Syllabus¶
Course website: |
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Instructors: |
Katie Keith (TCL 304) and Stephen Freund (TPL 302) |
Staff Email: |
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Lectures: |
MWF 10:00-10:50am (Katie) and 11:00-11:50am (Steve) in Schow 030B |
Labs: |
MT 1:00-2:30 and 2:30-4:00 in Schow 027 |
Course Description¶
Many of the world’s greatest discoveries and most consequential decisions are enabled or informed by the analysis of data from a myriad of sources. Indeed, the ability to organize, visualize, and draw conclusions from data is now a critical tool in the sciences, business, medicine, politics, other academic disciplines, and society as a whole. This course lays the foundations for reasoning about data by exploring complementary computational, statistical, and visualization concepts. These concepts will be reinforced by lab experiences designed to teach programming and statistics skills while analyzing real-world data sets. This course will also examine the broader context and social issues surrounding data analysis, including privacy and ethics.
Learning Outcomes¶
Leverage the combination of computer science and statistics.
Come away with practical data science and Python programming skills applicable to any domain.
Be able to run experiments, test hypotheses, and draw inferences.
Quantify and understand uncertainty in data.
Illustrate the above concepts with real-world data from a variety of domains.
Organization¶
We will meet three times each week for lecture, and once a week for lab. During lecture hours we will learn new concepts and problem solving strategies. During the 90-minute lab section, we will gain hands-on experience with the concepts through programming assignments.
Course Components¶
Lectures¶
There are two lecture sections. Please attend the section that you are registered for.
Labs¶
Each week, you will work on a lab assignment covering the topics from lecture. These labs will involve programming in the Python programming language. Our computing platform where you will complete your work is accessible via a web browser from any computer. We will provide computers to use during scheduled lab meetings. These will be available outside of lab hours for you to use as well. You may also use your own computer.
We will post labs on Friday so that you may look them over before our scheduled lab meetings on Mondays and Tuesdays. Labs are due:
Wednesday at 10pm for the Monday lab groups.
Thursday at 10pm for the Tuesday lab groups.
Late Days for Labs¶
Each student may use a maximum of three late days during the course of the semester. A single late day enables you to hand in a lab up to 24 hours after the original due date. Once those late days are used up, late homework will be penalized 20% per day.
Prelabs and Case Studies¶
We’ll also assign short prelab exercises to help you prepare for the labs. We will post prelabs on Wednesday, and you will complete them by Monday morning. Prelabs are designed to help you prepare to have a productive scheduled lab session. Any score above 80% will receive full credit on each prelab. You may not use late days for prelabs.
You will also read and complete several short case studies during the semester. These studies relate the technical material to ethical and privacy questions surrounding the analysis of data.
You may not use late days for prelabs or case studies.
Midterm Exam¶
The exam will be on the evening of Thursday, Nov 3. The exam will be closed book, closed notes, and will focus on conceptual understanding of the material. Details regarding the specific format of the exam will be discussed in class.
Projects¶
You will have two larger projects in the course. The first project will be due on Thursday, Oct. 27. The second will be a final project synthesizing the concepts and skills from the entire semester. It will be due on Wednesday, Dec 16.
Grading¶
Final grades will reflect the course components as follows.
Component |
Weight |
|---|---|
Labs |
40% |
Prelabs and Case Studies |
10% |
Midterm Exam |
10% |
Midterm Project |
15% |
Final Project |
25% |
Workload¶
Attendance is required in both lecture and lab. In general, beyond the 4 hours we spend together during our class and lab meetings, you should expect to spend (on average) approximately 7-8 hours per week on work related to class. Aside from the weekly lab and prelab assignments, you are responsible for reading supporting material and investigating our online resources as necessary.
Expectations and Norms¶
You can expect us (the instructors) to:
Contribute to and support a respectful and welcoming environment.
Start and end class on time.
Craft lectures and assignments designed to help you learn the material.
Reply to emails within 24 hours on weekdays and 48 hours on weekends.
We can expect you (the students) to:
Contribute to and support a respectful and welcoming environment.
Attend all lecture and lab sessions in person except for health emergencies or extenuating circumstances.
Arrive to class and lab on time, and plan to stay until the end.
Stay engaged in the class and material.
Reach out for help from the TAs or instructors.
Adhere to the Honor Code.
Class Norms:
If you become sick with COVID or another illness please stay home and let us know via email.
If must miss class for other reasons please give us as much advance notice as possible.
The Computer Science department strives to be a friendly and welcoming community. You may find it slightly less formal (but no less respectful) than what you encountered in previous academic settings. For example, most students and faculty address us by our first names (i.e. Katie and Steve). You are welcome to do so as well.
You are also welcome to address us informally in email (i.e. starting an email with “Hi Katie.”) Here are a few other tips for emailing professors if that is something new to you out of your comfort zome.
We have set office hours. Feel free to use these times to discuss questions adjacent to the course.
Katie uses she/her pronouns and Steve uses he/him pronouns. We will try to use your prefered pronouns, as indicated in PeopleSoft. Please don’t hesitate to correct us.
Honor Code¶
For computer assignments in computer science courses, the honor code is interpreted in very specific ways. Labs are expected to be the work of the individual student unless otherwise designated, designed and coded by them alone. Help locating errors and interpreting error messages is allowed, but a student may only receive help in correcting errors of syntax; help in correcting errors of logic is strictly forbidden. In general, if you are taking photos of someone else’s screen, looking at someone else’s screen, or telling someone else what to type, it is likely the work is no longer the work of an individual student.
The College and Department also have computer usage policies that apply to courses that make use of computers. Read more about these policies here.
Sharing Solutions. Please do not post your solutions to our assignments in any public forum, including public GitHub repositories. Students taking the course should not be looking for solutions, but tempting them by making solutions available is inappropriate. This applies not just to the semester you are taking the course, but to the future as well.
If in doubt as to what is appropriate, do not hesitate to ask. We’re happy to discuss this anytime.
Intellectual Property¶
As per College policy, no part of this course may be reproduced and/or distributed. In particular, no videos recorded as part of this class may be shared with anyone external to the CS104 course.
Accommodations¶
If formal accommodations need to be made to meet your specific learning or physical abilities, you should contact your instructors as possible to discuss appropriate accommodations. You should also contact the Director of Accessible Education, Dr. G. L. Wallace (x4672) or the Dean’s office (x4171). We will work together to ensure this class is accessible and inclusive.
Mental Health¶
If you are experiencing mental or physical health challenges that are significantly affecting your academic work, you are encouraged to contact your instructor and/or speak with Dean’s Office staff (x4171).
Public Health¶
In an attempt to keep our classroom and lab environments as healthy as possible, students and staff will be required to wear a mask at all times in the classroom and lab. If you feel ill, please do not come to class or lab and let us know if you are unable to attend class due to COVID restrictions. We will work with you to make sure you can make up any missed work, and to develop a plan that allows you to continue making progress in the course during your time in isolation/quarantine.
Acknowledgement¶
This course uses materials developed for the Data 8 course at the University of California, Berkeley, as well as publicly-available materials and data sets from a variety of other sources, including UMass COMPSCI/STATS 190F and Cornell CS 1380.