Instructor: Amélie Marian
Office hours: Tuesdays
2-3pm CoRE 324
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.
TA contact information
and office hours posted on Canvas
9/2: Recitations will
start on 9/9
This course is designed to provide students with the knowledge and skills needed to acquire and curate real word data, to explore the data to discover patterns and distributions, and to manage large datasets with databases.
Students will learn the minimal aspects of Python as needed to acquire and curate datasets. Much of their work will be done using Python libraries that deliver maximum benefit with minimal programming effort: to get data from various online data sources online, detect which aspects of data are incomplete or unreliable and understand why it is so, learn various domain independent and domain dependent ways to curate the data, and get the curated data into a form that can be explored, managed and analyzed. Students will also learn how to get datasets into database-ready form, and do basic analysis of such datasets using relational databases and SQL, and (time permitting) NoSQL databases.
The course content is designed to be accessible to all SAS students regardless of their major. Some small amount of programming background is expected via CS 111 (Java) or CS 142 (R).
Recitations
Recitations will consist of labs, using Python Jupyter notebooks.
Section 1: Friday 10:35am-11:30am Livingston BE-250
Section 2: Friday 12:25pm-1:20pm Livingston BE-250
Section 3: Wednesday 5:55pm-6:50pm Livingston BRR-5105
Section 4: Wednesday 12:25-1:20pm Livingston TIL-226
Prerequisites
CS111 or CS142, or by permission of instructor.
Grading will be based on:
· Participation Activities - 5%
· Homework Assignments (3-4) – 30%
· Online Quizzes (4) – 20%
· Midterms (2) – 25%
· Final Exam (1) – 20%
Regrade requests must be raised within one week of grades being returned. After one week, grades are considered final.
The course will use a Zybook for reading and participation activities.
Date |
Topics |
Homework and Quizzes |
Part 1: Introduction to Python |
||
Tue
September 3 |
|
|
|
Python
Programming Logic. |
|
|
Lecture Canceled |
Quiz 1 |
|
Python
Strings and Dictionaries. Loops, Exceptions |
|
Tue October 1 |
Midterm 1 Exam |
|
Part 2: Python Libraries |
||
|
Importing/Exporting
data from files |
|
|
|
Quiz 2 |
Part 3: Data Wrangling with Python |
||
Tue
October 22 |
Storing
and Analyzing Data with Pandas |
Assignment 1 due |
Thu October 31 |
Midterm Exam |
|
Tue
November 5 |
|
|
|
|
Assignment 2 |
Part 4: Introduction to Databases |
||
Thu
November 21 |
Relational Databases and SQL |
Assignment 3 due (tentative) |
Thanksgiving break |
||
Tue
December 3 |
|
Quiz 4 (tentative) |
Tue December 10 |
Wrapping up. |
|
Tue December 17 |
Final Exam |
Students are expected to be present and participate in class but should prioritize their health and safety.
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
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/
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/