zyBook: Intro to Exploratory Data Analysis & Visualization Signup Here
Enter code SIENASCDV-110BreimerFall2018
The cost to subscribe is $40 and subscriptions will be valid through Jan 04, 2019.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:
| up to -40% | Penalty for lecture absences/lateness |
| 10% | zyBook Reading/Activities |
| 15% | Homeworks |
| 15% | Final Project |
| 15% | Exam 1: Early Concepts |
| 15% | Exam 2: Midterm |
| 15% | Exam 3: Practical Programming |
| 15% | Exam 4: During Finals Week |
A student is expected to attend every lecture, arrive on time and stay for the full period. It is the student's responsibility to be aware of this policy.
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 | 5% penalty on final average |
| 5 unexcused lecture absences | 10% penalty on final average |
| 6 unexcused lecture absences | 15% penalty on final average |
| 7 unexcused lecture absences | 20% penalty on final average |
| 8 unexcused lecture absences | 25% penalty on final average |
| 9 unexcused lecture absences | 30% penalty on final average |
| 10 unexcused lecture absences | Automatic failure |
Each week, students will have to read select chapters in the zyBook and complete the online activities that are integrated into the chapters. See the course schedule for the due dates on assigned reading/activities. Students are required to purchase the zyBook. If you cannot purchase the zyBook, you should contact your instructor immediately to resolve the situation. Students can lose up to 1% each week (10% total for the semester) for failing to complete the assigned reading and online questions/activities.
There will be 7-10 homework assignments due during the semester. These assignments will involve Python programming. Because of debugging challenges and the need to read documentation, some of these homeworks can take up to 8 hours to complete outside of lecture, so do not wait until the due date to start. Due dates will be announced and put on the course schedule.
Unless a student has a serious issue that is brought to the attention of the instructor in advance, late homeworks will not be accepted and will be given a grade of zero. Thus, it is important that you submit work prior to the deadline to get credit. Homework will be submitted though Canvas or email. Submission instructions will be given in the homework description.
In the last three weeks of the course, student will complete a final project that will require them to pose a data science problem/question, collect/find data, process the data into meaningful information, and then produce an infographic or non-trivial visualization the helps address the problem or answer the question. This project will require research and python programming.
Multiple choice, fill-in, and open-ended questions about concepts and fundamental math covered in the first 4 weeks of the semester.
Multiple choice, fill-in, and open-ended questions about concepts and fundamental math covered in the first 8 weeks of the semester.
Taken on a computer and submitted via Canvas or email, this exam will require students to solve problems via programming.
Multiple choice, fill-in, and open-ended questions about concepts and fundamental math covered in the last 8 weeks of the semester.
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.
Students caught cheating on an exam, will receive a zero on the exam, will be penalized a full letter-grade in the course, and a letter describing the student's actions will be sent to Siena's Vice President of Academic Affairs. During an exam period, students cannot share information, look at each other's tests, or use unauthorized materials.
Unless explicit permission is given, assume that exams are closed-book/closed-notes and that cheat sheets and electronic devices are prohibited.
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 who commit plagiarism a second time will again receive a zero, but will also be penalized a full letter-grade in the course and a letter describing the student's violation will be sent to Siena's Vice President of Academic Affairs.
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.
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 web page, site or application 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 lab partners on lab work and project partners on group project work.
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 web page, site or application 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 even 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 labs independently.