Monday, Wednesday & Friday
Section 1: 8:00-9:00am RB 330
Section 3: 9:20-10:20am RB 330
Section 9: 1:20-2:20pm RB 350
Enter code SIENASCDV110BreimerFall2021
The cost to subscribe is $88 and subscriptions will be valid through Jan 22, 2021.Students will learn how to...
Letter grades will be assigned based on your numeric final average:
| A | >= 93.0 |
| A- | >= 90.0 |
| B+ | >= 87.0 |
| B | >= 83.0 |
| B- | >= 80.0 |
| C+ | >= 77.0 |
| C | >= 73.0 |
| C- | >= 70.0 |
| D+ | >= 67.0 |
| D | >= 63.0 |
| D- | >= 60.0 |
| F | < 60.0 |
Final grades will be based on the following weights:
| 15% | zyBook Reading/Activities |
| 15% | zyLab Programming Assignments |
| 20% | Google Colab Assignments |
| 15% | Early Concept Midterm |
| 15% | Practical Programming Exam |
| 20% | Final Exam: Cummulative, During Finals Week |
A student is expected to attend every lecture in-person, arrive on time and stay for the full period. It is the student's responsibility to be aware of this policy. Lecture will not be available on Zoom and will not be recorded unless Siena College suspends in-person classes. While Zoom may be setup for students who are officially not allowed to participate in-person, students without an official excuse must attend in-person.
Students can lose up to 40% on their final average leading to automatic failure for lack of participation, lateness, absence or disruption during lecture.
Students will be given two warnings if they are late to lecture. After the two warnings, any subsequent lateness will be considered an absence and the penalties below will be incurred.
Students can have two unexcused lecture absences without any penalty. But after two absences, students will be penalized as follows:
| 3 unexcused lecture absences | 2% penalty on final average |
| 4 unexcused lecture absences | 6% penalty on final average |
| 5 unexcused lecture absences | 12% penalty on final average |
| 6 unexcused lecture absences | 20% penalty on final average |
| 7 unexcused lecture absences | 30% penalty on final average |
| 8 unexcused lecture absences | Automatic failure |
Students will read select chapters in the zyBook and complete the online activities integrated into the book. See the course schedule for the due dates. Students are required to purchase the zyBook. If you cannot purchase the zyBook, you should contact your instructor immediately to resolve the situation.
Throughout the semester there will be 7-9 zyLab programming assignments. See the course schedule for the due dates. zyLabs will often be introduced in lecture and hints will be given. However, students must complete the labs outside of lecture time.
Throughout the semester there will be 7-9 Google Colab assignments that will require you to write code, answer questions and eventually create data visualizations. Google Colab is a cloud-based platform for writing and sharing Python code with integrated content and visualizations. See the course schedule for the due dates
See the course schedule for dates
During final exam week, date will be announced before midterms.
The instructor makes the final decision to excuse or not to excuse an absence. If you are concerned that an absence will not be excused, you should contact the instructor as soon as possible. The following guidelines will be used to make decisions.
Students can be excused (and not penalized) from lecture for illnesses, job interviews, and serious commitments such as athletic or academic trips/competitions. However, students must inform the instructor as soon as possible, provide proof/documentation, and take responsibility to acquire notes and information from other students.
During an exam period, students cannot share information, look at each other's tests, or use unauthorized materials. Using an electronic device and the Internet to search for answers is explicitly prohibited. The instructor will inform students in advance if the textbook or a cheatsheet can be used for a particular exams. Typically, a cheatsheet (one page, two sides of notes) will be permitted for Exam 1 and textbook usage will be permitted for Exam 2 and the Final. Students caught cheating on an exam, will receive a zero on the exam and will be penalized a full letter-grade in the course.
It is very easy to copy Python code from classmates or other sources and claim it as your own. This is academically dishonest and considered plagiarism. Students who present other authors' code, documents, images, or designs as their own will receive a grade of zero on the entire project or lab. Students caught cheating on a homework, will receive a zero on the homework and will be penalized a full letter-grade in the course.
Exception: In data science, it is considered professionally acceptable to use open source code and data as long as such usage is documented by giving the original author credit in any newly created work. Documenting sources should be done by using citations and/or comments in source files. Note that it is very important to cite your sources before you submit your work.
A student caught cheating a 2nd time (exam or homework) will automatically fail the course and a letter describing the student's violation will be sent to Siena's Vice President of Academic Affairs. Note that Siena has expelled students for repeat academic integrity violations in multiple courses.
Carefully read the following academic integrity guidelines. It is your responsibility to follow the following guidelines:
In this course, integrating code from open sources is considered an acceptable practice as long as the integration is non-trivial and leads to a finished work that is significantly different when compared to the original open/public sources. Students will not be penalized for using other authors' code as long as the source is cited and as long as the code comes from an open source or public domain. In lecture, the instructor will teach students strategies for identifying open and public domain sources vs. protected, commercial and copyrighted sources.
While it is natural for students to help each other, students retain more knowledge if they attempt to write and debug code on their own. It is acceptable for students to help each other understand general concepts, but students are prohibited from sharing their code. And, students should never write code for other students. The only exception is when students are working with instructor-designated partners for group homework assignments.
It is appropriate to ask for or provide help solving a coding problem as long as it is done in a general or abstract way. Appropriate examples include: helping a peer understand an error message, sharing debugging strategies, or explaining a concept related to a specific problem. But, it is inappropriate to have any other students (including tutors) solve your problems directly. Seeking excessive help is a form of cheating. Inappropriate help includes: Asking a peer or tutor to write code for you, looking at another student's working solution, or receiving excessive (step-by-step) help in directly completing individual work.
Integrating code from multiple sources into a new unique finished deliverable often requires great effort to get all the part to work together properly. However, it is important that you can point to the parts of your code that you wrote yourself and the parts taken from other sources. If a student cannot explain the purpose, function, and details of the code that they claim to have written themselves, the code will be considered plagiarized.
An important goal in this course is for students to learn strategies for becoming more independent with respect to problem solving, coding, and debugging. Towards end of the course, students should not need excessive help from classmates, tutors, or the instructor. Requiring excessive help toward the end of this course is an indication of poor performance and students will be penalized if they cannot complete work independently.
As per Siena's official policy, students required to wear a mask who do not comply will be asked to leave lecture and absence penalties will be incurred. A Zoom option will NOT be provided to students who must leave class due to non-compliance. Vaccinated students are highly encouraged to wear a mask, especially in the first few weeks of the semester.