{
    "links":{},
    "photo":"https://coursera-course-photos.s3.amazonaws.com/3f/9c77bd7de73c7696f32dd9ee792802/mmds_logo2.png",
    "courseFormat":"There will be about 2 hours of video to watch each week, broken into small segments.  There will be automated homeworks to do for each week, and a final exam.",
    "smallIcon":"https://d15cw65ipctsrr.cloudfront.net/a7/14a8efc7955918d44f7df1689c2d67/mmds_logo2.png",
    "universityLogo":"",
    "video":"501a80e0393d11e4a552ef40cd141f44",
    "smallIconHover":"https://d15cw65ipctsrr.cloudfront.net/a7/14a8efc7955918d44f7df1689c2d67/mmds_logo2.png",
    "shortDescription":"This class teaches algorithms for extracting models and other information from very large amounts of data.  The emphasis is on techniques that are efficient and that scale well.",
    "id":896,
    "estimatedClassWorkload":"8-10 hours/week",
    "universityLogoSt":"",
    "targetAudience":1,
    "courseSyllabus":"<p>Week 1:<br>\nMapReduce<br>\nLink Analysis -- PageRank</p>\n<p>Week 2:<br>\nLocality-Sensitive Hashing -- Basics + Applications<br>\nDistance Measures<br>\nNearest Neighbors<br>\nFrequent Itemsets</p>\n<p>Week 3:<br>\nData Stream Mining<br>\nAnalysis of Large Graphs</p>\n<p>Week 4:<br>\nRecommender Systems<br>\nDimensionality Reduction</p><p></p>\n<p>Week 5:<br>\nClustering<br>\nComputational Advertising</p>\n<p>Week 6:<br>\nSupport-Vector Machines<br>\nDecision Trees<br>\nMapReduce Algorithms</p>\n<p>Week 7:<br>\nMore About Link Analysis -- &nbsp;Topic-specific PageRank, Link Spam.<br>\nMore About Locality-Sensitive Hashing</p>",
    "aboutTheCourse":"We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. &nbsp;The rest of the course is devoted to algorithms for extracting models and information from large datasets. &nbsp;Participants will learn how Google's PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes. &nbsp;We'll cover locality-sensitive hashing, a bit of magic that allows you to find similar items in a set of items so large you cannot possibly compare each pair. &nbsp;When data is stored as a very large, sparse matrix, dimensionality reduction is often a good way to model the data, but standard approaches do not scale well; we'll talk about efficient approaches. &nbsp;Many other large-scale algorithms are covered as well, as outlined in the course syllabus.",
    "largeIcon":"https://d15cw65ipctsrr.cloudfront.net/32/e0a840352a11e4ae1bdd88b9e8f59b/mmds_logo2.png",
    "suggestedReadings":"There is a free book \"Mining of Massive Datasets, by Leskovec, Rajaraman, and Ullman (who by coincidence are the instructors for this course :-). &nbsp;You can download it at <a href=\"http://www.mmds.org\" target=\"_blank\">http://www.mmds.org/</a> &nbsp;Hardcopies can be purchased from Cambridge Univ. Press.",
    "videoId":"501a80e0393d11e4a552ef40cd141f44",
    "faq":"<ul><li><strong>Will I get a Statement of Accomplishment after completing this class?</strong><p>Yes. Participants who successfully complete the class will receive a Statement of Accomplishment signed by the instructors. &nbsp;A level designated \"distinction\" will also be offered.</p></li></ul>",
    "shortName":"mmds",
    "name":"Mining Massive Datasets",
    "subtitleLanguagesCsv":"",
    "recommendedBackground":"A course in database systems &nbsp;is recommended, as is a basic course on algorithms and data structures. &nbsp;You should also understand mathematics up to multivariable calculus and linear algebra.<br>",
    "aboutTheInstructor":""
}