| Week (Week of) | Tuesday | Thursday | Due This Week |
|---|---|---|---|
| 1 (Aug. 32) |
Intro to Class Download Anaconda & VScode See Appendix A & B of text book for more instructions |
Setup Environment. See Appendix A & B & C in text book. |
N/A |
| 2 (Sept. 7) |
Read Chap. 1 Working with Variables | Variables, continued | Variables, continued |
| 3 (Sept. 14) |
Read Chap. 2: Flow Control | Continue Flow Control | Week 3 Homework (Sept. 20) |
| 4 (Sept. 22) |
Read Chap. 3: Loops | Read Chap. 4: Functions | Week 4 Homework (Sept. 27) |
| 5 (Sept. 29) |
Chap. 6 DataFrame Basics | Ch. 6, cont.: Reading/Writing Data | Week 5 Homework (Oct. 4) |
| 6 (Oct. 6) |
Chap. 8 Advanced Data frames | Advanced DataFrames, cont. | Week 6 Homework (Oct. 11) & Project: Dataset Question (Oct. 11) |
| 7 (Oct. 13) |
Read Ch. 8 Univariate Statistics | Univariate Statistics, cont. | N/A |
| 8 (Oct. 20) |
Read Chap. 9 Univariate Visualization | Univariate Visualization, cont. | Week 8 Homework (Oct. 25) |
| 9 (Oct. 27) |
Read Chap. 10: Bivariate N2N Statistics | Bivariate N2N Statistics, cont. | Project: Basic Statistics due Sun (Nov. 1) |
| 10 (Nov. 3) |
Read Chap. 11 Bivariate N2N Visualization | Bivariate N2N Visualization, cont. | Week 10 Homework (Nov. 8) |
| 11 (Nov. 10) |
Read Chap. 12 Bivariate C2N Statistics | Bivariate C2N Statistics, cont. | N/A |
| 12 (Nov. 17) |
Chap. 13 Bivariate C2N Visualization | Bivariate C2N Visualization, cont. | Week 12 Homework (Nov. 22) |
| 13 (Nov. 24) |
No Class Thanksgiving |
No Class Thanksgiving |
N/A |
| 14 (Dec. 1) |
Aligning Stats to Problems | Designing useful Visualizations | Project:Variable Analysis and Visualizations due Tuesday (Dec. 6) |
| 15 (Dec. 8) |
Solving problems | Workshop Day | Work On Your Project |
| 16 | N/A | N/A | Final Project & Process Paper Due Dec. 17 |
Dr. Nathan Michalewicz
| Class | Office Hours |
|---|---|
| Tues. / Thur. | Mon. / Wed. |
| 9:55 AM — 11:40 AM | 8:30 AM — 10:30 AM |
| or by appointment | |
| Dana 312 | Watkins 207 |
Course Description
This is a high-impact course introducing computer programming as a critical 21st-century technical literacy and as the “glue” that connects powerful data-driven technologies to form solutions for real-world challenges in finance, biotech, political science, and robotics. The course will adopt the open-source Python programming language and provide an overview of machine learning and its algorithms. No experience is needed. Prerequisite: None. (Offered every term.) Credits: 4
Course Outcomes
Our Goals for This Course:
- Understand the fundamentals of computer programming and Python,
- Be able to answer questions about the world using data.
We Will Get There By:
- Understanding the data types and structures in Python,
- Executing loops and conditional statements in Python,
- Reading documentation to learn how to use Python libraries,
- Loading, analyzing, and exporting data using Python,
- Identifying and fixing errors in raw datasets before beginning analysis,
- Writing Python scripts to represent numerical and categorical data visually in a way that is easy to understand.
- Learning how to use Python libraries to perform statistical analysis and visualizations of data.
Required Texts
This course will use the following online textbook: Applying Python to Data Analytics: An Introduction. It is free. References to a chapter in the course outline below refer to the chapters in this book. There will also be additional videos available as links in the course outline as well. In such cases, they are intended as supplementary material to the textbook and not to be watched in lieu of reading the textbook.
You will also need to download three software packages:
- uv — an extremely fast project management tool and package manager for Python.
- Visual Studio Code - an integrated development environment that supports Python.
- Git - a version control system to manage your code.
Assignments
| Assignment | Weight (%) |
|---|---|
| Engagement | 15% |
| Weekly Homework | 35% |
| Project: Hello World | 1% |
| Project: Dataset & Question | 5% |
| Project: Basic Statistics | 5% |
| Project: Variable Analysis & Visualizations | 15% |
| Project: Process Paper | 5% |
| Project: Final Project | 20% |
Grading
Queens uses a plus/minus grade scale: B- receives less than 3 points, for instance. Grades in this class will follow the same pattern with one exception. Here are the numeric grades associated with each letter grade:
A = 90-100 B+ = 89-87 B = 86-83 B- = 82-80 |
C+ = 79-77 C = 76-73 C- = 72-70 |
D+ = 69-67 D = 66-60 F = 59-0 |
Notice a couple inconsistencies. One, I do not give an A-. In my experience, if you get an A, you deserved it without qualification. Also, there is not a D-. This difference is because Queens does not include a D-.
Late-Work Policy
This policy excludes the final assignment(s) (due after the final day of class) because I cannot provide an extension outside of extreme situations due to limits on when I have to turn in grades. Otherwise, see below.
Each student receives one (1) free extension for the course. If you request an extension 24 hours BEFORE the due date via email, I will provide a no-questions-asked (no excuse needed) 48-hour extension for an assignment. If the assignment is turned in after the 48-hour extension, the assignment will be deducted 10 points. Longer extensions are available only at the professor’s discretion on a case-by-case basis. If an extension is not requested 24 hours before the due date, the assignment will be deducted 10 points if the assignment is turned in late up to 6 days after the due date. If it is turned in seven (7) days after the deadline or later, the grade will be deducted twenty (20) points.
Work turned in 14+ days late
I will NOT accept any worker later than fourteen (14) days after the due date. Exceptions to this policy can be made at the discretion of the professor based on the student’s excuse.
Example:
If an assignment is due February 1, it will be reduced by 10 points if it is turned in (without an extension) between February 2 and February 7. It will be reduced 20 points if it is turned it between February 8 and February 14. It will not be accepted after February 14.
Honor Code
The Honor Code, which permeates all phases of university life, is based on three fundamental principles. It assumes that Queens students: a) are truthful at all times, b) respect the property of others, and c) are honest in tests, examinations, term papers, and all other academic assignments. Please contact the Instructor if you believe a violation of the Honor Code has occurred. It is a violation of the Honor Code for a student to be untruthful concerning the reason for a class absence. See The Honor Code Book for more information on the process in the event of a suspected violation.
Every student is expected to produce their own work based on their own ideas and cite anyone else’s ideas or words appropriately. Certain material that an average person would consider common knowledge does not need to be cited. Such information would include that Christopher Columbus sailed across the Atlantic Ocean in 1492 or that the American Revolution began in 1776. Other information, however, needs to be cited if it did not originate in your mind. See the Queens Library’s page on plagiarism: https://library.queens.edu/plagiarism/.
If you are suspected of violating the honor code, you will be reported to the honor council. If you are found guilty of the violation, you will receive a 0 on the assignment.
Queens AI Policy
The use of AI Tools is prohibited for coursework unless explicitly communicated by the course instructor.
If the instructor has given explicit direction(s) that AI Tools may be used (such as but not limited to ChatGPT, QuillBot, DALL-E, or Bard), the material produced must be acknowledged with a citation by the student for any assignment for which it was used. Their use should be limited so that they do not interfere with the student learning objectives for the assignment and the course.
My AI Policy
All submissions should reflect your work. Any use of AI to generate assignments is prohibited. If I suspect that you have used AI to generate part or all of your assignment, I will submit the assignment to the honor council for review. If you are found guilty of violating the honor code, and it is your first incident in the class, you will receive a 0 on the assignment. If it is the second incident, you will fail the class.
Student Accessibility Services
Queens University of Charlotte is committed to making reasonable accommodations to assist students with disabilities. If you have a disability which may impact your performance, attendance or grades in this course and require accommodations, you must first contact Student Accessibility Services at sashelp@queens.edu. The steps for receiving accommodations must be completed before accommodations can be given. The steps are available on the Student Accessibility Homepage. SAS is responsible for coordinating classroom accommodations and other services for students with disabilities. Please note that students are responsible for sharing their letter of accommodation with their instructors to receive classroom accommodations.
Student Complaint Process
Queens University of Charlotte is committed to providing an educational climate that is conducive to the personal and professional development of each individual. To ensure that commitment, the university has developed procedures for students to pursue grievances within the university community should such action become necessary. A student who has an unresolved disagreement or dissatisfaction with a faculty or staff member, another student, a student group, or an administrator has the right to file a written complaint without prejudicing his or her status with the university. For more information, please visit the Student Complaint Process page. For information regarding the online student complaint process, please visit the online student complaint process page.
QAlert
QAlert is the emergency notification system Queens uses to notify the campus community of an emergency, inclement weather, or class cancellations. It sends messages about the status of a given situation, as well as other details the campus needs to know. Students, faculty, and staff are automatically registered for QAlert through the university’s enterprise resource.
Religious Holidays
If any assignments or due dates interfere with your personal religious observation, I will be happy to make accommodations. Remember, within the first two weeks of the semester, the student must let me know the dates of major religious holidays on which the student will be absent or unavailable due to religious observances. Please, see the Queens Office for Equity, Diversity, and Inclusion religious holiday calendar:
E-mail Communication
When writing emails to me, be sure to include a subject line, address me properly by my title and last name, and sign off with your name. Proper email formatting skills are required in post-university life.
Each student is issued a University e-mail address (username@queens.edu) upon admittance. The University uses this e-mail address for official communication with students. Students are expected to read e-mails sent to this account on a regular basis. Failure to read and react to University communications in a timely manner does not absolve the student from knowing and complying with the content of the communications.