Instructor: Amélie Marian (amelie@cs)
Recitations:
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
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.
"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 will be based on weekly assignments (60%), a midterm (20%), a final project (15%), and participation quizzes (5%).
Readings will be posted on Canvas.
Date |
Topics |
Assignments |
Part 1: Manipulating Data: Introduction to
R |
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Tue September 6 |
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Thu September 8 |
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Assignment 1: Find Interesting Data Due Monday September 19 Assignment 2: Plot your Data Due Monday September 26 Assignment 3: Explore a Dataset Due Monday October 3 |
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Data Visualization. |
Assignment 4: Lie with Data Due Monday October 10 |
Part 2: Understanding Data: Statistical Analysis |
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Thu September 29 |
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Assignment 5: Test Hypotheses Due Monday October 17 |
Thu October 13 |
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Assignment 6: Data-based arguments Due Monday October 24 Assignment 7: Creating a Data 101 dataset Due Monday October 24 Assignment 8: De-anonymize Data Due Monday November 7 |
Tue October 25 |
Bayesian Reasoning. Bayesian Approach. |
Assignment 9: Prior and Posterior Beliefs Due Monday November 14 |
Tue November 1 |
Midterm review | |
Thu November 3 |
Midterm Exam (tentative) |
Part 4a: Trusting Data; Data and Society |
Tue November 8 |
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Part 3: Predicting Data: Identifying patterns |
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Thu November 10 |
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Assignment 10: Decision Trees Due Monday November 21 Assignment 11: Prediction Challenge 1 Due Monday November 28 Assignment 12: Prediction Challenge 2 Due Monday December 5 |
Tue November 22 |
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Thanksgiving break - No recitation this week |
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Tue November 29 |
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Part 4b: Trusting Data; Data and Society |
Thu December 1 |
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Final Project Due Monday December 12 |
Tue December 13 |
Wrapping up: Prediction competition and Project discussion |
Assignment 13 (optional): Prediction Challenge 3 Due Monday December 19 |
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.
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
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.
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):If you are ever in doubt, consult your instructor.
- 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
Please familiarize yourself with the University Academic Intgrity Policy http://nbacademicintegrity.rutgers.edu/
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/