Instructor: Todd Dole
Semester: Fall 2024
Course Type: Online and Asynchronous
First Day of Class: August 26
Contact: todd.dole@hsutx.edu, Phone 325-670-1502, Office AH100
Note that since this is an online course, Mr. Dole can meet via Microsoft Teams, Zoom, or Discord if coming to office hours in person is not a possibility.
With student permission, Q&A sessions may be recorded and uploaded for other students to benefit from.
Monday and Wednesday: 11:00 AM - 12:00 PM, 2:00 PM - 4:00 PM
Tuesday and Thursday: 2:45 PM - 4:45 PM
This course introduces students to programming in Python. Students will complete weekly programming assignments, which will be due the following week on Friday at midnight. There will be two exams and a final exam. The course will follow the structure of the required textbook, "Python Crash Course" by Eric Matthes, with weekly readings assigned.
Note that some course videos will include general introduction to programming concepts and techniques. For students who have previously completed CSCI1320/2320, or with previous programming experience, these videos are optional.
New Course Material will be posted each Monday. Programming challenges will be assigned weekly, and due the following week (Friday at midnight).
Required Textbook: Python Crash Course Third Edition by Eric Matthes, available on Cowboy Access
Weekly readings will be assigned from this textbook. We will cover some of the book material in lectures, but not all. You will be responsible to know all information from the book.
Exercises in the book are optional.
| Week | Date | Topic | Reading/Assignment |
|---|---|---|---|
| 1 | Week of August 26 | Introduction to the Course. Introduction to Python. IDE Options. Basic Python Syntax. Intro to Jupyter. |
Read Chapter 1, Appendix A and B |
| 2 | Week of September 2 | Variables and Simple Data Types. Assert Statement. | Read Chapter 2, Assignment 1 due |
| 3 | Week of September 9 | Introducing Lists | Read Chapter 3, Assignment 2 Due |
| 4 | Week of September 16 | Working with Lists. Imports. Introduction to Pyplot | Read Chapter 4, Assignment 3 Due |
| 5 | Week of September 23 | If Statements | Read Chapter 5, Assignment 4 due |
| 6 | Week of September 30 | Dictionaries | Exam 1. Read Chapter 6 |
| 7 | Week of October 7 | User Input,While Loops | Read Chapter 7, Assignment 5 due (due Thursday at midnight) |
| 8 | Week of October 14 | Functions | Read Chapter 8, Assignment 6 due |
| 9 | Week of October 21 | Classes | Read Chapter 9, Assignment 7 due |
| 10 | Week of October 28 | Files and Exceptions | Read Chapter 10, Assignment 8 due |
| 11 | Week of November 4 | Consuming API's, using JSON | Assignment 9 due |
| 12 | Week of November 11 | Introduction to Numpy | Exam 2 |
| 13 | Week of November 18 | Introduction to Pandas DataFrames | Assignment 10 due |
| 14 | Week of November 25 | Numpy and Pandas Continued | |
| 15 | Week of Dec 2 | Advanced Lists and DataFrames | Assignment 11 due |
| 16 | Week of Dec 9 | Final Exam |
Your grade in the course will be earned / calculated as follows:
A: 90-100
B: 80-89
C: 70-79
D: 60-69
F: 0-59
All exams are comprehensive. The final exam will take place at the scheduled time during finals week. Exams will never be collaborative in nature, so receiving any form of assistance from anyone other than the instructor is a violation of the academic integrity policy. You may only use study aids during the exam if they are expressly allowed by the instructor for that particular exam.
An individual with a disability is defined by the Americans with Disabilities Act (ADA) as a “person who has a physical or maaental impairment that substantially limits one or more major life activities.” Any student with a documented disability may choose to seek accommodations. Eligible students seeking accommodation should contact the Director of Undergraduate Advising and Disabilities as soon as possible in the academic term (preferably during the first two weeks of a long semester) for which they are seeking accommodations. The director will prepare letters to appropriate faculty members concerning specific, reasonable academic adjustments for the student. The student is responsible for delivering accommodation letters and conferring with faculty members. Please refer to the most recent version of the Undergraduate Catalog for the complete policy. (Carol Krueger, Director of Undergraduate Advising and Disabilities, Office: Sandefer Memorial, 1st floor Academic Advising Center, Phone: 670-5867, Email: disabilityservices@hsutx.edu)
Peer-to-peer academic support (tutoring) is available for all undergraduate HSU students. The Academic Center for Enrichment (ACE) is open for virtual tutoring sessions via Zoom. To access instructions or make an appointment, open the ACE course on your Canvas dashboard. For additional information regarding academic support, contact the Advising Center at 325-670-1480 or tutoring@hsutx.edu.
In addition, all full or part-time students are eligible to receive free, confidential, and voluntary counseling services at HSU. Services include consultation, evaluation, counseling, and crisis support services for students facing issues impacting their overall well-being. To obtain any of these services, students may call The Office of Counseling Services at (325) 671-2272, email counseling@hsutx.edu, or begin the intake process by completing our online forms at https://www.hsutx.edu/intake.
Violations of academic integrity have been described to some degree in other sections of this syllabus. Cases of suspected academic dishonesty will be handled in accordance with university policies outlined in the Undergraduate Catalog and in the Student Handbook. The current catalog prescribes that “no student who has violated the Academic Integrity Policy will be allowed to graduate from Hardin-Simmons University with honors.” Penalties will be assigned at the discretion of the instructor and typically range from failure on the assignment to failure of the course. A general rule-of-thumb is that a first offense (if not too major) will result in a zero on the assignment and a second offense will result in an F for the course. The current catalog states that an F earned in this way cannot be replaced by retaking the course.
There is an acceptable time and place to use large language models such as ChatGPT or Microsoft Copilot. These tools may be used to help you learn, to answer questions you have about the algorithms or data structures we will cover, or to give you very broad help on lab assignments. However, it is never acceptable in this course to turn in work generated by AI, except for specific assignments which will be clearly designated by the instructor. This course is foundational to a career in the world of computer science. The goal of all lab assignments is for you to learn the material and skill necessary to succeed in the field. These are skills that in many cases you may have to demonstrate from memory in job interviews. Do not cut take shortcuts by having AI do the work for you. Students deemed to have turned in work generated by AI will be in violation of the Academic Integrity provisions listed above, with the same penalties.
The instructor may occasionally use email to communicate with the class as a whole or with individuals. When contacting you for this course the instructor will use your HSU email account. You are expected to check your HSU email account at least once per day and you will be held responsible for any content distributed in this way.
Because this is an online, asynchronous course, attendance will not be taken.