| Section 1 | |
| Professor: | Renzhi Cao |
| Office: | MCLT 248 |
| Email: | caora@plu.edu |
| Phone: | 253-535-7409 |
| Office hours: | Online Schedule |
This course is going to introduce data science concepts by solving data related problems in different fields. The data manipulation and cleaning techniques will be introduced and used in course project, where students could form groups and work on Github to collaborate. Students will also learn hands-on skills on topics like machine learning, natural language processing, SQL on databases, etc. It is intended for learners who have basic python or programming background and want to gain new insight into data by training machine learning models, visualizing patterns, and analyzing texts, etc. The programming language Python is the main language introduced in this course and used in course projects. Prerequisite: DS 133 or CS 144.
The following textbook is required for this course. The lecture slides and other materials provided by the instructor are also important as the secondary reference.
Data Science from Scratch - First Principles with Python. O'Reilly Media, 2015.
Section 1: Tuesday and Thursday 09:55-11:40, Morken 203
The official prerequisite for this course is DS 133 - Intro. to Data Science, or CS 144. Some programming experience (especially Python programming) is preferred, and math background is plus. If you have questions about your programming background I will be happy to discuss them with you.
You are expected to attend all lectures. There may be in-class exercises, quiz or assignments given regularly. You are responsible for all material covered during the class. If you must miss a class, you will want to contact someone in your section for his or her notes. Expect that missing classes may result in a lower grade, directly or indirectly.
The assignments and other helpful information is available from the class home page and occasionally I will make announcements on the class Sakai site. I strongly recommend you check the home page and Sakai regularly. I may also contact you via email (using your PLU email address) with important class information, so you should check your email regularly as well. Please feel free to email me with any questions you might have or to set up an appointment if you need to meet with me outside of office hours.
The department operates several laboratories in the Morken Center. Morken 210 serves as a closed lab for CS 270, CS 144, and CS 131, as well as for other classes on occasion. It serves as an open lab all other times during the week and in the evenings and you are welcome to use it during those times. The lab opens with a card-swipe lock so be sure to bring your PLU ID in order to be admitted. The lock will only work for IDs of students on the "admit list". Please let me know right away if you if your ID card does not work. If the 210 lab is full or being used by another class you may use the machines in Morken 227.
As members of the PLU community, it is all of our responsibility to provide a safe, inclusive classroom environment that is considerate of others, encourages exploration of ideas and allows opportunities for everyone to fully engage in classroom discussions, activities, lectures, etc. To accomplish this, I ask that each of us refrain from conduct that is disrespectful and/or distracting to others in the classroom. It is amazing how playing Internet games, checking out facebook/blogs or holding private conversations during class can distract the most focused of students (or instructors!).
Examples of classroom misconduct includes:
Your grade will be based on the following:
| Component | Weight | Details |
|---|---|---|
| Course participation | 10% | There will be in-class exercises, pop-up quiz, and interactions in or out of class, counting 10% of your final grade. Students will only be allowed to skip a class in the event of an emergency, illness, or absence due to a university sanctioned activity such as a sporting event or music performance. If you must miss a class, you should make every effort to notify me ahead via e-mail or voice mail. |
| Home work | 40% | There will be several home works during the semester. 10% per day penalty for late submission. |
| Mid-term exam | 15% | There will be one mid-term exam for this class. |
| Final exam | 15% | There will be one final exam at the end of semester. No make-up. |
| Project | 20% | There will be one project based on group. You can choose to present any project instead of project proposed from the class, but you need to get approval from instructor before you start. |
Your final grade will be based on your weighted average using some approximation of the following table:
| Overall Score | Grade |
| 100% -- 90% | A / A- |
| 90% -- 80% | B+ / B / B- |
| 80% -- 70% | C+ / C / C- |
| 70% -- 60% | D+ / D / D- |
| 60% -- 0% | E |
The grading scale is a general guideline only. I may adjust your grade depending on various factors including class participation, attitude, and timeliness (turning in assignments, attendance etc.).
In computer science courses, we recognize that interactions with classmates and others can help facilitate the learning process. However, there is a line between enlisting the help of another and submitting the work of another. The following is intended to help clarify that line as it applies to this class. If in doubt, ask your instructor before receiving or giving the assistance.
All work that you submit must be your own. The following lists include examples that indicate the kinds of collaboration that are acceptable and unacceptable in this course. These lists are not exhaustive. If you are unsure about a behavior, ask your instructor.
Be careful when providing help to your fellow students. Refer other students to class resources (lecture examples, the text- book, the web site, or emailing an instructor). You must not share your solution with others. You must also ensure that your work is not copied by others by not leaving it in public places, emailing it others, posting it on the web, etc.
Make sure to call ahead to confirm whether class is meeting if you have any concerns about snow accumulations or icy roads that would make travel to campus unsafe. You can call the University's hotline after 6 a.m. (535-7100) or access the PLU website to see if school has been cancelled. If the university is open, but this class needs to be cancelled, that information can either be found on Sakai or will be emailed to you. Students are urged to use caution and personal discretion and avoid undue risk and personal danger when making travel decisions during extreme weather conditions.
Students with medically recognized and documented disabilities and who are in need of special accommodation have an obligation to notify the University of their needs. Students in need of accommodation should contact the Office of Disability Support Services (http://www.plu.edu/dss/, x7206). If you need course adaptations or accommodations because of a disability, if you have emergency medical information, or if you need special arrangements in case the building must be evacuated, please make an appointment with your instructor as soon as possible.
Students are also reminded that they are responsible for notifying instructors of any conditions that may impair their academic performance. Without advance warning, such difficulties cannot be used later as a basis for requesting make-up exams or reconsideration of grades.