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_challenges_of_big_data.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 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 make your work more productive 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 bachelors 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 companys approach to Big Data problems and 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 data integration problem: 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 companys 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 todays 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 worlds 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 known as CSAIL is the largest research laboratory at MIT and one of the worlds 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 ([email protected]) to discuss your training and education needs. CSAIL is the largest research laboratory at MIT and one of the worlds most important centers of information technology research. CSAIL and its members have played a key role in the computer revolution. The labs 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
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