This document and others linked within it should be your PRIMARY source for understanding the expectations of this course. Be sure to read it carefully. You must contact the instructor for clarification if you receive information from any another source that is in contradiction to what is provided below.

Course Description

Data is everywhere in the modern world. Each of us has access to data on virtually any topic imaginable via the internet, and public and private institutions are making use of data in decision-making more than ever before. However, access to data is not the same thing as access to information. The purpose of this course is to develop some of the foundational skills needed to consume data and create information. The main theme in the course is understanding the variability inherent in data, and the inherent uncertainty associated with conclusions drawn from data.

The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets from a variety of disciplines. It delves into social issues surrounding data analysis such as privacy and design.




Our primary text is an online book Python Data Science Handbook

The computing platform (Jupyter Notebooks) for the course is hosted at


You are not alone in this course; the mentors (staff and the instructors) are here to support you as you learn the material. It’s expected that some aspects of the course will take time to master, and the best way to master challenging material is to ask questions.

Contact us on Piazza!

We will be communicating with you and making announcements through an online question-and-answer platform called Piazza. We ask that when you have a question about the class that might be relevant to other students, post it on Piazza instead of emailing us (if you wish, you can post your question anonymously to your classmates). That way, all the staff can be on the same page and everyone can benefit from the response. You can also post private messages to instructors on Piazza, which we prefer to email.

We will also hold office hours and open-lab hours for in-person discussions. Small-group tutoring sessions can be available for students in need.


Your mastery of class material will be assessed in the following ways, and final grades will be computed as follows:

It is certainly possible for all students to receive high grades in this course if all of you show mastery of the material on exams and complete all assignments.


Lecture attendance is optional but is highly encouraged. You are adults and are responsible for your learning. I expect you to come to all classes, since this is an essential part of your education. This is also your time to engage with the material and ask your questions. During class, you will work alone and in groups to work through problems and answer questions. On some days, the groups will be asked to turn in their in-class work. If you were absent, you miss the opportunity for the points on that in-class assignment. Period. There is no makeup. In lieu of providing a makeup opportunity, I will drop the lowest 3 in-class-assignment grades. Each class activity will be of equal value. The participation portion of your grade will also include providing good answers on Piazza and engaging with the various activities that the instructor will provide throughout the quarter.


Data science is about analyzing real-world data sets, and so a series of projects involving real data are a required part of the course. You may work with one partner, and we strongly recommend that you find a partner in your lab section.

Weekly homework assignments are a required part of the course. Each student must submit each homework independently, but you are allowed to discuss problems with other students without directly sharing the answers.

Make a serious attempt at the assignment yourself, and then discuss your doubts with others. In this way you, and they, will get more out of the discussion. Please write up your answers in your own words and don’t share your completed work.


Weekly labs are a required part of the course and should be submitted during your lab session. To receive credit, you must attend lab, work on the lab assignment until you’re finished or the lab period is over, and get checked off by a TA. Labs will be released on Wednesday night. If you cannot attend lab physically, you may complete a lab assignment remotely, but you must complete it by the date and time listed at the top of each lab. Note that if you attend lab, you can still get credit even if you don’t finish all of the lab problems. However, if you choose to work remotely, you must finish the entire lab to receive credit. Each person must submit each lab independently, but you are welcome to collaborate with other students in your lab room.

Midterm Exam, Thursday, May 9, 2019

Unless you have accommodations as determined by the university and approved by the instructor, you must take the exam at the date and time provided here. Please check your course schedule and make sure that you have no conflicts with the exam. If you have a conflict, please post a private note on Piazza visible to Instructors before the end of the third week of classes.

Final Project

More information will be provided later in the class. Due June 12, 2019.

Late Submission

Late submissions of labs will not be accepted under any circumstances. The same goes for homework, unless you have relevant DSP accommodations and you contact us before the assignment is due.

Learning Cooperatively

With the obvious exception of exams, we encourage you to discuss all of the course activities with your friends and classmates as you are working on them. You will definitely learn more in this class if you work with others than if you do not. Ask questions, answer questions, and share ideas liberally. If some medical or personal emergency takes you away from the course for an extended period, or if you decide to drop the course for any reason, please don’t just disappear silently! You should inform your project partner, so that nobody is depending on you to do something you can’t finish.

Academic Honesty

Cooperation has a limit, however. You should not share your code or answers directly with other students. Doing so doesn’t help them; it just sets them up for trouble on exams. Feel free to discuss the problems with others beforehand, but not the solutions. Please complete your own work and keep it to yourself. The exception to this rule is that you can share everything related to a project with your project partner and turn in one project between you.

Penalties for cheating are severe — they range from a zero grade for the assignment or exam up to dismissal from the University, for a second offense.

Rather than copying someone else’s work, ask for help. You are not alone in this course! We are here to help you succeed. If you invest the time to learn the material and complete the projects, you won’t need to copy any answers.

A Parting Thought

This page shouldn’t end with a list of penalties for cheating or lateness, because penalties and grades aren’t the purpose of the course. We actually just want you to learn and have a great time in the process. Please keep that goal in mind throughout the semester. Welcome to Data Science Tools and Techniques!