CS 108 - Data Science Foundations

Winter 2025 (8232)

Course Information

• Meeting times: MW 10:00-11:50am
• Location: AC 250 (Auburn Center Campus, 1221 D St NE, Auburn, WA | map)
• Credits: 5
• Prerequisite: Prerequisites: Eligible for MATH 97 or higher; and eligible for ENGL& 101; or instructor consent.

Instructor Information

• Tina Ostrander, tostrander@greenriver.edu
  • Office Hours
  • MTWTh 9-9:50am
    • AC 360C (Auburn Center, 1221 D St NE, Auburn, WA | map) or
    • Zoom (Passcode: learn)
  • By Appointment: Email me at tostrander@greenriver.edu with 2-3 days and times that you are available
  • Contact hours are weekdays from 9am through 5pm. Students can expect a response to email messages within one business day. If you haven't heard back by then, please try again in case the spam folder ate your message!

Course Description and Outcomes

Hands-on introduction to data science for everyone, no previous experience is required. Students learn how to collect, compute, analyze, and visualize data to better understand the world in which we live.

By the end of the course, the student will be able to:

• Create a computational artifact using computing tools and techniques to solve a problem.
• Collaborate in the creation of computational artifacts or when writing a program.
• Develop an abstraction when writing a program or creating computational artifacts.
• Find patterns and test hypotheses about digitally processed information to gain insight and knowledge.
• Explain the insight and knowledge gained from digitally processed data by using appropriate visualization, notations, and precise language.
• Summarize information from data to explain patterns or trends using descriptive statistics.
• Manage, clean, summarize, and visualize simple data sets.

Program Outcomes

• Use appropriate reasoning to evaluate problems, make decisions, and formulate solutions.
• Give reasons for conclusions, assumptions, beliefs, and hypotheses.
• Evaluate and interpret quantitative and symbolic reasoning information/data.
• Implement technology to solve problems.

Campus Learning Outcomes

• Quantitative and Symbolic Reasoning: Quantitative Reasoning encompasses abilities necessary for a student to become literate in today’s technological world. Quantitative reasoning begins with basic skills and extends to problem solving.

Course Modality

This is a hybrid course. We meet four hours each week in person. There is also an online component to the course, approximately one hour per week. The online component of the course may consist of readings, videos, tutorials and other activities. All assignments and announcements are posted in the course Canvas shell.

Course Resources

Textbooks

There is no required textbook for this course. All resources are free and online.

Canvas

All assignments, supplementary materials, the course schedule, due dates, and updates to this syllabus will be posted to the course Canvas shell at https://egator.greenriver.edu/

If you have any questions about the course, reading, or the homework, ***post them to Canvas Discussions***. This will enable you to get an answer to your questions more quickly, and also help classmates who might have the same question. If you see a question in the Discussions that you think you can answer, please do so!

Email

Check the course Canvas shell and your @mail.greenriver.edu email account daily for important announcements.

If you have questions of a personal nature, such as regarding a specific grade or scheduling an appointment, then either email me or visit me during office hours.

Tutors

There are tutors available for all Software Development classes. The tutoring schedule will be posted in Canvas under Course Resources.

Tutoring Protocols

It is OK to share your assignment solution with a tutor ahead of time and ask for feedback and suggestions.

It is OK to bounce ideas off a tutor and/or for them to suggest specific constructs for approaching a problem.

It is OK to go over how specific constructs work (e.g. Try stuff in a sandbox)

It is OK to debug your code with a tutor as long as the tutor is asking questions and not dictating exactly how to correct each mistake.

It is NOT OK for a tutor to "drive" by correcting or writing code for you.

It is NOT OK for a tutor to dictate a step by step approach to solving the problem.

It is NOT OK for a tutor to show you their own solution to an individual assignment.

Course Policies

Late Policy

All assignments are posted well in advance. Be sure to get an early start so that you have plenty of time to get help if you need it.

All assignments will have a 24-hour grace period during which no points will be deducted.

Pair programs may be turned in within one week of the due date and still receive full credit. Pair programs submitted more than one week late will not receive credit.

All other assignments may be turned in up to one week after the due date for 50% credit. After one week, an assignment will not receive credit.

No late assignments will be accepted after the last day of the quarter.

Attendance

Regular attendance and participation are required to succeed in this course. Absences have a huge impact on your learning. If missing a class is unavoidable, you do not need to notify the instructor. Instead, ask a classmate to take notes for you, and pick up any handouts you may have missed.

Academic Integrity and Collaboration

Plagiarism occurs when you knowingly submit someone else's work (ideas, words, code) as your own. Plagiarism is an act of intentional deception that is not only dishonest, it robs you of the most important product of education - the actual learning. Should I suspect that you have plagiarized, I will talk with you one-on-one and ask you to prove the work in question is your own. 

You may use AI tools for learning or research, but you are responsible for verifying the accuracy of any AI-generated information. All submitted work must be your own. AI-generated submissions will be considered academic dishonesty.

The purpose of this restriction is to ensure that students develop a fundamental understanding of technical concepts and problem-solving skills.

Software Development and Data Analytics are skills that demands active engagement, critical thinking, and hands-on practice. By prohibiting the use of AI text generators, we aim to promote a genuine learning experience where students grapple with challenges, debugging issues, and algorithmic thinking on their own. This approach encourages the development of analytical skills, creativity, and the ability to translate conceptual knowledge into practical solutions.

Furthermore, fostering a learning environment that relies solely on individual effort and peer collaboration prepares students for real-world scenarios where coding proficiency is essential. While tools like ChatGPT have their place in certain applications, this course aims to lay a strong foundation in skills that students can build upon throughout their academic and professional journeys.

Students are encouraged to seek assistance from the instructor, tutors, and peers, as well as to utilize the provided course materials and resources to enhance their understanding and overcome challenges. Embracing the learning process, persevering through difficulties, and honing problem-solving abilities are key objectives of this course, and refraining from the use of AI text generators supports the achievement of these goals.

If your work is not your own, you will receive a failing grade of zero on the assignment. If your work continues to be plagiarized during the quarter, you will receive a failing grade for the course.

Grading

Grading in this course consists of your demonstrated competency and professionalism. If you have any questions or concerns about a course grade, talk to the instructor within two weeks of receiving the grade.

Grades will be converted according to the following scale: