Syllabus
MATH 2530 Introductory Data Science, Ohio University, Fall 2022-23
Section 100 lecture (class number 12703) and section 101 recitation/laboratory (class number 12704).
- Instructor
- Martin J. Mohlenkamp, mohlenka@ohio.edu, Morton Hall 321C. The best ways to contact me are email or Teams.
- Office hours
- I will make sure to be available in my office Fridays 2:00-3:00pm to answer questions and otherwise help. I am happy to help you other times upon request.
- Web page
- https://data-ohio.github.io/MATH2530_Fall22-23/
- Class hours/ location
- Lecture 3:05 PM to 4:00 PM Monday, Wednesday, and Friday. Recitation 2:00 PM to 3:20 PM Tuesday. The “recitation” is really an 80-minute computer laboratory. All meetings are in Morton Hall room 314, which is a teaching computer lab.
- Text
- Introductory Data Science
- Computing Environment
- We will be using the Python programming language within Jupyter Notebooks, run in Ohio University’s JupyterHub.
- Lecture attendance
- Attendance counts for a small part of your grade. Your attendance record will be available in Blackboard.
- Homework
- There is a homework assignment due in most weeks. Late homework is penalized 10% per day (or part thereof) late. Your lowest two scores are dropped.
- Labs
- The Tuesday lab meeting will be used for (computer) laboratories, to be completed during that time. Missed labs cannot be made up, but your lowest two scores are dropped when computing your grade.
- Tests
- There will be 4 mid-term tests, one after each chapter in the textbook. Tests are cumulative.
- Final Exam
- The final exam is on Wednesday, December 7, 12:20-2:20pm.
- Grade
- Your grade is based on
- 5% Lecture attendance
- 20% labs
- 35% homework
- 25% mid-term tests
- 15% final exam.
An average of 90% guarantees you at least an A-, 80% a B-, 70% a C-, and 60% a D-.
- Academic (mis)conduct
- You are allowed to use most resources, but there are some limitations.
- Unlimited use, without specific acknowledgment
-
- The textbook.
- Discussions with me.
- Broad use, with acknowledgment
-
- Websites on statistics, data science, etc.
- Explanations by other students in this class.
- Explanations by friends, roommates etc.
Acknowledge and describe this help in writing on the problem where it was used. For example, you might write “[Name] explained to me how to do [some part] of this problem” or “I found an explanation of [concept] at the website [url]”.
- Forbidden
-
- The work or programs from students who took this class (in any of its versions at any university).
- Websites that claim to have solutions for this class.
- Direct copying.
If you are not sure if something is allowed, then ask me first.
A minor, first-time violation of this policy will receive a warning and discussion and clarification of the rules. Serious or second violations will result in a grade penalty on the assignment. Very serious or repeated violations will result in failure in the class and be reported to the Office of Community Standards and Student Responsibility, which may impose additional sanctions. You may appeal any sanctions through the grade appeal process.
- Special Needs
- If you have specific physical, psychiatric, or learning disabilities and require accommodations, please let me know as soon as possible so that your learning needs may be appropriately met. You should also register with Student Accessibility Services to obtain written documentation and to learn about the resources they have available.
- Responsible Employee Reporting Obligation
- If I learn of any instances of sexual harassment, sexual violence, and/or other forms of prohibited discrimination, I am required to report them. If you wish to share such information in confidence, then use the Office of Equity and Civil Rights Compliance.