For students interested in algorithmic problem solving in a variety of settings. No experience is required. Students will learn to solve problems using a variety of coding styles while becoming familiar with the Python Standard Library and its essential third-party packages. The focus is on building skills that students can transfer to other settings, including data science and analytics.
Meeting Times, Location, & Course Delivery Details
Contact Information
- Textbook: Introduction to Python Programming by OpenStax.
ISBN-13: 978-1-961584-45-7
Author: Udayan Das
Publisher: OpenStax - Alternative Textbook: Python for Everybody, Exploring Data Using Python 3
ISBN-13: 9781530051120
Author: Charles R. Severance
Publisher: Charles R. Severance - Website: SQLBolt
- An interactive online resource that will be used to introduce and practice fundamental SQL concepts and queries.
The purpose is to provide students with a foundational understanding of programming concepts and practices using Python and SQL. This course aims to equip students with essential problem-solving skills applicable across various domains, including data science and analytics. Through hands-on experience with Python's Standard Library and essential third-party packages, students will develop the ability to write, debug, and optimize code while exploring diverse coding styles. The objective is to foster a comprehensive understanding of algorithmic thinking and programming fundamentals, enabling students to tackle real-world problems effectively and prepare for more advanced studies or professional applications in computer science.
Tentative Schedule:
Week | Dates | Topics / Sections Covered | Homework |
|---|---|---|---|
| 1 | Aug 24–28 | §§1.1–1.8 | |
| 2 | Aug 31– Sep 4 | §§2.1–2.7 | HW1 assigned |
| 3 | Sep 7–11 (Mon, Sep 7 Laber Day) | §§3.1–3.5 | |
| 4 | Sep 14–18 | §§4.1–4.7 | HW2 assigned |
| 5 | Sep 21–25 | §§5.1–5.5 | |
| 6 | Sep 28–Oct 2 | §§6.1–6.6 | HW3 assigned |
| 7 | Oct 5–9 | §§7.1–7.5 | |
| 8 | Oct 12–16 (Tue, Oct 13 No classes) | Test 1 (Chapter 1-6), Thursday, Oct 15 | |
| 9 | Oct 19–23 (Mon, Oct 19 Fall Break) | §§8.1–8.5 | HW4 assigned |
| 10 | Oct 26–30 | §§9.1–9.5 | |
| 11 | Nov 2–6 | §§10.1–10.5 (Skip chapter 11) | HW5 assigned |
| 12 | Nov 9–13 | §§12.1–12.6 (Skip chapter 13) | |
| 13 | Nov 16–20 | §§14.1–14.5 (Skip chapter 15) | HW6 assigned |
| 14 | Nov 23–27 (Nov 25-27 Thanksgiving Holiday) | Test 2 (Chapter 7-14), Tuesday Nov 24 | |
| 15 | Nov 30–Dec 4 | SQL | |
| 16 | Dec 7–9 Last Day of Classes | SQL | HW7 assigned |
| — | Tuesday, Dec 15 (8:00–10:00 AM) | No Final Exam |
- Subject to change during the semester.
Homework assignments play a crucial role in reinforcing concepts learned in class and promoting individual understanding. In this course, the following policies govern homework submissions and assessments:
- Timely Submission: All homework assignments are expected to be submitted by the specified deadline. Late submissions may result in a deduction of points, with the severity of the penalty increasing the longer the delay.
- Minimum Homework Score: The lowest homework score will be dropped from the total, which allows for flexibility and accommodates any challenges you may face.
- Quality and Originality: Homework solutions should reflect individual effort and understanding. Plagiarism or copying from external sources is strictly prohibited and will result in academic consequences.
- Clarity and Organization: Clear presentation of solutions and organized work are essential. Use proper formatting, labeling, and explanations to ensure that your responses are easily understandable.
- Collaboration: Unless explicitly stated otherwise, homework assignments are to be completed individually. Unauthorized collaboration may lead to academic penalties.
- Grading and Feedback: Assignments will be graded based on correctness, completeness, and adherence to instructions. Constructive feedback will be provided to aid in your understanding and improvement.
- Resubmission: In certain cases, resubmission of corrected assignments may be allowed after receiving feedback. However, this is at the discretion of the instructor and may be subject to specific guidelines.
EXTRA CREDIT: Occasionally, I may offer extra credit opportunities in addition to regular homework assignments. Each extra credit task completed will earn you one extra point, which will be added to your final grade average at the end of the term.
EVALUATION:
- Homework 25%
- Quizzes 15%
- Test 1 25%
- Test 2 25%
- Class Participation 10%
GRADING SCALE:
- A 95-100% A- 90-94% B+ 87-89%
- B 83-86% B- 80-82% C+ 77-79%
- C 73-76% C- 70-72% D+ 67-69%
- D 60-66% F < 60%
Instructor Course Policies
More than three unexcused absences will significantly impact your grade, and excessive absences may lower it naturally. It is essential to communicate in advance about any absence, providing a valid reason and documentation for excused absences.
Please submit all documentation related to absences on this form.
Documentation Submission for Excused Absences – Fill out form
Upholding academic integrity is paramount in this course, with severe consequences for violations. Plagiarism, cheating, and unauthorized collaboration can lead to failing grades for assignments or exams and referral for judicial review. Quizzes and exams require students to show their work for full credit, emphasizing clarity in expressing calculator processes if used extensively. Cell phone use, including texting, is strictly prohibited. Familiarizing yourself with the current Student Handbook is crucial for understanding academic integrity policies, examination procedures, and the attendance policy, especially regarding excused absences, classroom behavior, and the process for handling academic misconduct charges. Adhering to these policies ensures a fair and enriching educational experience for all.
Material To Be Learned
In this course, students will delve into the foundational concepts of programming using Python. The course will cover essential topics such as basic syntax, control structures, and data types, providing a solid grounding in coding principles. Students will explore functions, modules, and object-oriented programming to understand how to structure and organize code effectively. Additionally, the course will introduce data structures like lists, dictionaries, and sets, and emphasize practical skills in file handling and error management. Through the use of Python’s Standard Library and third-party packages, including libraries for data analysis like NumPy and pandas, students will gain hands-on experience with real-world applications. The course will culminate in a project that allows students to apply their programming skills to solve complex problems and present their solutions, preparing them for further studies in data science and analytics.
Course Workload and Expectations
To support your success in this course, please be aware that a minimum of two hours of out-of-class student work is expected for each hour of in-class time. This means that for every hour spent in class, you should plan to dedicate approximately two additional hours outside of class to complete assignments, study, and engage with course materials. This expectation ensures that you have ample time to grasp the concepts, complete homework, and prepare for assessments.
Catalog Description
For students interested in algorithmic problem-solving in a variety of settings. No experience is required. Students will learn to solve problems using a variety of coding styles while becoming familiar with the Python Standard Library and its essential third-party packages. The focus is on building skills that students can transfer to other settings, including data science and analytics.
Learning Outcome
- Students can define and use variables and functions, including types and pass-by-reference vs pass-by-value behaviors, optional and keyword arguments, and so on
- Students are familiar with standard data types, including integer, floating point, string, and boolean types
- Students are familiar with language-specific types, such as lists, arrays, matrices, dicts, tuples, etc.
- Students can define and use custom objects such as classes, structs, etc.
- Students are familiar with standard practices, such as typecase conventions, package management, etc.
- Students can use standard control flow to structure larger programs
- Students can use standard and supplemental debugging tools to find and fix problems
- Students can implement test-driven design patterns
- Students are familiar with the language’s standard library and essential third-party packages, and know how to find and install such packages
- Students can use the information and skills listed above to produce applications of varying complexity, including but not limited to simple data analysis
Skills
This course equips students with fundamental skills in algorithmic problem-solving across various contexts. Beginning with no prior experience required, students will learn to tackle problems using diverse coding approaches and gain proficiency in the Python Standard Library alongside key third-party packages. Emphasis will be placed on developing versatile skills applicable to a range of fields, including data science and analytics, ensuring that students can effectively transfer their problem-solving abilities to other practical applications.
Institutional and Program-Level Policies
All exams will follow the Final Exam Schedule. Students scheduled to take three or more final examinations on one day may request to arrange their examination schedule, so no more than two exams occur on one day.
Requests for early or late exams are considered only under extreme circumstances. Prior to the exam period, the student must file a written request on the Early/Late Exam Form available in the Student One Stop Center, Academic Records, and on the WC portal. The form must be signed by the Instructor and the Academic Dean, approving the alternate exam time. This process must be completed prior to the scheduled exam period.
Undergraduate: FA26 Final Exam Schedule
Out-of-class Work Expectation A minimum of 2 hours of out-of-class student work is expected for each hour of in-class time for traditional face-to-face courses. For online and hybrid courses, the combination of face-to-face time and out-of-class work should be equal to 3 hours per credit hour per week. |
Instructional Course Delivery Definition of Courses |
Academic Integrity Policy The use of generative AI is prohibited except where expressly allowed in assignment instructions. Academic Integrity Policy |
Class Attendance Policy Institutional Class Attendance Policy |