Introduction to Computer Science II (CSC 242)
Section 507, Winter 2026
Overview
This course is the second of a two-course sequence introducing
Computer Science skills of problem solving, algorithm development
and programming using Python. In particular, the concept of a class
and object oriented programming will be motivated and introduced.
The algorithm development technique of recursion will also be
introduced. We will apply these skills in several application areas
of Computer Science including web search.
Preconditions
You must have taken CSC241 or an equivalent course that introduces
problem solving techniques and programming in Python. I will assume
that:
- you know how to create, debug, compile and run Python, and
you use a reasonable programming style (i.e. your code is easy
to read and concise);
- you know Python's basic control structures and types;
- you can solve basic algorithmic problems.
Postconditions
After the successful completion of this course:
- you will strengthen your Python programming skills;
- You will understand the role of namespaces to support code
encapsulation and abstraction;
- you will know how to design classes and understand the
fundamental principles of object-oriented programming;
- you will be able to apply recursion as a problem solving and
programming technique;
- you will be able to write simple Internet client programs
including a basic web crawler.
Course Calendar
[subject to change]
Instructors
Please send me an email if you need an appointment at another time.
Class Hours
SECTION 507
|
Lectures
|
TuTh
|
3:10pm-4:40pm |
Room 315 in 243 S Wabash
|
|
Lab
|
Mo
|
1:30pm-3:00pm
|
Room 512 in 14 E Jackson
|
Texts
- Required
- Introduction to Computing Using Python: An Application
Development Focus, Second Edition, Ljubomir Perkovic, John Wiley
& Sons, 2015.
- Note: The E-Book version of the textbook has everything you
will need for this and the followup course (CSC 242). The
Paperback version is missing the Case Studies Appendix; The Case
Studies Appendix can be purchased in E-Book form separately
through https://store.vitalsource.com/search?q=9781119185390.
Course web page
This syllabus, as well as the class lecture notes, homework
assignments, D2L links, and other links can be found on the course
web page at
https://reed.cs.depaul.edu/lperkovic/courses/csc242. Please
check this site and the discussion forum regularly.
Grading
The course grade will be apportioned as follows:
homework assignments
|
30%
|
| midterm exam |
30% |
| final exam |
40% |
All homework must be submitted by the deadline and no later. Any
homework not handed in by the deadline will receive 0 points,
without any exceptions. There will be a total of 9 homework
assignments, but only your best 8 count toward the final grade, so
you may miss up to one homework assignment without penalty. There
will be a total of 8 labs; if you attend 6 or more labs then your
best 7 homework assignments will count toward the final grade, so
you may miss up to two homework assignments without penalty.
To do well in this course, you should attend the class and the
labs regularly, participate in the class, lab, and online
discussions, read the chapters in the book each week as indicated
in the homework assignment, start working on the homework early,
and talk to me promptly if you have any problems. The answers to
the homework and exam questions should be written in a way that is
rigorous, clear, and concise.
Policies
Lateness and
Absence
No late homework will be accepted. If you don't hand in a
homework/lab in time, you will receive 0 points for the homework.
Midterm and final exams makeups must be arranged at least one week
in advance, barring extreme situations.
Deadlines for adds, drops,
and withdraws
See the deadlines here.
Changes to
Syllabus
This syllabus is subject to change as necessary during the quarter.
If a change occurs, it will be thoroughly addressed during class,
posted under Announcements in D2L, and sent via email.
Online Course Evaluations
Evaluations are a way for students to provide valuable feedback
regarding their instructor and the course. Detailed feedback will
enable the instructor to continuously tailor teaching methods and
course content to meet the learning goals of the course and the
academic needs of the students. They are a requirement of the course
and are key to continue to provide you with the highest quality of
teaching. The evaluations are anonymous; the instructor and
administration do not track who entered what responses. A program is
used to check if the student completed the evaluations, but the
evaluation is completely separate from the student’s identity. Since
100% participation is our goal, students are sent periodic reminders
over three weeks. Students do not receive reminders once they
complete the evaluation. Students complete the evaluation online
in CampusConnect.
Academic Integrity and Plagiarism
This course will be subject to the university's academic integrity
policy. More information can be found at here.
If you have any questions be sure to consult with your professor.
All students are expected to abide by the University's Academic
Integrity Policy which prohibits cheating and other misconduct in
student coursework. Publicly sharing or posting online any prior or
current materials from this course (including exam questions or
answers), is considered to be providing unauthorized assistance
prohibited by the policy. Both students who share/post and students
who access or use such materials are considered to be cheating under
the Policy and will be subject to sanctions for violations of
Academic Integrity.
Generative AI tools are trained on existing texts to generate
content like writing and code based on prompts from users. ChatGPT,
Gemini, and Claude are examples of generative AI tools. You are
prohibited from using generative AI when working on homework
assignments, lab exercises, and exam questions in this course. The
only exception to this is when you are explicitely given
instructions by the instructor to use a generative AI tool in the
context of a specific assignment or exercise. We will be developing
skills that are important to practice on your own and using
generative AI may in general inhibit development, practice, or
understanding of those skills.
If you’re unsure if a specific tool makes use of AI, or if a
specific tool is permitted for use on assignments in this course,
please contact me. Attempting to pass off AI-generated work as your
own will violate DePaul’s Academic Integrity Policy and could result
in failure of the assignment or the course.
Academic Policies
All students are required to manage their class schedules each term
in accordance with the deadlines for enrolling and withdrawing as
indicated in the University Academic Calendar.
Information on enrollment, withdrawal, grading and incompletes can
be found at http://www.cdm.depaul.edu/Current%20Students/Pages/PoliciesandProcedures.aspx.
Students with Disabilities
Students who feel they may need an accommodation based on the impact
of a disability should contact the instructor privately to discuss
their specific needs. All discussions will remain confidential.
To ensure that you receive the most appropriate accommodation based
on your needs, contact the instructor as early as possible in the
quarter (preferably within the first week of class), and make sure
that you have contacted the Center for Students with Disabilities
(CSD) at:
Lewis Center
1420, 25 East Jackson Blvd.
Phone number: (312)362-8002
Fax: (312)362-6544
TTY: (773)325.7296