DATA 101: Making Sense of a Data-Oriented Society
SYLLABUS – Semester/Year
This syllabus is subject to change. The most up-to-date copy will always be available in
Canvas in the course shell. Students are responsible for accessing the most recent
syllabus.
PART 1. COURSE INFORMATION
Department: School of Management
Course Title: Making Sense of a Data-Oriented
Society
Instructor: Prof. William Wall
Email: wwall01@nyit.edu
Office Hours: By Appointment
Course CRN #: DATA 101
Pre-Requisites: none
Credits: 3
CATALOG COURSE DESCRIPTION
This course introduces students to the power of data as applied to real-life problems in
today’s data-driven world. Students will learn basic statistical concepts, how to identify
reliable data, and to think critically about how to extract meaning from data. The course
will discuss various biases, including social biases, how they affect data gathering and
analysis, and how to address these biases. The course will also address ethical and
moral issues associated with statistics, data collection and visualization, and data
analysis. Students will learn how to present a narrative supported by data.
COURSE OVERVIEW
This course will combine theoretical principles with hands-on exercises, requiring
teamwork, critical thinking, and problem solving. The group project aims to equip
students with practical knowledge pertaining to how reliable data sources can be
identified, how statistical analysis can extract meanings from data, and how the
outcome of such analyses could be represented graphically.
The objective of this interdisciplinary course is to provide undergraduate students
with a basic understanding of how data can create both great opportunities but
also can be misrepresented easily in a modern society. During the semester,
students will learn about the scientific principles of data analysis that should be
followed to investigate cultural, political, social, or economic phenomena.
Moreover, students will learn about the importance of ethical considerations, and
how bias can affect the process of data collection and interpretation.
REFERENCE RESOURCE(S):
Recommended readings
Beaulieu, A., & Leonelli, S. (2021). Data and Society: A Critical
Introduction. SAGE. or
Bergstrom, Carl T., and Jevin D. West. Calling bullshit: the art of
skepticism in a data-driven world. Random House Trade
Paperbacks, 2021.
Pink, S., Ruckenstein, M., Willim, R., & Duque, M. (2018). Broken data:
Conceptualising data in an emerging world. Big Data & Society, 5(1),
2053951717753228.
Sadowski, J. (2019). When data is capital: Datafication, accumulation,
and extraction. Big data & society, 6(1), 2053951718820549.
Hand, D. J. (2018). Aspects of data ethics in a changing world: Where are
we now?. Big data, 6(3), 176-190.
Paul Beynon-Davies, Data and Society World Scientific Pub Co Inc
(August 11, 2021), ISBN: 978- 9811237249
Announcements
I will use Canvas Announcements to communicate important course information.
Announcements posted in Canvas and may or may not be sent via email. I highly
recommend you adjust your Canvas Notifications page to give you email or text
message reminders about Announcements and other course notifications. Learn how to
adjust your notification settings by watching this online video tutorial or reading this textbased tutorial.
Email
The best way to communicate with me is via email at wwall01@nyit.edyu. You can
expect a response within 24 hours during the week and 48 hours on the weekends &
holidays. FERPA laws require that faculty communicate directly with students about
their education, therefore, you must use your New York Tech address when
communicating with me about your class.
Support for Canvas, Zoom, and other Technologies
Support for Canvas, Zoom, and other technologies is available through Service Central
via website, email, or phone.
● Website: https://www.nyit.edu/service_central
● Email: servicecentral@nyit.edu
● Phone Number: 516.686.1400
PART 2. LEARNING OBJECTIVES AND ASSESSMENTS
COURSE LEARNING OBJECTIVES
Upon the successful completion of this course, students will be able to:
LO1: Identify, evaluate, use, and communicate quantitative information
clearly, effectively, and responsibly.
LO2: Distinguish among opinions, facts, and inferences and develop wellsupported arguments, grounded in data, that convey diverse viewpoints;
LO3: Identify ethical and moral dilemmas resulting from data analysis,
interpretation, and presentation;
LO4: Demonstrate knowledge of basic statistical concepts;
LO5: Apply critical thinking and statistical concepts to the
analysis of results;
LO6: Collaborate effectively in teams.
HOW TO SUCCEED IN THIS COURSE
● Set yourself up to succeed with strong time management, study habits, testtaking, and stress management skills. Review New York Tech's skill building
page to learn more about these skills, and if you are taking an online course, visit
the Finding Success in Online Learning Canvas course. See the Student
Resources section of this syllabus in Section 5: College Policies and Student
Resources
● Complete all course readings and review all course resources.
● Complete all assignments on time.
● Attend class or log into the course site regularly.
● Participate in learning activities and engage your peers using appropriate
netiquette (see netiquette policy below).
ASSESSMENTS AND EXPECTATIONS
Viewing Grades on Canvas: Points you've earned for graded activities will be posted to
the grade book in Canvas. In this course, you will be assessed on the following:
Percentage
15%
15%
15%
15%
10%
15%
15%
100%
Activity/Assignment
Exam 1
Exam 2
Group Project -- Infographics
Power Point or Poster Session (Presentation)
Final Report: Evaluation of Self and Others
Reflection (two)
Other Shorter Assignments
TOTAL
Final course letter grades will be assigned as follows:
Letter
Grade
A
AB+
B
BC+
C
CD
F
Minimum % or
Score
94+
90-92
87-89
84-86
80-83
77-79
74-76
70-73
60-69
Below 60
View assignment descriptions in Canvas to see full details.
● Exams 1 and 2 (15% each): The exams will include questions related to case
studies that will test whether the students understand the ethical and moral
implications of data biases and data analysis. The exams will assess LO2, LO3, and
LO4.
● Group Project -- Infographics (15%): Student teams (2-3 students per team) will
be given a dataset from a curated library. They will use the dataset to run some
●
●
●
●
simple analyses using tools provided as part of the library, and will create two
infographics: one accurate, and one “manipulative”, which is designed to lead to a
false conclusion. The infographic will assess LO1, LO4, LO5, and LO6
PowerPoint or Poster Session (15%): The teams will present their infographics in
a poster session. Students will answer questions and discuss their infographics with
other students. The session will assess LO1, LO5, LO6.
Reflections (15%): two reflections regarding individual progress in this course
Evaluation of Self and Others (10%): Evaluation activities will occur in-class and
via Canvas assignments. Each student will prepare an individual final report which
includes: (1) a critique of one presentation from another group. (2) a selfassessment and retrospective on the team dynamics. Presentation session
reflections will assess LO2, LO3, LO5, LO6.
Other shorter assignments (15%): Shorter assignments will occur in-class and via
Canvas assignments.
PART 3. COURSE SCHEDULE
The possibility exists that unforeseen events will make schedule changes necessary.
Any changes will be clearly noted in course Announcements or through New York Tech
email.
Week
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7
Week 8
Week 9
Week 10
Week 11
Week 12
Week 13
Week 14
Week 15
Topic
Introduction
Causality
Data type & data collection basics; validity of data sets
Sources and levels of bias in data (cultural and individual)
Exam 1 + Identifying & avoiding bias in data analytics
Data ethics & privacy; machine learning and AI
Data visualizations
Descriptive statistics: concepts
Descriptive statistics: toolkits (excel hands on) & case study
Project introduction + Exam 2
Story telling: building a narrative from data
Exploring problems in data
Work session
Poster session of infographics
Final report (critical evaluation of someone else's
infographic) and self-assessment
PART 4. COURSE POLICIES
ATTENDANCE POLICY
Students are expected to attend every class session. Instructors will inform students of
the exact number of absences and late-arrivals permitted during the semester. Students
who exceed these limits may be subject to failure. If a student misses any class or test,
the instructor has the right to either grant or deny an opportunity to make up the work
that was missed. In such cases, the instructor shall be the sole judge of the validity of a
student's explanation for having missed the class or test.
LATE WORK POLICY
You can expect an automatic deduction (up to 10%) for late submissions. The instructor
can allow make up assignments based on individual circumstances (e.g., family
emergency).
CLASSROOM BEHAVIOR POLICY
Behavior that disrupts, impairs, interferes with, or obstructs the orderly conduct,
processes, and functions within an academic classroom or laboratory violates the
student code of conduct and may result in disciplinary action. This includes interfering
with the academic mission of NYIT or individual classroom or interfering with a faculty
member’s or instructor’s role to carry out the normal academic or educational functions
of his classroom or laboratory, including teaching and research.
PART 5. COLLEGE POLICIES & STUDENT RESOURCE
STUDENT RESOURCES FOR ACADEMIC SUCCESS
New York Tech is committed to supporting your educational journey with the support
and resources you need to succeed. Students should familiarize themselves with the
following services:
● The Learning Center at New York Tech offers peer tutoring, online learning
assistance, academic workshops and 24/7 free virtual tutoring from Brainfuse
Tutoring.
● The Writing Center supports students in every phase of the writing process with
one-on-one appointments.
● The Math Resource Center offers virtual and in-person appointments.
● The Science Learning Center offers tutoring appointments with experienced
science teachers.
● Academic Computing Services offers support with printing, wifi access.
LIBRARY RESOURCES
All students can access the NYIT virtual library from both on and off campus at
www.nyit.edu/library. The same login you use to access your New York Tech e-mail and
NYITConnect will also give you access to the library’s resources from off campus.
On the upper left side of the library’s home page, select links for “Find Resources”,
“Research Assistance”, “Services”, “Help”, and “About”. Using “Quick Links” on the right
hand side of the home page will also assist you in navigating the library’s web pages.
Should you have any questions, please look under “Research Assistance” to submit a
web-based “Ask-A-Librarian” form.
You can also set an appointment to Meet with a Librarian to get guidance and support
on your academic research.
ACADEMIC INTEGRITY POLICY STATEMENT
Each student enrolled in a course at New York Tech agrees that, by taking such course,
he or she consents to the submission of all required papers for textual similarity review
to any commercial service engaged by New York Tech to detect plagiarism. Each
student also agrees that all papers submitted to any such service may be included as
source documents in the service’s database, solely for the purpose of detecting
plagiarism of such papers.
Plagiarism is the appropriation of all or part of someone else’s works (such as but not
limited to writing, coding, programs, images, etc.) and offering it as one’s own. Cheating
is using false pretenses, tricks, devices, artifices or deception to obtain credit on an
examination or in a college course. If a faculty member determines that a student has
committed academic dishonesty by plagiarism, cheating or in any other manner, the
faculty has the academic right to 1) fail the student for the paper, assignment, project
and/or exam, and/or 2) fail the student for the course and/or 3) bring the student up on
disciplinary charges, pursuant to Article VI, Academic Conduct Proceedings, of the
Student Code of Conduct.
Cheating on an examination in this course will result in a zero for the examination and
the matter will be reported to the appropriate college authorities as per the Student
Code of Conduct. A second incident of cheating on an examination will result in failure
for the course.
Please note: Some assessments in this class may be checked for plagiarism using
Canvas's native plagiarism detection software, Turnitin. Submitting work in Canvas will
have access to the originality reports, and submission of assignments denotes consent
to having their work checked for academic integrity and plagiarism.
STUDENT ACCOMMODATIONS AND ACCESSIBILITY SERVICES
New York Tech adheres to the requirements of the Americans with Disabilities Act of
1990 and the Rehabilitation Act of 1973, Section 504. The Office of Accessibility
Services actively supports students in the pursuit of their academic and career goals.
Identification of oneself as an individual with disability is voluntary and confidential.
Students wishing to receive accommodations, referrals and other services are
encouraged to contact the Office of Accessibility Services as early in the semester as
possible, although requests can be made throughout the academic year. Visit
https://www.nyit.edu/administrative_offices/accessibility_services to for policies, forms,
and learn more about your rights and responsibilities. To contact the Office of
Accessibility Services please send an e-mail to accessibility@nyit.edu or call 516.
686.4934 for the Long Island campus (Old Westbury) and 212.261.1759 for the
Manhattan campus (Monday - Friday 9 a.m. - 5 p.m. (ET))
NETIQUETTE GUIDELINES
Internet etiquette is known as “netiquette,” and includes habits and practices that
encourage collegial and respectful dialogue in an online setting. Students are expected
to abide by these guidelines in all of their online communications.
● Hold High Standards in Your Written Communications:
o Address your professor by their title, unless specifically told otherwise.
o Avoid writing in ALL CAPS, which is the online equivalent of yelling.
o Avoid sarcasm, which is difficult to interpret in an online setting.
● Remember to write "please" and "thank you," and to address your peers by name.
● Make Meaningful Contributions: A great post is more than just spell-checked and
written in a respectful or academic tone. Online discussions are often a key part of a
course experience. Check to ensure your posts are expanding the conversation,
adding information, sharing a meaningful reflection, or contributing in a significant
way.
● Give Credit: Whether you’re referring to a text, a video, or even one of your peers,
cite or identify your sources for the benefit of everyone.
● Stay Focused: While peripheral content in your post may be appropriate, be sure to
effectively thoroughly and clearly respond to the discussion board prompt.
● Disagree Politely: Productive disagreement is a learning activity, and to be expected
in an online course. Never attack a fellow student for their opinions, but focus
instead on discussing the ideas. Look for common ground, and state your own
position without disrespecting others.
● Fact-check Yourself: Distinguish between fact or opinion; when responding to peers,
be certain of the facts, and never hesitate to disclose if you are not.
● Be Brief: Clear, concise posts are more likely to garner responses and encourage
conversation in your online community.
● Be Patient: Online communication can be challenging. Be measured in your
reactions and be willing to overlook the errors of your peers.
COURSE MATERIAL AND COPYRIGHT STATEMENT
Course material accessed from Canvas, Zoom, etc. is for the exclusive use of students
who are currently enrolled in the course. Content from these systems cannot be reused
or distributed without written permission of the instructor and/or the copyright holder.
Duplication of materials protected by copyright, without permission of the copyright
holder is a violation of the Federal copyright law, as well as a violation of New York
Tech’s Academic Integrity.
BASIC NEEDS FOR STUDENTS
A healthy lifestyle, including access to nutritious food, housing, and other basic needs
and resources, is essential for students to reach their highest personal and academic
potential. To ensure that all its students have access to healthy food, information and
resources, New York Institute of Technology launched the Bear Bytes initiative. One of
its programs is the Grizzly Cupboard.
The Grizzly Cupboard is a food and resource pantry located on each New York campus.
It is open during the fall and spring semesters and provides food and other health and
wellness resources to all students. For local food pantries and health, wellness,
housing, and financial resources, students may visit the Bear Bytes web page to learn
more.