Course Syllabus

Data Science II

Winter 2021

Course Information

• Meeting times: TBD
• Location: TBD
• Credits: 5
• Prerequisite: Data Science I

Instructor Information

• Name: Tina Ostrander
• Email: tostrander@greenriver.edu 
• Office Hours, AC 360C
  • TBD
  • by appointment (email me with 2-3 times that you are available)
• I will reply to emails within one business day. If you haven't heard back by then, please try again in case my spam folder ate your message!

Course Description

In this course, you will basic statistical analysis and machine learning methods, along with prediction. The course uses computational tools to analyze data and perform inferential reasoning.

Course Content

• Basic statistical analysis and machine learning methods
• Prediction (linear and logistic regression)
• Sampling variability and uncertainty of statistical estimates (Inference I)
• Testing statistical hypotheses (Inference II)
• Safeguarding reproducibility and multiple hypothesis testing
• Causality and experimentation
• Machine learning
• Ongoing Challenges to Data Science + Review

Learning Outcomes

• Basic statistical analysis and machine learning methods
• How to effectively visualize results

Course Resources

Required Textbook

• Python Data Science Handbook: Essential Tools for Working with Data (2016)

Canvas

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

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

If you have any questions about the course, reading, or the homework, please 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! 

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 both at Auburn Center and the main campus (Holman Library) for all IT Software Development classes. View the Tutoring Schedule

Tutoring Protocols

• It is OK to ask a tutor for feedback on your assignment ahead of time.
• 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.

Student Portal

my.greenriver.edu contains information and links for important student resources.

LinkedIn Learning

LinkedIn Learning provides a wide range of technical video tutorials, and is free to Green River students.

Course Policies

Late Work

All assignments are posted well in advance, so be sure to get an early start! Late assignments will be accepted up to one week after the due date, and will receive 50% credit. Pair programs may be turned in within one week of the due date without penalty.

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 are responsible for making arrangements with a classmate to get  any missed announcements, handouts, or lecture material.

The Donut Rule

Cell phones and pagers must be turned off while in the classroom. If your cell phone rings in class, you owe me a latte the next class period. If my phone rings in class, everyone gets doughnuts!

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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: