Course Procedures - Craig Persiko's Math 108 class

Is Math 108 the right class for you?

Math 108 Foundations of Data Science combines an introduction to inferential statistics with the fundamental skills and concepts of computer programming, using Python and Jupyter notebooks for hands-on experience in analyzing datasets. Additionally, the course investigates ethical issues surrounding Data Science such as algorithmic bias.
Math 108 is modeled after UC Berkeley's popular Data 8 course, and it articulates with it. So this course transfers to UC Berkeley in place of Data 8.
The only prerequisite knowledge I will assume is algebra: you need to be comfortable with order of operations, variables and functions from algebra. For example, you should know how to evaluate an expression like:
f(x) = 5 + 3 * x
when x=2, you should be able to calculate that f(2) = 11.
This is a 5-unit class, so I expect the average student to spend approximately 15 hours per week on the course material for this class, including class time, reading, homework, studying, etc.
The digital textbook Inferential Thinking will be offered to you at no additional cost thanks to Professors Ani Adhikari and John DeNero at UC Berkeley.

Course Requirements

This class will meet face-to-face on campus: Mondays and Wednesdays 1:10 PM - 3:25 PM in MUB 255 (Ocean Campus). Our classroom will have computers available for each student to use during class.
This is the only fully face-to-face section of MATH 108 this semester. (The other sections allow you to be remote if you want to.) So please register for it only if you want to be on campus, and plan to be there for all (or most) class meetings. Class participation will be part of your grade.
Proof of vaccination against Covid-19 is required, via this link, before you can register for this or any class that meets face-to-face in the Spring.
You will need to use a computer (or tablet with keyboard) for this class. Only a web browser is needed, so a simple computer or Chromebook is fine, but a phone is not enough - you need a full-size screen and keyboard. You may be able to borrow a chromebook and/or get wifi from CCSF. You should also be able to use a computer lab on campus.
Learning to write computer programs and analyze data is a time consuming and sometimes frustrating endeavor of open-ended problem-solving. I expect an average student to spend about 15 hours per week on this class: reading, watching videos, attending class, studying, working on programming assignments and other class work. If you don't have the time or dedication for such work, this class may not be for you. Make sure to keep up with the course materials and do all the assignments before they are due. Everything in this class builds cumulatively, so if you get behind, it is very difficult to pass the class. I am available to help via office hours and email, plus there are discussions and lots of opportunities to practice in this class.

Major Student Learning Outcomes

Upon successful completion of this course a student will be able to:

Use computational tools to create original visualizations and accompanying analysis of tabular data.
Write computer scripts that use conditional statements, loops, and subroutines and can interact with files.
Infer information about a population based on simulations created from sample data.
Implement machine learning and statistical methods to form predictions based on sample data.

How to reach me:

Instructor Web Page: http://fog.ccsf.edu/~cpersiko

Email: cpersiko@ccsf.edu

Office Hours (January 18 - May 25):

Mon, Tue, Wed, Thur 12:30 - 1:00pm in MUB 255 (Ocean Campus) and via Zoom
Fridays 4:30 - 5:00pm on Zoom only

Online System for Course Material, Discussions, and Assignment Submission (Canvas):

For more information and to access Canvas, see: https://ccsf.instructure.com/

Getting Help

Sharing ideas with each other is one of the best ways for you to learn, so when you have a question or problem, ask your classmates for help. You can email me anytime: please send me your entire program by email, and specify exactly what error messages or output your program is producing, along with your question. I'm also available during my office hours listed above.

There are also professional math tutors available to help you with this course.

Textbook (Free, Online only):

The digital textbook Inferential Thinking

Attendance Policy

You are expected to participate in class every week and submit all assignments on time. I may drop you from the class if you don't post or submit anything, nor come to class for over two weeks, without explanation.

Course Prerequisite Advisories:

ENGL 88 or ESL 188 or readiness for college-level English: you will need to be able to read and write college-level English, as we discuss and analyze data and statistics.
Two years of high school algebra or Mathematics 60 (Intermediate Algebra) is advised.
You must be familiar with the concept of functions and how to use them, as well as order of operations. You'll find that computer programming requires a similar sort of discipline and reasoning as mathematics.

Grading Policy:

Your final score will be made up of the following components:

30% - Lab Assignments done in class
5% - Lesson Quizzes in Canvas
20% - Homework Assignments
15% - Projects (homework)
10% - Midterm Exam
20% - Final Exam

Midterm and final grades will be assigned on the following percentage scale:

   90% - 100%   A
   80% - 89%     B
   70% - 79%     C
   60% - 69%     D
   0 - 59%         F

Students who do not take the final exam will be assigned a grade of "FW". An "FW" is an "F" grade that also indicates that the student did not complete the course.

I might employ a student worker to grade assignments for this class. If you have any questions or concerns about this arrangement or a particular grading decision the grader makes, please don't hesitate to tell me. I will be happy to review grading decisions on request.

Late Assignments:

Because of the importance of keeping up with the pace of class, each assignment has a cutoff date when submissions are no longer accepted. All assignments and projects are due by 11:59pm on the due date specified. If you need extra time for an assignment, please email me.

Semester Schedule

First day of class: Wednesday, August 19
Last day to drop a class without having to pay anything for it (SF Residents have to pay for the course if dropped after this date): January 28
Last day to add a class, also last day to drop a class without it appearing on transcript: February 4
Presidents' Day Weekend - no classes, campus closed: February 18-21
Flex Day (faculty professional development) - no classes: Tuesday, March 1
Midterm Test: March 23 in class only covering Chapters 1 - 11 of the course textbook.
Spring Break - no classes, college closed: March 26 - April 1
Last day to drop a class: April 21
Last Regular Class: Monday, May 16
Final Exam in class only: Friday, May 20 from 1-3pm in MUB 255, or Tuesday May 24 from 10:30-12:30 in Bungalow 708 (on the opposite side of campus, near the soccer field and tennis courts.) covering all topics from the semester, which includes the entire textbook.
Last day late assignments will be accepted: May 25
Final grades available on myRAM starting June 10, on Canvas starting June 2 or sooner.

Cheating

Cheating of any kind will not be tolerated. It will result in a grade of 0 on the assignment or test in question and can be cause for disciplinary action, including suspension or expulsion. Cheating on assignments means copying code or answers from another source - that includes copying code from a web site, or submitting work written by someone else. Getting help from other sources is not cheating as long as you're not copying their work or allowing them to copy yours. All code and work that you submit must be written by you. On the exams, any collaboration or copying constitutes cheating.

Software and Computer Access

All the course materials and assignments are designed to work with Jupyter Notebooks using Python, with a JupyterHub that is all setup for you on the web. So only a web browser is needed, and a simple computer or Chromebook is fine, but a phone is not enough - you need a full-size screen and keyboard. You may be able to borrow a chromebook and/or get wifi from CCSF. There should also be a computer lab available to you on campus.

Use of CCSF computers, including remote access, is regulated by the CCSF Computer Usage Policy. Do not give passwords and other sensitive information to unauthorized persons. This means you shouldn't tell anyone your personal passwords and you shouldn't give class account passwords to people who aren't in this class.

Drop Procedures

Generally it is your responsibility to drop or withdraw from a class by the final deadlines given in your course schedule. Do not ask me to drop you; use the myRAM system, or contact the Office of Admissions and Records to be withdrawn from a class. If you don't come to class, submit any assignments, or post in any discussions for 10 days without explanation, I may drop you from the class. If your name is on the roll at the end of the semester and you don't take the final exam, you will be assigned a final grade of FW. I will not give a late or retroactive drop or withdrawal.

Disability Accomodations

Students with disabilities who need accommodations are encouraged to contact me. Disabled Students Programs and Services (DSPS) is available to facilitate the reasonable accommodation process. The DSPS office is located in the Rosenberg Library, Room 323 and can be reached at (415)452-5481 or http://www.ccsf.edu/dsps

Equity, Diversity, Inclusion, and Anti-Discrimination

I am committed to promoting equity, diversity, and inclusion in the fields of computer science and mathematics. We strive to make our classes accessible and exciting to all, particularly those who are often excluded from or face frequent identity discrimination in the field. If you have a suggestion for how we can better support you and/or your classmates, please reach out to any instructor or to the Department Chair. We will make sure your voice is heard. For more information and resources outside of our department, please visit CCSF’s Office of Student Equity.

The San Francisco Community College District is committed to the principles of equal opportunity, and the prevention of discrimination and harassment in any program or activity of the District on the basis of race, color, ancestry, national origin, ethnic group identification, religion, age, gender, gender identity, marital status, domestic partner status, sexual orientation, disability or AIDS/HIV status, medical conditions, or status as Vietnam-era veteran, or on the basis of these perceived characteristics, or based on association with a person or group with one or more of these actual or perceived characteristics. If you believe you have been subject to discrimination, please contact Mildred Otis, Title 5/EEO/ADA/Title IX Compliance Officer, at motis@ccsf.edu

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