Spring 2026 198:210
Data Management for Data Science

Mondays-Thursdays, 12:10-1:30pm, BE-100, Livingston Campus

Instructor: Amélie Marian
Office hours: Thursdays 3-4pm CoRE 324 

Announcements

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

1/20: Recitations will start on 1/26

 

Course Description

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 5: Thursday 4:05pm-5:00pm Livingston TIL-253

Section 6: Monday 7:45pm-8:40pm Livingston TIL-253

Section 7: Monday 5:55pm-6:50pm Livingston TIL-253

Section 8: Monday 4:05pm-5:00pm Livingston TIL-253

Prerequisites

CS111 or CS142, or by permission of instructor.

 

Grading

Grading will be based on:

·       Participation/Labs Quizzes (4) – 15%

·       Assignments (4) – 40%

·       Exams (3) – 45%

Regrade requests must be raised within one week of grades being returned. After one week, grades are considered final.

 

 

Schedule (To be Confirmed)

 

Date	Topics	Homework and Quizzes
Part 1: Introduction to Python
Thu January 22 Mon January 27	Introduction to Data Science. Introduction to Python. Basic Python Elements:Variables, Expression, Types
Thu January 29 Mon February 2 Thu February 5	Python Programming Logic. Functions, Branching, Modules Python Lists and Strings
Mon February 9 Thu February 12 Mon February 16	Python Strings and Dictionaries. Loops, Exceptions Importing/Exporting data from files	Quiz 1 (Th 2/12 & M 2/16) Assignment 1 due (tentative 2/16)
Thu February 19	Midterm 1 Exam
Part 2: Data Wrangling with Python
Mon February 23 Thu February 26	Working with CSV and JSON Managing Numeric Data with Numpy
Mon March 2 Thu March 5 Mon March 9 Thu March 12	Transforming and Querying Data Storing and Analyzing Data with Pandas Pandas Dataframes	Quiz 2 (Th 3/5 & M 3/9) Assignment 2 due (tentative 3/23)
Spring Break
Mon March 23 Thu March 26 Mon March 30	Data Cleaning String Manipulations Graphing Data with Matplotlib	Quiz 3 (Th 3/26 & M 3/30)
Thu April 2	Midterm 2 Exam
Mon April 6 Thu April 9	Graphing Data with Matplotlib Visualizing Data	Assignment 3 due (tentative 4/13)
Part 3: Introduction to Databases
Mon April 13 Thu April 16 Mon April 20 Thu April 23 Mon April 27	Relational Databases and SQL	Quiz 4 (Th 4/16 and M 4/20)
Thu April 30	Midterm 3 Exam
Mon May 4	Wrapping up.	Assignment 4 due (5/4)

 

Course Policies

Attendance and Participation 

Students are expected to be present and participate in class but should prioritize their health and safety.

 

Disability Accommodations 

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 

 

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/