Fall 2022 198:142
Data 101 - Data Literacy

Tuesdays-Thursdays, 5:40-7:00pm, ARC-103, Busch Campus

Instructor: Amélie Marian (amelie@cs)

Recitations:

• Section 1: Wednesdays, 8:45am-9:40am, ARC-204 (Busch) ; TA: Jash Gagliani (jg1700) ;
• Section 2: Tuesdays, 8:45am-9:40am, ARC-206 (Busch) ; TA: Janish Parikh (jrp328);
• Section 3: Fridays, 5:55pm-6:50pm, SEC-220 (Busch) ; TA: Ekta Dhobley (ed560);
• Section 4: Fridays, 2:15pm-3:10pm, SEC-220 (Busch) ; TA: Aditya Maheshwari (am2971);
• Section 5: Mondays, 9:35pm-10:30pm, SEC-220 (Busch) ; TA: Aditya Maheshwari (am2971);
• Section 6: Tuesdays, 9:35pm-10:30pm, SEC-211 (Busch) ; TA: Neel Doshi (nsd77);
• Section 7: Thursdays, 9:35pm-10:30pm, ARC-108 (Busch) ; TA: Jash Gagliani (jg1700) ;
• Section 8: Wednesdays, 9:35pm-10:30pm, ARC-207 (Busch) ; TA: Neel Doshi (nsd77);
• Section 9: Mondays, 2:15pm-3:10pm, SEC-205 (Busch) ; TA: Rohit Upadhyay (rru9);
• Section 15: Tuesdays, 12:25pm-1:20pm, BE-201 (Livingston) ; TA: Kunj Mehta (kcm161);

Office hours:
Amélie Marian: Tuesdays 2-3pm CoRE 324

Mondays 3pm-4pm Ekta Dhobley, https://rutgers.webex.com/rutgers/j.php?MTID=m8199e5af516b016c65efb7dca473bdc1
Mondays 7:30-8:30pm Aditya Maheshwari, RUTCOR 111
Tuesdays 8-9pm Aditya Maheshwari, https://rutgers.zoom.us/j/97880854475?pwd=aEVyOG95bEt1NFRuWGtqcDFlK0FBQT09
Wednesdays 9:45-10:45am Jash Gagliani, RUTCOR 111
Wednesdays 10:45-11:45am Neel Doshi, RUTCOR 111
Thursdays 7:30-8:30 pm Jash Gagliani, https://rutgers.webex.com/meet/jg1700
Fridays 10:30-11:30am Neel Doshi, https://rutgers.zoom.us/j/2903166697?pwd=ZlkxaXl6VmZEOXZVU1pJZDVTSmo0Zz09
Fridays 4-5pm Rohit Upadhyay, RUTCOR 111
Fridays 4-5pm Janish Parikh, https://rutgers.webex.com/rutgers/j.php?MTID=m8403f0f94038e2dcf8cc54f4c71871f0
Saturdays 9-10am Kunj Mehta, https://rutgers.webex.com/meet/kcm161

---

Announcements

Recitations will start on 9/12
Class Announcements will be posted via Canvas. If you are registered for the course and do not see the course on Canvas (once the semester has started), please contact the instructor.

Course Description

"Big Data," algorithms, and statistics are everywhere today. But how do you tell good data from bad? Misinformation from useful analysis? And who owns the information about our lives and decisions?

Data 101 will help you improve your data literacy and develop a healthy skepticism about empirical claims presented in the popular media. We will explore examples of erroneous, rushed and ad hoc conclusions based on so-called "big data," and you will get hands-on experience analyzing and using data to make persuasive arguments. You will also learn to make more informed decisions about what you find and share online. Along the way, you will learn fundamental concepts in statistics and probability and acquire basic programming skills that will benefit you in your future coursework and beyond.

This course is recommended for students from all schools and disciplines. (The course does require placement into Intermediate Algebra or above, or credit for 01:640:025.) Data 101 can be used to meet the SAS Core Curriculum goals in 21st Century Challenges [21C], Quantitative and Formal Reasoning [QQ or QR], and Information Technology and Research [ITR].

Prerequisite: Some math Knowledge, placement into Intermediate Algebra or above, or credit for 01:640:025

Grading

Grading will be based on weekly assignments (60%), a midterm (20%), a final project (15%), and participation quizzes (5%).

Readings

Readings will be posted on Canvas.

Schedule (tentative)

Course Policies

Attendance and Participation

Students are expected to be presentin class and participate in the discussions, but should prioritize their health and safety.
If you cannot attend class, please let the instructor know. Students will not be penalized for notified absences.

Disability Accomodations

Students in need of disability accommodations to register for accommodations and consult the policies and procedures of the Office of Disability Services website: https://ods.rutgers.edu

Civility

Some topics covered in the class will relate to ethics and fairness in data management and decision systems. Students are expected to behave in a respectful manner towards everyone in the course, to ensure that all participants in the class feel welcome and supported.

Academic Integrity Policy

Rutgers University takes academic dishonesty very seriously. By enrolling in this course, you assume responsibility for familiarizing yourself with the Academic Integrity Policy and the possible penalties (including suspension and expulsion) for violating the policy. As per the policy, all suspected violations will be reported to the Office of Student Conduct. Academic dishonesty includes (but is not limited to):

• Cheating
• Plagiarism
• Aiding others in committing a violation or allowing others to use your work
• Failure to cite sources correctly
• Fabrication
• Using another person's ideas or words without attribution, including re-using a previous assignment Unauthorized collaboration
• Sabotaging another student's work

If you are ever in doubt, consult your instructor.
Please familiarize yourself with the University Academic Intgrity Policy http://nbacademicintegrity.rutgers.edu/

Student Support and Mental Wellness

In the last few years, we have all been going through a lot, individually and together. It is important to acknowledge that events and circumstances outside of the classroom can impact our ability to be present and engaged at any given moment. At Rutgers, we are focused on the whole student. If, at any point, you experience anything impacting your performance or ability to participate in this class, please reach out to me. Please also see the academic, health, and mental wellness resources on the syllabus as well as others searchable at https://success.rutgers.edu/ for further support.

Additional support resources:

• Student Success Essentials: https://success.rutgers.edu
• Student Support Services: https://www.rutgers.edu/academics/student-support
• The Learning Centers: https://rlc.rutgers.edu/
• The Writing Centers (including Tutoring and Writing Coaching): https://writingctr.rutgers.edu
• Rutgers Libraries: https://www.libraries.rutgers.edu/
• Office of Veteran and Military Programs and Services: https://veterans.rutgers.edu
• Student Health Services: http://health.rutgers.edu/
• Counseling, Alcohol and Other Drug Assistance Program & Psychiatric Services (CAPS): http://health.rutgers.edu/medical-counseling-services/counseling/
• Office for Violence Prevention and Victim Assistance: www.vpva.rutgers.edu/