Data Science 2011

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Instructor: Professor Peter Fox - foxp at rpi dot edu
TA: Weijing Chen - chenw8 at rpi dot edu
Meeting times: Tuesday morning 9:00 am - 11:50 am.
Office Hours: Monday 2:00-3:00pm in Winslow 2120 or by appointment in JRSC 1W06
phone: 518-276-4862
Class Listing: DATA SCIENCE - 45229 - ITEC- CSCI -ERTH 6961 - 01
Class Location SAGE 2715 (note room change)

Table of Contents

Description

Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

Syllabus/ Calendar

Refer to Reading/ Assignment/ Reference list for each week (see below). Note that the schedule is likely to change based on the number of people in the class, especially around weeks 5 and 6.

  • Week 1 (Aug. 30): History of Data and Information, Data, Information, Knowledge Concepts and State-of-the-Art, Data life-cycle for Science; Data acquisition, curation, preservation, metadata Week 1 slides [Download]
  • Week 2 (Sep. 6): Data and information acquisition (curation) and metadata/ provenance - management Week 2 slides [Download]
  • Week 3 (Sep. 13): Data formats, metadata standards, conventions, reading and writing data and information Week 3 slides [Download]
  • Week 4 (Sep. 20): Class exercise - collecting data - individual Week 4 notes [Download]
  • Week 5 (Sep. 27): Class Presentations: present your data I
  • Week 6 (Oct. 4) : Class Presentations: present your data II
  • Oct. 11 - no classes (Tuesday follows Monday schedule)
  • Week 7 (Oct. 18): Data Analysis and Data Mining Week 7 slides [Download]
  • Week 8 (Oct. 25): Academic basis for Data and Information Science, Data Models, Schema, Markup Languages and Data as Service Paradigms and Class exercise - group project - working with someone else's data Week 8 slides and notes [Download]
  • Week 9 (Nov. 1): no lecture - continue work on group projects
  • Week 10 (Nov. 8): 0930 AM - Guest lecture - Prof. Bulent Yener slides
  • Week 11 (Nov. 15): Data Workflow Management, Preservation and Data Stewardship Week 11 slides [Download]
  • Week 12 (Nov. 22): Webs of Data and Data on the Web, the Deep Web, Data Discovery, Data Integration, Data Citation Week 12 slides [Download]
  • Week 13 (Nov. 29): Final Project Presentations

Reading/ Assignment/ Reference List

Class 1 Reading Assignment:

Reference

  • Fourth Paradigm: [9]
  • Humanities - Digging into Data [10]

Class 2: Reading Assignment:

  • MIT Libraries: [11]
  • Earth Science Information Partners Data Management Workshop: [11a]
  • Earth Science Information Partners: [11b]
  • Univ. Minnesota [12]
  • Moore et al., Data Management Systems for Scientific Applications, IFIP Conference Proceedings; Vol. 188, Proceedings of the IFIP TC2/WG2.5 Working Conference on the Architecture of Scientific Software, pp. 273 – 284 (2000) [13]
  • Data Management and Workflows [14]
  • Metadata and Provenance Management [15]
  • Provenance Management in Astronomy (case study) [16]
  • Web Data Provenance for QA [17]

Assignment 1 - Data Science 2011 Assignment 1 [Download] Preparing for Data Collection (10% of grade) due week 3 on Sept. 13, 2011

Class 3: Reading Assignment:

  • Data formats: netCDF [18]
  • Spatial Data Transfer Standard GIS format [19]
  • Metadata resources [20]
  • Metadata Encoding and Transfer Standard - METS [21]
  • Open Archives Initiative - Protocol for Metadata Harvesting - OAI-PMH [22]

  • Keyhole Markup Languge - KML Tutorial [23]
  • Earth Science Markup Language - ESML [24]
  • Climate Science Markup Language - CSML [25]

Class 4: Reading Assignment: None

Class 5: Reading Assignment:

Class 6: Reading Assignment:

  • None

Assignment 3: Data Science 2011 Assignment 3 [Download] Reformatting Data (20% of grade) due in week 8, October 25, 2011

Class 7: Reading Assignment: preview government and other (science) data repositories

  • Department of Energy EIA [30]
  • Humanities - Digging into Data [31]
  • Environmental Protection Agency (EPA)
  • US Geological Survey (and state surveys) (USGS)
  • NASA Earth Observing System (EOS) and ECHO
  • National Oceanic and Atmospheric Administration (NOAA) NODC, NGDC, NCDC
  • Department of Energy (DoE): [32]
  • National Library of Medicine (NLM): [32a]
  • Cancer Grid (CaBIG)
  • OneGeology
  • data.gov [33]
  • Find one of your own

Class 8: Reading Assignment:

  • None

Assignment 4: Data Science 2011 Assignment 4 [Download] Working with someone else's data (40% of grade)

Class 10: Reading Assignment:

  • NITRD report: [38]
  • OCLC Sustainable Digital Preservation and Access [39]
  • National Science Founcation Cyberinfrastructure Plan chapter on Data [40]
  • European High-Level Group on Data [41]
  • Relational Databases on the Semantic Web, Tim Berners-Lee, Design Issue Note, 1998-2009. [42]
  • A Survey of Current Approaches for Mapping of Relational Databases to RDF (PDF), Satya S. Sahoo, Wolfgang Halb, Sebastian Hellmann, Kingsley Idehen, Ted Thibodeau Jr, Sören Auer, Juan Sequeda, Ahmed Ezzat, 2009-01-31. [43]

Class 11: Reading Assignment:

  • Introduction to Data Management [44]
  • Changing software, hardware a nightmare for tracking scientific data [45] (and Parts I, II and III)
  • Overview of Scientific Workflow Systems, Gil (AAAI08 Tutorial) [46]
  • Comparison of workflow software products, Krasimira Stoilova ,Todor Stoilov [47]
  • Scientific Workflow Systems for 21st Century, New Bottle or New Wine? Yong Zhao, Ioan Raicu, Ian Foster [48]

Class 12: Reading Assignment:

  • Semantic Deep Web, James Geller, Soon Ae Chun, and Yoo Jung An, [49]
  • The Deep Web (Internet Tutorials) [50]
  • Digital Image Resources on the Deep Web [51]

Assignment - final: Data Science 2011 Final Assignment [Download] Stewardship: Workflow construction for Preservation (10% of grade)

Class 13: Reading Assignment:

Reference material (purchase not required - please ask instructor if you are interested in any of these):

  • Beautiful data: [52]
  • Scientific data management: [53]

  • Interface to Science Archives [54]

Goals

To instruct future scientist how to sustainably generate/ collect and use data for their research as well as for others: data science. Participants will learn and be evaluated on the full life-cycle of data and relevant methods, technologies and best practices.

Topics for Data Science/ Foundations:

  • History of Data and Information
  • Data, Information, Knowledge Concepts and State-of-the-Art
  • Academic basis for Data and Information Science
  • Introduction to Informatics
  • Data life-cycle for Science
  • Data acquisition, curation, preservation
  • Data Integration
  • Metadata
  • Data Models, Schema
  • Data Tools and Data as Service Paradigms
  • Webs of Data and Data on the Web, the Deep Web
  • Data Workflow Management
  • Data Visualization
  • Data Discovery
  • Data and Information Management

Data Science Applications:

  • Geoscience
  • Biology
  • Sun, Earth, Environment and Climate
  • Chemistry, Physics and Astronomy
  • Environmental Engineering
  • Digital Libraries and Scientific Publications

Data Science Project options (examples):

  • Data Collection and Management
  • Data Models and Metadata
  • Data Standards
  • Tool Use and Evaluation
  • Data Life-Cycle Studies
  • Data and Information Product Generation

Academic Integrity

Student-teacher relationships are built on trust. For example, students must trust that teachers have made appropriate decisions about the structure and content of the courses they teach, and teachers must trust that the assignments that students turn in are their own. Acts, which violate this trust, undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities defines various forms of Academic Dishonesty and you should make yourself familiar with these. In this class, all assignments that are turned in for a grade must represent the student’s own work. In cases where help was received, or teamwork was allowed, a notation on the assignment should indicate your collaboration. Submission of any assignment that is in violation of this policy will result in a penalty. If found in violation of the academic dishonesty policy, students may be subject to two types of penalties. The instructor administers an academic (grade) penalty, and the student may also enter the Institute judicial process and be subject to such additional sanctions as: warning, probation, suspension, expulsion, and alternative actions as defined in the current Handbook of Student Rights and Responsibilities. of an academic grade penalty or . If you have any question concerning this policy before submitting an assignment, please ask for clarification.

Objectives

  • To instruct future scientists how to sustainably generate/ collect and use data for their research as well as for others: data science.
  • To instruct future technologists how to understand and support essential data and information needs of a wide variety of producers and consumers
  • For both to know tools, and requirements to properly handle data and information
  • Will learn and be evaluated on the full life-cycle of data and relevant methods, technologies and best practices.
  • Course Learning Objectives

    Through class lectures, practical sessions, written and oral presentation assignments and projects, students should:

    • Understand and develop skill in Data Collection and Management
    • Understand and know how to developData Models and Metadata
    • Knowledge of Data Standards
    • Skill in Data Science Tool Use and Evaluation
    • Understand and apply the Data Life-Cycle principles
    • Become proficient in Data and Information Product Generation

    Assessment Criteria

  • Via written assignments with specific percentage of grade allocation provided with each assignment
  • Via oral presentations with specific percentage of grade allocation provided
  • Via group presentations
  • Via participation in class (not to exceed 10% of total)
  • Late submission policy: first time with valid reason – no penalty, otherwise 20% of score deducted each late day
  • Suggested Prerequisites

    • Knowledge such as that gained in a Data Base class (e.g., CSCI-4380)
    • Knowledge such as that gained in a Data Structures class (e.g., CSCI-1200)
    • or permission of the instructor

    Attendance Policy

    Enrolled students may miss at most one class without permission of the instructor.