[LINK] New MIT Big Data Course

stephen at melbpc.org.au stephen at melbpc.org.au
Sat Jan 11 13:30:32 AEDT 2014


Excellent posts Roger, and Bernard.

You both seem absolutely right. This new MIT big data course certainly
appears in-depth regards how to do big data, but light on why and when.

Any bigger-picture social factors (compared to business-as-usual) seems
to be missing. This course could make excellent NSA plumbers but social
responsibility awareness for and with collective big data seems missing.  

Much Internet big data traverses through the U.S. and as such, plumbers
of it have an international and social duty of care. The U.S often does
ignore this. Their big-gun attitude re international matters is a worry.

Big data's an international yet personal matter. Big data is you and me.
It's not just data. And it would certainly appear that course graduates
will exit with little such awareness, or sense of social responsibility.

Cheers,
Stephen


> At 4:19 +0000 10/1/14, stephen at melbpc.org.au wrote:
> >MIT Professional Education
> >"TACKLING THE CHALLENGES OF BIG DATA"
> >Dates: Course Runs Online, March 4 - April 1, 2014 | Fee: USD$495
> ><http://web.mit.edu/professional/onlinex-
programs/courses/tackling_the_cha> llenges_of_big_data.html>
> 
> Impressive as MIT is technically, the capacity of CSAIL people to
> 
> recognise the bigger picture seems to be seriously lacking.
> 
> ('Oh, *th-a-a-a-at* Code of Ethics.  Ah, but it's okay,
> because I didn't renew my membership last year').
> 
> There's one hint that maybe, just maybe, there could be some ethical,
> 
> some legal, and some business problems, embedded in all this.  That's
> 
> this bit:
> >Protecting confidential data in a large database using encryption
> >Techniques for executing database queries over encrypted data
> 
> >without decryption
> 
> That's good, but it addresses just one small threat among many, viz.
> 
> access by a third party that hasn't paid to join the club.
> 
> There's nil sense of such aspects as:
> -   the data's original purposes
> -   semantic inconsistencies
> -   data quality factors (datedness, completeness, etc.)
> -   expropriation and consent
> -   copyright
> -   deidentification
> -   reidentifiability
> -   constructive data falsification in order to deny administrative value
>      while sustaining statistical value
> -   checking of inferences drawn against the real world
> -   transborder issues
> -   extra-territorial reach of law (which the US asserts in spades,
>      but the EU in particular is slowly learning to claim as well).
> 
> If those kinds of issues aren't addressed, then CSAIL is just
> 
> teaching people to be (and are just acting like) glorified plumbers.
> 
> Even in their own terms, the problem statement is wrong, and hence so
> 
> is the solution spec, and so is the answer that tumbles out of the
> 
> exciting algorithm.
> 
> Check out:
> 
> Wigan M.R. & Clarke R. (2013)  'Big Data's Big Unintended Consequences'
> IEEE Computer 46, 6 (June 2013) 46 - 53, PrePrint at
> 
> http://www.rogerclarke.com/DV/BigData-1303.html
> 
> _____________________________________________________________________
> 
> 
> >COURSE DESCRIPTION
> >
> >This new Online X course will survey state-of-the-art topics in Big 
Data,
> >looking at data collection (smartphones, sensors, the Web), data storage
> >and processing (scalable relational databases, Hadoop, Spark, etc.),
> >extracting structured data from unstructured data, systems issues
> >(exploiting multicore, security), analytics (machine learning, data
> >compression, efficient algorithms), visualization, and a range of
> >applications.
> >
> >Each module will introduce broad concepts as well as provide the most
> >recent developments in research.
> >
> >The course will be taught by a team of world experts from MIT and the 
MIT
> >Computer Science and Artificial Intelligence Laboratory (CSAIL) in each 
of
> >these areas.
> >
> >Registration Deadlines:
> >
> >It is highly recommended that you register as soon as possible.
> >
> >Registration will be accepted up until February 28, but registrants will
> >not be given access to the course site or materials until payment is
> >received.
> >
> >Course Flyer (pdf):
> >
> ><http://web.mit.edu/professional/pdf/oxp-docs/BigDataCourseFlyer.pdf>
> >
> >
> >COURSE OVERVIEW:
> >
> >The course is held over four weeks and will provide the following:
> >
> >Online accessibility 24/7 ñ self-paced
> >Five modules covering 18 topic areas: with 20 hours of video
> >Five assessments to reinforce key learning concepts of each module
> >Case studies
> >Discussion Forums for participants to discuss thought provoking 
questions
> >in medicine, social media, finance, and transportation posed by the MIT
> >faculty teaching the course; share, engage, and ideate with other
> >participants
> >Community Wiki for sharing additional resources, suggested readings, and
> >related links
> >
> >Participants will also take away:
> >
> >Course materials from all presentations
> >
> >30 day access to the archived course (includes videos, discussion 
boards,
> >content, and Wiki)
> >
> >KEY BENEFITS
> >
> >Position yourself in your organization as a vital subject matter expert
> >regarding major technologies and applications in your industry that are
> >driving the Big Data revolution and position your company to propel 
forward
> >and stay competitive
> >
> >Engage confidently with management on opportunities and Big Data 
challenges
> >faced by your industry; analyze emerging technologies and how those
> >technologies can be applied effectively to address real business 
problems
> >while unlocking the value of data and its potential use for company 
growth
> >
> >Learn and assess the issues of scalability ñ make your work more
product> ive
> >
> >Gain valuable insights and access to CSAIL research that will 
differentiate
> >how you and your company breakdown Big Data to save time and money while
> >making work more efficient
> >
> >Convenient, flexible schedule with access 24 hours a day, from anywhere 
in
> >the world, no travel time, inexpensive, taught by world-renowned MIT
> >faculty
> >
> >MIT PROFESSIONAL EDUCATION ALUMNI BENEFITS
> >
> >After completing Tackling the Challenges of Big Data, participants will
> >become alumni of MIT Professional Education and will receive all the
> >associated benefits and courtesies listed below.
> >
> >Receive exclusive discounts on all future Short Programs and Online X
> >Programs courses
> >Access will be provided to our restricted MIT Professional Education 
alumni
> >group on LinkedIn; this includes invites to join all MIT Professional
> >Education social media platforms
> >Networking opportunities with other individuals from around the globe
> >working in a variety of industries interested in technology, computer
> >science, entrepreneurship, science, research, and Big Data, among many
> >others
> >Email distribution of our MIT Professional Education newsletter
> >Finally, participants will join the MIT Professional Education alumni
> >mailing list where they will receive advanced notice regarding special
> >announcements on upcoming courses, programs, and events
> >
> >
> >EARN A CERTIFICATE OF COMPLETION
> >
> >Upon successful completion of the course a Certificate of Completion 
will
> >be awarded by MIT Professional Education.
> >
> >To earn a Certificate of Completion in this course, participants should
> >watch all the videos, actively participate in the discussion boards, and
> >complete all assessments by April 01, 2014, with an average of 80 
percent
> >success rate.
> >
> >The Certificate of Completion will be awarded on April 02, 2014, by MIT
> >Professional Education.
> >
> >Grading: Grades are not awarded for this course.
> >
> >
> >WHO SHOULD PARTICIPATE
> >
> >Prerequisite(s): This course is designed to be suitable for anyone with 
a
> >bachelorís level education in computer science.
> >
> >Tackling the Challenges of Big Data is designed to be valuable to both
> >individuals and companies because it provides a platform for discussion
> >from numerous technical perspectives. The concepts delivered through 
this
> >course can spark idea generation among team members and the knowledge
> >gained can be applied to their companyís approach to Big Data problems
a> nd
> >shape the way business operate today.
> >
> >The application of the course is broad and can apply to both early 
career
> >professionals as well as senior technical managers.
> >
> >Participants will benefit the most from the concepts taught in this 
course
> >if they have at least three years of work experience.
> >
> >Participants may include:
> >
> >Engineers who need to understand the new Big Data technologies and 
concepts
> >to apply in their work
> >Technical managers who want to familiarize themselves with these 
emerging
> >technologies
> >Entrepreneurs who would like to gain perspective on trends and future
> >capabilities of Big Data technology
> >
> >LEARNING OBJECTIVES
> >
> >Participants will learn the state-of-the-art in Big Data. The course 
aims
> >to reduce the time from research to industry dissemination and expose
> >participants to some of the most recent ideas and techniques in Big 
Data.
> >
> >After taking this course, participants will:
> >
> >Distinguish what is Big Data (volume, velocity, variety), and will learn
> >where it comes from, and what are the key challenges
> >Determine how and where Big Data challenges arise in a number of 
domains,
> >including social media, transportation, finance, and medicine
> >Investigate multicore challenges and how to engineer around them
> >Explore the relational model, SQL, and capabilities of new relational
> >systems in terms of scalability and performance
> >Understand the capabilities of NoSQL systems, their capabilities and
> >pitfalls, and how the NewSQL movement addresses these issues
> >Learn how to maximize the MapReduce programming model: What are its
> >benefits, how it compares to relational systems, and new developments 
that
> >improve its performance and robustness
> >Learn why building secure Big Data systems is so hard and survey recent
> >techniques that help; including learning direct processing on encrypted
> >data, information flow control, auditing, and replay
> >Discover user interfaces for Big Data and what makes building them
> >difficult
> >Measure the need for and understand how to create sublinear time 
algorithms
> >Manage the development of data compression algorithms
> >Formulate the ìdata integration problemî: semantic and schematic
> >heterogeneity and discuss recent breakthroughs in solving this problem
> >Understand the benefits and challenges of open-linked data
> >Comprehend machine learning and algorithms for data analytics
> >
> >
> >COURSE OUTLINE
> >
> >Modules, Topics, and Faculty
> >
> >
> >MODULE ONE: INTRODUCTION AND USE CASES
> >
> >The introductory module aims to give a broad survey of Big Data 
challenges
> >and opportunities and highlights applications as case studies.
> >
> >Introduction: Big Data Challenges (Sam Madden)
> >
> >Identify and understand the application of existing tools and new
> >technologies needed to solve next generation data challenges
> >Challenges posed by the ability to scale and the constraints of today's
> >computing platforms and algorithms
> >Addressing the universal issue of Big Data and how to use the data to 
align
> >with a companyís mission and goals
> >Case Study: Transportation (Daniela Rus)
> >
> >Data driven models for transportation
> >Coresets for Global Positioning System (GPS) data streams
> >Congestion aware planning
> >Case Study: Visualizing Twitter (Sam Madden)
> >
> >Understand the power of geocoded Twitter data
> >Learn how Graphic Processing Units (GPUs) can be used for extremely high
> >throughput data processing
> >Utilize MapD, a new GPU based database system for visualizing Twitter in
> >action
> >
> >MODULE TWO: BIG DATA COLLECTION
> >
> >The data capture module surveys approaches to data collection, cleaning,
> >and integration.
> >
> >Data Cleaning and Integration (Michael Stonebraker)
> >
> >Available tools and protocols for performing data integration
> >Curation issues (cleaning, transforming, and consolidating data)
> >Hosted Data Platforms and the Cloud (Matei Zaharia)
> >
> >How performance, scalability, and cost models are impacted by hosted 
data
> >platforms in the cloud
> >Internal and external platforms to store data
> >
> >MODULE THREE: BIG DATA STORAGE
> >The module on Big Data storage describes modern approaches to databases 
and
> >computing platforms.
> >
> >Modern Databases (Michael Stonebraker)
> >
> >Survey data management solutions in todayís market place, including
> >traditional RDBMS, NoSQL, NewSQL, and Hadoop
> >Strategic aspects of database management
> >Distributed Computing Platforms (Matei Zaharia)
> >
> >Parallel computing systems that enable distributed data processing on
> >clusters, including MapReduce, Dryad, Spark
> >Programming models for batch, interactive, and streaming applications
> >Tradeoffs between programming models
> >NoSQL, NewSQL (Sam Madden)
> >
> >Survey of new emerging database and storage systems for Big Data
> >Tradeoffs between reduced consistency, performance, and availability
> >Understanding how to rethink the design of database systems can lead to
> >order of magnitude performance improvements
> >
> >MODULE FOUR: BIG DATA SYSTEMS
> >
> >The systems module discusses solutions to creating and deploying working
> >Big Data systems and applications.
> >
> >Multicore Scalability (Nickolai Zeldovich)
> >
> >Understanding what affects the scalability of concurrent programs on
> >multicore systems
> >Lock-free synchronization for data structures in cache-coherent shared
> >memory
> >Security (Nickolai Zeldovich)
> >
> >Protecting confidential data in a large database using encryption
> >Techniques for executing database queries over encrypted data without
> >decryption
> >User Interfaces for Data (David Karger)
> >
> >Principles of and tools for data visualization and exploratory data
> >analysis
> >Research in data-oriented user interfaces
> >
> >MODULE FIVE: BIG DATA ANALYTICS
> >
> >The analytics module covers state-of-the-art algorithms for very large 
data
> >sets and streaming computation.
> >
> >Machine Learning Tools (Tommi Jaakkola)
> >
> >Computational capabilities of the latest advances in machine learning
> >Advanced machine learning algorithms and techniques for application to
> >large data sets
> >Fast Algorithms I (Ronitt Rubinfeld)
> >
> >Efficiency in data analysis
> >Fast Algorithms II (Piotr Indyk)
> >
> >Advanced applications of efficient algorithms
> >Scale-up properties
> >Data Compression (Daniela Rus)
> >
> >Reducing the size of the Big Data file and its impact on storage and
> >transmission capacity
> >Design of data compression schemes such as coresets to apply to Big Data
> >Case Study: Information Summarization (Regina Barzilay)
> >
> >Applications: Medicine (John Guttag)
> >
> >Utilize data to improve operational efficiency and reduce costs
> >Analytics and tools to improve patient care and control risks
> >Using Big Data to improve hospital performance and equipment management
> >Applications: Finance (Andrew Lo)
> >
> >
> >COURSE VISION
> >
> >MIT wants to help solve the worldís biggest and most important problems
> >such as Big Data. Tackling the Challenges of Big Data is an online 
course
> >developed by the MIT Computer Science and Artificial Intelligence
> >Laboratory in collaboration with MIT Professional Education, and edX.
> >
> >MIT Professional Education
> >
> >For 65 years MIT Professional Education has been providing a gateway to
> >renowned MIT research, knowledge, and expertise for those engaged in
> >science and technology worldwide, through advanced education programs
> >designed for working professionals. Read more
> >
> >CSAIL
> >
> >Computer Science and Artificial Intelligence Laboratory (CSAIL)
> >The Computer Science and Artificial Intelligence Laboratory ñ known as
> >CSAIL ñ is the largest research laboratory at MIT and one of the
world> ís
> >most important centers of information technology research. Read more
> >
> >edX
> >
> >Open edX is the opensource educational platform developed by edX and its
> >open source partners, including leading institutions. It powers the 
edX.org
> >destination site and research initiatives. Read more
> >
> >LOCATION
> >
> >This course takes place online. We can also offer this course for large
> >groups of employees from the same organization online. Please contact 
MIT
> >Professional Education (onlinex at mit.edu) to discuss your training and
> >education needs.
> >
> >
> >CSAIL is the largest research laboratory at MIT and one of the worldís
m> ost
> >important centers of information technology research. CSAIL and its 
members
> >have played a key role in the computer revolution. The labís researchers
> >have been key movers in developments like time-sharing, massively 
parallel
> >computers, public key encryption, the mass commercialization of robots, 
and
> >much of the technology underlying the ARPANet, Internet, and the World 
Wide
> >Web.
> >
> >CSAIL members (former and current) have launched more than 100 
companies,
> >including RSA Data Security, Akamai, iRobot, Meraki, ITA Software, and
> >Vertica. The Lab is home to the World Wide Web Consortium (W3C).
> >
> >With backgrounds in data, programming, finance, multicore technology,
> >database systems, robotics, transportation, hardware, and operating
> >systems, each MIT Tackling the Challenges of Big Data professor brings
> >their own unique experience and expertise to the course.
> >
> >Download Course Flyer (pdf):
> >
> ><http://web.mit.edu/professional/pdf/oxp-docs/BigDataCourseFlyer.pdf>
> >
> >Message sent using MelbPC WebMail Server
> >
> >_______________________________________________
> >Link mailing list
> >Link at mailman.anu.edu.au
> >http://mailman.anu.edu.au/mailman/listinfo/link
> 
> --
> 
> Roger Clarke                                 http://www.rogerclarke.com/
> 
> Xamax Consultancy Pty Ltd      78 Sidaway St, Chapman ACT 2611 AUSTRALIA
> Tel: +61 2 6288 6916                        http://about.me/roger.clarke
> mailto:Roger.Clarke at xamax.com.au                http://www.xamax.com.au/
> 
> Visiting Professor in the Faculty of Law            University of N.S.W.
> Visiting Professor in Computer Science    Australian National University
> _______________________________________________
> Link mailing list
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