Data Science 2022

Instructor: Thilanka Munasinghe, munast at rpi dot edu
Class Meeting times: Thursdays 11:00AM - 1:50 PM EST
Class Location: Low Center for Industrial Innovation, Room 4034
Instructor Office Hours: Tue/Fri from 12:30PM - 1:30PM EST or by appointment/ email/ online via WebEx (Instructor online office hours loing information available on LMS
Instructor Office Location: Lally 315
TA: Megan(YingTzu) Yu - yuy12@rpi.edu
TA office hours : Wednesdays 2 PM – 4 PM ET OR by Appointment.
TA Office Hours Location: Lally 215

Sections: CSCI 4350/6350, ITWS 4350/6350, ERTH 4350/6350

Syllabus/ Calendar

Refer to Reading/ Assignment/ Reference list for each week (see below). Recommended Reading/Textbooks : Data Action : Using Data for Public Good by Prof. Sarah Williams

(Reading material available at RPI Library. RPI Library login is required)

Week 1 (Sep. 01): History of Data and Information, Data, Information, Knowledge Concepts and State-of-the-Art
Week 2 (Sept. 08): Data and information acquisition (curation, preservation) and metadata - management
Week 3 (Sept 15): Data formats, metadata standards, conventions, reading and writing data and information
Week 4 (Sept. 22): Module 2 & 3 Review, Data Analysis I
Week 5 (Sept. 29): Class exercise - collecting data - individual
Week 6 (Oct. 06): Class Presentations: present your data, Part of Assignment 2
Week 7 (Oct. 13): Class Presentations: present your data, Part of Assignment 2
Week 8 (Oct. 20): Academic basis for Data Science, Data Models, Schema, Markup Languages, group project, working with someone else's data
Week 9 (Oct. 27): Introduction to Data Mining for Data Science
Week 10 (Nov. 03): Data Analysis II and Class exercise
Week 11 (Nov. 10): Data Workflow Management, Preservation, and Data Stewardship
Week 12 (Nov. 17): Data Quality, Uncertainty and Bias, Final Project Preparation – Project work discussion with the instructor
Week 13 (Nov. 24): No classes: Thanksgiving break – continue project and assignment work
Week 14 (Dec. 01): Webs of Data and Data on the Web, the Deep Web, Data Discovery, Data Integration, Data Citation
Week 15 (Dec. 08): Final project work discussion with the instructor – Group One-on-Ones
Week 16 (Dec. TBA): Final Project Report Submission and Presentations

Reading/ Assignment/ Reference List

Detailed information on reading material will be shared on LMS.

Class 1 Reading Assignment (choose 5-6 and at least 2-3 in-depth):

Changing Science: Chris Anderson: [1]
Rise of the Data Scientist [2]
Where to draw the line? [3]
Career of the Future [4]
What is Data Science (I) [5]
What is Data Science (II) [6]
Data Scientist: The Hottest Job You've Never Heard Of [7]
What Is a Data Scientist? [8]
Data Scientist - sexiest job of the 21st Century ? [9]
An example of data science [10]
Big Data Science [11]
A Very Short History of Data Science [12]
Data Science Programs on the Increase [13]
7 Phases of Data-Life-Cycle[14]

Reference

Fourth Paradigm: [14]
Humanities - Digging into Data [15]
National Science Founcation Cyberinfrastructure Plan chapter on Data [15a]

Class 2: Reading Assignment:

Data Lineage [16]
Earth Science Information Partners Data Management Workshop: [17]
Earth Science Information Partners: Course Outline [18]
Data Management Plan - Univ. Minnesota [19]
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) [20]
Data Management Plan (DMP) and Getting Started with Data Management Plan Template - Univ. Minnesota [21]
Metadata and Provenance Management [22]
Provenance Management in Astronomy (case study) [23]
Web Data Provenance for QA [24]
W3 PROV Overview [25]
W3 PROV Data Model [26]

Class 3: Reading Assignment:

Data formats: netCDF [27]
Spatial Data Transfer Standard GIS format [28]
HDF5 TUTORIAL: Learning HDF5 with HDFVIEW [29]
Metadata Encoding and Transfer Standard - METS [30]
Open Archives Initiative - Protocol for Metadata Harvesting - OAI-PMH [31]
Keyhole Markup Languge - KML Tutorial [32]
Earth Science Markup Language - ESML [33]
HDF5View User's Guide [34]
HDF5 files in Python [35]

Class 4: Reading Assignment:

Brief Introduction to Data Mining [36]
Longer Introduction to Data Mining and slide sets [37]
See the software resources list [38]
Data Analysis - Introduction[39]
Example: Data Mining[40]

Class 5: Reading Assignment: Data Action: Using Data for Public Good: -- How to use data as a tool for empowerment rather than oppression. Class 6: Reading Assignment: None. Class 7: Reading Assignment: preview government and other (science) data repositories Some of these have no single "entry point" to their data; you can find them fairly easily by searching for the name of the agency:

Department of Energy EIA [41]
Humanities - Digging into Data [42]
Environmental Protection Agency (EPA)
US Geological Survey (and state surveys) (USGS), data.usgs.gov
NASA Earth Observing System (EOS) and ECHO, data.nasa.gov
National Oceanic and Atmospheric Administration (NOAA) NCEI, data.noaa.gov
Department of Energy (DoE): [43]
National Library of Medicine (NLM): [43a]
data.gov [44]
data.ny.gov [45]
Find one of your own

Class 8: Reading Assignment:

See Class 4 reading
Knowledge Scientists: Unlocking the data-driven organization : https://arxiv.org/abs/2004.07917
The Worst Chart In The World : https://www.businessinsider.com/pie-charts-are-the-worst-2013-6
What do you mean I'm not supposed to use Pie Charts? : https://www.geckoboard.com/blog/pie-charts/#:~:text=The%20basic%20premis....

Class 9: Reading Assignment: pre-reading

Another Look at Data (Mealy 1967)! [53]
Identifying Content and Levels of Representation in Scientific Data (Wickett et al. 2012) [54]

Class 10: Reading Assignment: none

Introduction to Data Management [45]
Changing software, hardware a nightmare for tracking scientific data [46] (and Parts I, II and III)
Overview of Scientific Workflow Systems, Gil (AAAI08 Tutorial) [47]
Comparison of workflow software products, Krasimira Stoilova ,Todor Stoilov [48]
Scientific Workflow Systems for 21st Century, New Bottle or New Wine? Yong Zhao, Ioan Raicu, Ian Foster [49]
OCLC Sustainable Digital Preservation and Access [50]
Preservation and Access of NOAA Open Data [51]
NITRD report: [52]

Class 11: Reading Assignment: Class 12: Reading Assignment:

The Deep Web (Internet Tutorials) [55]
Digital Image Resources on the Deep Web [56]
Facilitating Discovery of Public Datasets [57]
Tom Heath Linked Data Tutorial (2009)[58]
Relational Databases on the Semantic Web, Tim Berners-Lee, Design Issue Note, 1998-2009. [59]
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. [60]
On directly mapping relational databases to RDF and OWL, 2012, Sequeda, Arenas, Miranker in WWW '12 Proceedings of the 21st international conference on World Wide Web, pp. 649-658 [61]

Class 13: Reading Assignment:

Parsons and Fox Is Data Publication the Right Metaphor?[61]
Beautiful data: [62]
Scientific data management: [63]
BRDI activities: [64]
Data policy [65]

Self-directed study (answer the quiz): [66] Reference material (purchase not required - please ask the instructor if you are interested in any of these):

Course Goals / 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. Through class lectures, practical sessions, written and oral presentation assignments and projects, students should: Develop and demonstrate skill in Data Collection and Management Develop Data Models and Generate Metadata Demonstrate Knowledge of Data Standards Demonstrate Skill in Data Science Tool Use and Evaluation Demonstration the application the Data Life-Cycle principles Become proficient in 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 that violate this trust undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities and The Graduate Student Supplement define 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 questions concerning this policy before submitting an assignment, please ask for clarification. First violation results in zero grade for the relevant portion of the work. Second offense results in a failing grade. Submission of any assignment that is in violation of this policy will result in a penalty for the first violation results in zero grade for the relevant portion of the work. Second offense results in a failing grade. If you have any question concerning this policy before submitting an assignment, please ask for clarification.

COVID-19 code of conduct :

All students must comply with all health and safety protocols specified by the Institute under the Return-to-Campus plan available at the Rensselaer COVID-19 website. Appropriate action will be taken against those who do not comply fully with these protocols”.

This code will apply to any class that meets fully or partially in an on-campus physical classroom for in-person instruction. Violations: Refusal to comply with the COVID-19 code of conduct will be treated just as any classroom disruption, which will receive requests for immediate compliance, failing which the student will be asked to leave the classroom. Any further noncompliance will result in the dismissal of the entire class. All Covid-19 related violations will be reported by the instructor to the Compliance Officer at Lally School, and the Dean of Students. A student found to be in violation of the code, or required repeated reminders for compliance, will be asked to participate in all classes remotely. This is to protect their health and safety as well as the health and safety of their classmates, instructor, and the university community.

Masks: All students must wear a mask in classrooms and all public places including anywhere inside the building. Masks will be provided to the student by the Institute.

Traffic Flow and Social Distancing: Students and faculty will respect the need for social distancing. They are required to follow the traffic flow arrows posted in all rooms and buildings, including bathrooms and common areas.

In-Class Seating: Students should sit in the appropriate designated seating in the classroom. Students are not allowed to move furniture or sit in seats not designated by the Institute.

Students who are ill, under quarantine for COVID-19, or suspect they are ill should not come to class. All faculty will make every reasonable effort to accommodate the student’s absence and will communicate that accommodation directly to the student. Students who need to report an illness should contact the Student Health Center via email or call 518-276-6287. For students seen off campus, a student may request an excused absence via www.bit.ly/rpiabsence with an uploaded doctor’s note that excuses them.

ACADEMIC ACCOMMODATIONS: Rensselaer Polytechnic Institute strives to make all learning experiences as accessible as possible. If you anticipate or experience academic barriers based on a disability, please let me know immediately so that we can discuss your options. To establish reasonable accommodations, please register with The Office of Disability Services for Students (mailto:dss@rpi.edu; 518-276-8197; 4226 Academy Hall). After registration, make arrangements with me as soon as possible to discuss your accommodations so that they may be implemented in a timely fashion.”

COVID-19 code of conduct:

Students taking Lally School of Management classes are required to abide by the COVID-19 code of conduct below. This code will apply to any class that meets fully or partially in an on-campus physical classroom for in-person instruction. Violations: Refusal to comply with the COVID-19 code of conduct will be treated just as any classroom disruption, which will receive request for immediate compliance, failing which the student will be asked to leave the classroom. Any further noncompliance will result in the dismissal of the entire class. All Covid-19 related violations will be reported by the instructor to the Compliance Officer and the Dean of Students. A student found to be in violation of the code, or required repeated reminders for compliance, will be asked to participate in all classes remotely. This is to protect their health and safety as well as the health and safety of their classmates, instructor, and the university community.

In-Class Seating: Students should sit in the appropriate designated seating in the classroom. Students are not allowed to move furniture or sit in seats not designated by the Institute.

Cleaning of Spaces: Students are encouraged to clean the surfaces of the chairs/tables/desks they occupy before they sit down and as they prepare to leave. Cleaning and sanitizing solutions will be provided the classroom. Students who are ill, under quarantine for COVID-19, or suspect they are ill should not come to class. All faculty will make every reasonable effort to accommodate the student’s absence and will communicate that accommodation directly to the student. Students who need to report an illness should contact the Student Health Center via email or call 518-276-6287. For student seen off campus, a student may request an excused absence via www.bit.ly/rpiabsence with an uploaded doctor’s note that excuses them.

Rensselaer Polytechnic Institute On- and Off-Campus Support Resources: Fall 2022 Remember, seeking help is a strength, not a weakness

ON-CAMPUS HEALTH & WELLNESS SUPPORT

Student Health Center*

Mon-Fri, 8:30 am – 5:00 pm EST

The mission of the Student Health Center (SHC) is to keep students healthy so that they may achieve their academic, personal, and athletic goals. The SHC provides confidential, accessible, cost-effective, current evidence-based treatment for acute and chronic physical health problems. At this time, appointments are being offered virtually (phone and video). Call 518-276-6287 to schedule an appointment, or schedule one through your Student Health portal. There are no walk-in appointments available at the Student Health Center during this time. *information subject to change based on pandemic conditions

Counseling Center* Mon-Fri, 8:30 am – 5:00 pm EST (some weekday evening hours available by appointment) The goal of the Counseling Center is to help students maximize their sense of well-being as well as their academic, personal, and social growth. Appointments are free and confidential, and in-person at the Counseling Center, 4th Floor of Academy Hall. Some WebEx and phone appointments will be offered as needed. Please contact the Counseling Center for this service. Appointments can be made by calling 518-276-6479 or email: counseling@rpi.edu Counseling Center staff are available in case of a crisis on evenings and weekends (call Public Safety at 518-276-6611 and ask to speak with the on-call counselor). *information subject to change based on pandemic conditions

Office of Health Promotion Health promotion initiatives at Rensselaer are evidence-based and comprehensive efforts to improve health knowledge, behaviors, and skills of Rensselaer students. Health Educators provide campus programming on a variety of health topics, and are available for one-on-one consultations around issues including, but not limited to: sleep hygiene, mental health, sexual health, alcohol and other drugs, LGBTQIA+ topics, sexual assault prevention, and more. All appointments are free and confidential and take place via WebEx. To schedule an appointment, email: healthed@rpi.edu Follow us on social media for daily health tips and event information! Instagram: rpi.studenthealth |Twitter: @RPIhealth | Facebook: RPI Student Health Services | Discord: https://discord.gg/8DZJJ38zWj Disability Services for Students The Office of Disability Services for Students (DSS) assists Rensselaer students with disabilities in gaining equal access to academic programs, extracurricular activities, and physical facilities on campus. DSS is the designated office at Rensselaer that obtains and files disability-related documentation, assesses for eligibility of services, and determines reasonable accommodations in consultation with students. Call 518-276-8197 or email dss@rpi.edu for more information.

Course: Data Science

Date: 2022-08-29 to 2022-12-21

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