SDEV 101 - Data Analytics Technologies

Winter 2024

Course Information

• Meeting times: TTh 1-2:50pm
• Location: AC 330, 1221 D St NE, Auburn, WA 98002 (map)
• Credits: 5

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!
• Instructor's contact hours are weekdays from 9am through 5pm on email and students can expect a response to email messages within 1-2 business days.

Course Description and Outcomes

Students learn how to use tools and technologies to collect, transform, and organize data to draw conclusions and drive informed decision making. Explore data analyst career paths. No prior experience required. Previously SDEV 122.

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

• Recall the six stages of the data processing cycle.
• Apply filter and sort tools in a spreadsheet to process and analyze data in a file.
• Apply filter and sort operators ina SQL SELECT statement to process and analyze data in a database.
• Apply spreadsheet formulas and functions to clean and summarize data.
• Apply SQL functions to clean and summarize data.
• Create a data visualization using a spreadsheet.
• Create a data visualization using a business intelligence tool.
• Recognize terms and concepts relating to contemporary data analytics technologies and careers.
• Recognize broader impacts and issues relating to data analytics such as privacy, security, ethics, and transparency.

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. It meets four hours in person, and also has an online component. Face-to-face sessions include activities that can not be made up outside of class. The online component to the course consists of readings, videos, and activities. All assignments and announcements are posted on the course website in Canvas.

Course Resources

Required Textbooks

• There are no required textbooks for this course. All resources will be available online.

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/

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!

Email

Check the course web site 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

The Tutoring Schedule is posted in Canvas under Course Resources.

Tutoring Protocols

It is OK to send your assignment to a tutor 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.

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.

After the grace period, assignments may be turned in up to one week after the due date for up to 50% credit. After one week, an assignment may receive at most 25% credit.

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

In-Class Assignments

All in-class assignments are to be completed during class. These assignments are designed to include an interactive group component, which is integral to the learning experience and class dynamics. It is essential for all students to attend class regularly and actively participate in these collaborative assignments to maximize their learning and contribute to the overall classroom environment.

There will be an opportunity during the quarter to make up lost points if you have an unavoidable absence.

Attendance

Regular attendance and participation are required to succeed in this course. Absences have a huge impact on your learning, especially since this class only meets twice a week. If missing a class is unavoidable, you do not need to notify the instructor. Instead, ask a classmate to take notes for you.

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: