A common task for scientists and engineers is to analyze data from an external source. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. See http://apmonitor.com/che263/index.php/Main/PythonDataAnalysis
Views: 173520 APMonitor.com
Data Mining, Web Research, Web Scraping
 
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-- Created using PowToon -- Free sign up at http://www.powtoon.com/youtube/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 102 Sumon Ali
Case Study: eCommerce | PromptCloud's Web Crawling Solution
 
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Visit us - https://www.promptcloud.com Discover how PromptCloud's customized Web Scraping & Data Extraction solution can help firms leverage their ECommerce business. Meet George. George leads product intelligence at a rapidly growing eCommerce firm. He had been looking high and low for acquiring data from retail brands. His concerns were, Aggregating a catalog of 1 Million products daily from more than 100 sites Structuring data uniformly And sustaining the quality of data during this process He tried accessing brands’ feeds but data was limited and not all of them had APIs. He then setup a team that could write crawlers; but ended with high costs and low returns, coupled with frustration. That’s when a great idea struck him on outsourcing crawls. George reached out to PromptCloud; a web crawling solution that sets up custom crawlers for enterprises. PromptCloud also helped George maintain these crawlers so that products were continuously extracted free of errors. George’s only task now was to download datasets that PromptCloud was providing daily, into his database. Now- George focuses all his energy on using fresh data he receives He’s agnostic to any crawling issues He is on track to meeting deadlines for his new product launch If you are like George; trying to make ends meet with fresh and valuable data; reach out to our super-friendly sales team at [email protected]
Views: 781 PromptCloud
Developing an effective Job Safety Analysis: Hazard identification
 
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Watch our video on 'Developing an effective Job Safety Analysis'. For more information, read Guidance Note QGN 17 'Development of effective Job Safety Analysis': https://www.business.qld.gov.au/industry/mining/safety-health/mining-safety-health/legislation-standards-guidelines/recognised-standards-guidelines-guidance-notes Direct link: QGN17: Development of effective job safety analysis (PDF, 1.1MB) https://www.dnrm.qld.gov.au/__data/assets/pdf_file/0005/240359/qld-guidance-note-17.pdf Connect with us: https://www.facebook.com/MiningQld http://twitter.com/MiningQLD http://twitter.com/MiningAlertsQLD https://www.linkedin.com/company/department-of-natural-resources-and-mines
Views: 1518 MiningQld
ISM6136 - Chandley - Final Presentation
 
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Data Mining Class Final Project for A. Chandley using Portugal Bank Marketing Dataset [Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS. Available at: [pdf] http://hdl.handle.net/1822/14838 [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt
Views: 340 Adam Chandley
Developing an effective Job Safety Analysis: Case Study
 
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Watch our video on 'Developing an effective Job Safety Analysis'. For more information, read Guidance Note QGN 17 'Development of effective Job Safety Analysis': https://www.business.qld.gov.au/industry/mining/safety-health/mining-safety-health/legislation-standards-guidelines/recognised-standards-guidelines-guidance-notes Direct link: QGN17: Development of effective job safety analysis (PDF, 1.1MB) https://www.dnrm.qld.gov.au/__data/assets/pdf_file/0005/240359/qld-guidance-note-17.pdf Connect with us: https://www.facebook.com/MiningQld http://twitter.com/MiningQLD http://twitter.com/MiningAlertsQLD https://www.linkedin.com/company/department-of-natural-resources-and-mines
Views: 1621 MiningQld
Text and Data Mining – Christophe Geiger, Giancarlo Frosio et Oleksandr Bulayenko –22.2.2018
 
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Source: © European Union, 2018 – European Parliament Presentation of the study on text and data mining at the Committee on Legal Affairs of the European Parliament in Brussels 22 February 2018. To download the study: http://www.europarl.europa.eu/RegData/etudes/IDAN/2018/604941/IPOL_IDA(2018)604941_EN.pdf or https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3160586 Disclaimer: The interpretation does not constitute an authentic record of proceedings. The simultaneous interpretation of debates provided by the European Parliament serves only to facilitate communication amongst the participants in the meeting. It does not constitute an authentic record of proceedings. Only the original speech or the revised written translation of that speech is authentic. Where there is any difference between the simultaneous interpretation and the original speech (or the revised written translation of the speech), the original speech (or the revised written translation) takes precedence.
Views: 101 CEIPI
decision tree example(ID3)
 
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Download this sum PDF from link below http://britsol.blogspot.in/2017/10/decision-tree-algorithm-pdf.html?m=1 book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
Views: 58294 fun 2 code
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: 1198 PyData
web Scraping data mining crawling collection import io extraction
 
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https://www.fiverr.com/jopakhanam/do-web-scraping-data-mining-import-io-as-virtual-assitant?arrived_from_manage_gigs=true&display_share=true
Views: 36 Salima Khanam
Introduction to ACL Analytics - Module 1 (What is ACL?)
 
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Data - http://bit.ly/ACLDataNew This is the 1st video of a series of 8 videos on how to use ACL Analytics. Stay tuned and don't forget to check out the rest of the channel.
Views: 63857 SAF Business Analytics
Introduction to Text Analysis with NVivo 11 for Windows
 
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It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share. http://www.qsrinternational.com
Views: 135657 NVivo by QSR
Best Course In Data Mining
 
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Description
Views: 34 Mira G
Digital Signatures for the Cloud:  A B2C Case Study
 
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If you are a solution architect, or a business strategist new to digital signatures, this webinar will give you an overview of the components needed to implement a digital signature solution, including building PDF document workflows, digital signing certificates and available architectures.
Views: 539 iText
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough
 
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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: 4729 MapR Technologies
Developing an effective Job Safety Analysis: JSA preperation
 
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Watch our video on 'Developing an effective Job Safety Analysis'. For more information, read Guidance Note QGN 17 'Development of effective Job Safety Analysis': https://www.business.qld.gov.au/industry/mining/safety-health/mining-safety-health/legislation-standards-guidelines/recognised-standards-guidelines-guidance-notes Direct link: QGN17: Development of effective job safety analysis (PDF, 1.1MB) https://www.dnrm.qld.gov.au/__data/assets/pdf_file/0005/240359/qld-guidance-note-17.pdf Connect with us: https://www.facebook.com/MiningQld http://twitter.com/MiningQLD http://twitter.com/MiningAlertsQLD https://www.linkedin.com/company/department-of-natural-resources-and-mines
Views: 767 MiningQld
Presentation Data Mining & Decision-making: Case of Amazon.com
 
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Week 2 assignment for MooreFMIS7003 course at NCU. Prepared by FahmeenaOdetta Moore.
Introduction to Data Mining: Similarity & Dissimilarity
 
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In this Data Mining Fundamentals tutorial, we introduce you to similarity and dissimilarity. Similarity is a numerical measure of how alike two data objects are, and dissimilarity is a numerical measure of how different two data objects are. We also discuss similarity and dissimilarity for single attributes. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8Lsn0 See what our past attendees are saying here: https://hubs.ly/H0f8Lsp0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 17920 Data Science Dojo
K Nearest Neighbor Algorithm (KNN) | Data Science | Big Data
 
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In this video you will learn about the KNN (K Nearest Neighbor Algorithm). KNN is a machine learning / data mining algorithm that is used for regression and classification purpose. This is a non parametric class of algorithms that works well with all kinds of data. The other types of data science algorithms that works similar to KNN are the Support vector machine, Logistic regression, Random forest, decision tree, Neural Network etc. ANalytics Study Pack : https://analyticuniversity.com Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 6229 Big Edu
Big Data Analytics Projects: From Business Idea to Successful Delivery
 
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SoftServe`s Webinar on Big Data Analytics Projects was held on July 10, 2014. SoftServe's Big Data experts Olha Hrytsay and Serhiy Haziyev discussed the skills and experience in cutting-edge technologies required to properly address today's Big Data challenges, as well as case studies of real-life projects successfully implemented for various companies including Fortune 100's and start-ups.
Views: 3623 SoftServe
Seneca Resources - Drone Mapping Energy Case Study
 
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To learn more about Drone Accuracy vs Standard Aerial in Seneca Resources Comparison (PDF) visit http://www.identifiedtech.com/learn-mapping-drone-case-studies-and-aerial-survey-drone-white-papers/drone-surveying-accuracy
Correlation Coefficient
 
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Hi everyone. This video will show you how to calculate the correlation coefficient with a formula step-by-step. Please subscribe to the channel: https://www.youtube.com/frankromerophoto?sub_confirmation=1+%E2%80%9Cauto+subscribe%E2%80%9D https://goo.gl/aWzM8C
Views: 523884 cylurian