{
    "links":{},
    "photo":"https://d15cw65ipctsrr.cloudfront.net/95/d286100d5911e48c8c3d24a9aa8265/coursera_logo.jpg",
    "courseFormat":"The school will have 9 \"chapters\", each corresponding to 1 day of the intensive, interactive school offered by Caltech and JPL in Sept. 2014.&nbsp; Each of those will consist of a set of video lectures (adding up to ~ 1.5-2 hours total), slides, additional links, and hands-on exercises.<br><br>The students are welcome to interact and exchange information on the discussion threads, but there is no commitment by the organizers or the instructors to participate in these on-line discussions, or to answer any questions.<br>",
    "smallIcon":"https://d15cw65ipctsrr.cloudfront.net/97/0190300d5911e4b7cf853662d3127d/coursera_logo.jpg",
    "universityLogo":"",
    "video":"",
    "smallIconHover":"https://d15cw65ipctsrr.cloudfront.net/97/0190300d5911e4b7cf853662d3127d/coursera_logo.jpg",
    "shortDescription":"This is an intensive, advanced summer school (in the sense used by scientists) in some of the methods of computational, data-intensive science.  It covers a variety of topics from applied computer science and engineering, and statistics, and it requires a strong background in computing, statistics, and data-intensive research.\n",
    "id":1973,
    "estimatedClassWorkload":"20-25 hours/week",
    "universityLogoSt":"",
    "targetAudience":2,
    "courseSyllabus":"<p>The anticipated schedule of lectures (subject to changes):<br></p><p>Each bullet bellow corresponds to a set of materials that includes approximately 2 hours of video lectures, various links and supplementary materials, plus some on-line, hands-on exercises.<br></p>1. Introduction to the school.&nbsp; Software architectures.&nbsp; Introduction to Machine Learning.<br><br><div><div><div>2. Best programming practices.&nbsp; Information retrieval.<br><br>3. Introduction to R.&nbsp; Markov Chain Monte Carlo.<br><br>4. Statistical resampling and inference.<br><br>5. Databases.<br><br>6. Data visualization.<br><br>7. Clustering and classification.<br><br>8. Decision trees and random forests.<br><br>9. Dimensionality reduction.&nbsp; Closing remarks.<br></div></div></div>",
    "aboutTheCourse":"This is not a class as it is commonly understood; it is the set of materials from a summer school offered by Caltech and JPL, in the sense used by most scientists: an intensive period of learning of some advanced topics, not on an introductory level.&nbsp;<br><br>The school will cover a variety of topics, with a focus on practical \ncomputing applications in research: the skills needed for a \ncomputational (\"big data\") science, not computer science. &nbsp;The specific \nfocus will be on applications in astrophysics, earth science (e.g., \nclimate science) and other areas of space science, but with an emphasis \non the general tools, methods, and skills that would apply across other \ndomains as well.&nbsp; It is aimed at an audience of practicing researchers who already have a strong background in computation and data analysis.&nbsp; The lecturers include computational science and \ntechnology experts from Caltech and JPL.<br><br>Students can evaluate their own progress, but there will be no tests, exams, and no formal credit or certificates will be offered.<br>",
    "largeIcon":"https://d15cw65ipctsrr.cloudfront.net/b7/47a830352b11e4a4351b48ac74e750/coursera_logo.png",
    "suggestedReadings":"Will be provided along with the other learning materials.<br>",
    "videoId":"",
    "faq":"<ul><li><strong></strong>\n<strong>What resources will I need for this class?</strong></li></ul>A decent network connection and a computer that you would use for your scientific computing or data analysis.<br><br><ul><li><strong>What is the coolest thing I'll learn if I take this class?</strong></li></ul>How to handle big data and extract knowledge from them.<br><br><ul><li><strong>What background is expected for learners in this class?</strong>\n</li></ul>See above.&nbsp; By the way, it is not a class; it is an advanced summer school.<br><br><ul><li><strong>I don't have a strong background in computing and data analysis, or I am just curious abut these subjects.&nbsp; Should I take this class?</strong></li></ul>Probably not.&nbsp; You will probably get frustrated and not get much from it.&nbsp; Better start with some introductory level classes in these subjects.<br><br><ul><li><strong>How much work is needed?</strong>\n</li></ul>At least 4-5 hours per chapter (and there are 9 of them), if you already have a strong background in scientific data analysis and computation; and more if you don't.<br><br><ul><li><strong>Why are there no quizzes or exams or certificates?</strong>\n</li></ul>Because it is not a class.&nbsp; We are simply sharing with you the materials from our advanced summer school.&nbsp; You can benefit from the learning, and you are the best judge of how well you have understood the material.&nbsp; You may want to wait until the spring of 2015 and take a full class based on these materials.<br><br><br>",
    "shortName":"bigdataschool",
    "name":"The Caltech-JPL Summer School on Big Data Analytics",
    "subtitleLanguagesCsv":"",
    "recommendedBackground":"The students should have a solid background in scientific computing and data analysis.&nbsp; The target audience includes upper-level undergraduate and graduate students, postdocs, or other researchers in science and technology fields.&nbsp; Good programming skills in at least one modern computer language (or the ability to quickly learn one) are needed, as well as some knowledge of statistics, and some experience with scientific data analysis.&nbsp; Background knowledge in computer science is a plus.<br>"
}