INFO 511 - Fundamentals of Data Science

Fall 2024

Course Description

This course presents fundamental aspects of data science, including Python programming (e.g., data collection, cleaning, visualization), statistics, and mathematics (e.g., linear algebra and calculus). The course establishes the foundation for advanced data-intensive classes, providing both theoretical understanding and practical knowledge essential for comprehending Data Science and its applications.

Course offering

642-2244-1 INFO 511 101 201 – Fundamentals of Data Science

Instructor Information

Instructor

Dr. Greg Chism,

Assistant Professor of Practice,

School of Information

gchism@arizona.edu

Office: Harvill 420

Office Hours: Tuesdays, 11:30am-12:30pm, or by appointment via Zoom

Prerequisites

Students should be comfortable with mathematical functions of one and two variables, should have at least some familiarity with basic concepts of probability and experience with a programming language.

Course Format

Asynchronous online lectures. Videos of each lecture will be available no later than the Monday of each week by 9am AZ time.

Course Objective

This course will (1) discuss effective approaches for acquiring, manipulating, and analyzing data, (2) outline fundamental concepts in programming such as variables, data types, and control structures, (3) discuss the use of Python libraries that are central for data science (e.g., NumPy, Matplotlib), (4) present a solidfoundation in descriptive statistics, probability and (5) discuss the mathematical foundations that are essential for data science.

Learning Outcomes

• Recognize the skills required to perform data science tasks from data acquisition to storytelling with data.

• Review fundamental topics in linear algebra and calculus for data science.

• Use Python programming techniques to prepare, visualize, and transform data.

• Demonstrate an understanding of how data science projects are approached.

• Communicate and present data science projects and results.

Textbooks

The following are freely available through the UA’s library system.

• Data Science from Scratch first principles with Python. Joel Grus. O’Reilly, 2nd edition, 2019. (Main textbook)

• Essential Math for Data Science.

All remaining books are freely available online.

• Python for Data Analysis. Wes McKinneyz. Python for Data Analysis. O’Reilly, 3rd edition 2023.

• Practical Statistics for Data Science. Peter Bruce, Andrew Bruce, Peter Gedeck. O’Rielly, 2016.

• Introduction to Statistical Learning with Applications in Python. James Garth, Witten Daniela, Hastie Trevor, Tibshirani Robert. Springer, 2021/2023.

• Python Companion to Statistical Thinking in the 21st Century. Russell A. Poldrack.

Course competencies

This course is a core requirement for the MS in Data Science. It will help you master the following competencies:

• MS1. Students will establish the ability to exercise the four key techniques of computational thinking: decomposition, pattern recognition, abstraction, and algorithms.

• MS2. Students will obtain the skills of collecting, manipulating, and analyzing different types of data at different scales, and interpreting the results properly.

Course Schedule

An up-to-date schedule, assignments, and due dates can be found on the course website: datasciaz.netlify.app.

Course Community

U of A Community Standard

All students must adhere to the U of A Student Rights & Responsibilities: The University of Arizona is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, and accountability. Citizens of this community commit to reflect upon these principles in all academic and non-academic endeavors, and to protect and promote a culture of integrity.

Inclusive community

It is my intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength, and benefit. It is my intent to present materials and activities that are respectful of diversity and in alignment with U of A’s Commitment to Diversity and Inclusion. Your suggestions are encouraged and appreciated. Please let me know ways to improve the effectiveness of the course for you personally, or for other students or student groups.

Furthermore, I would like to create a learning environment for my students that supports a diversity of thoughts, perspectives and experiences, and honors your identities. To help accomplish this:

• If you have a name that differs from those that appear in your official U of A records, please let me know! You’ll be able to note this in the Getting to know you survey.
• If you feel like your performance in the class is being impacted by your experiences outside of class, please don’t hesitate to come and talk with me. If you prefer to speak with someone outside of the course, your academic dean is an excellent resource.
• I (like many people) am still in the process of learning about diverse perspectives and identities. If something was said in class (by anyone) that made you feel uncomfortable, please let me or a member of the teaching team know.

Communication

All lecture notes, assignment instructions, an up-to-date schedule, and other course materials may be found on the course website: datasciaz.netlify.app.

I will regularly send course announcements via email and Slack, make sure to check one or the other of these regularly. If an announcement is sent Monday through Thursday, I will assume that you have read the announcement by the next day. If an announcement is sent on a Friday or over the weekend, I will assume that you have read it by Monday.

Where to get help

• If you have a question during lecture, feel free to ask it! There are likely other students with the same question, so by asking you will create a learning opportunity for everyone.
• The teaching team is here to help you be successful in the course. You are encouraged to attend office hours to ask questions about the course content and assignments. Many questions are most effectively answered as you discuss them with others, so office hours are a valuable resource. Please use them!
• Outside of class and office hours, any general questions about course content or assignments should be posted on the course Slack. There is a chance another student has already asked a similar question, so please check the other posts on Slack before adding a new question. If you know the answer to a question posted on Slack, I encourage you to respond!

Check out the Support page for more resources.

I want to make sure that you learn everything you were hoping to learn from this class. If this requires flexibility, please don’t hesitate to ask.

• You never owe me personal information about your health (mental or physical) but you’re always welcome to talk to me. If I can’t help, I likely know someone who can.

• I want you to learn lots of things from this class, but I primarily want you to stay healthy, balanced, and grounded during this crisis.

Lectures

The goal of the lectures is for them to be as interactive as possible. My role as instructor is to introduce you new tools and techniques, but it is up to you to take them and make use of them. A lot of what you do in this course will involve writing code, and coding is a skill that is best learned by doing. Therefore, as much as possible, you will be working on a variety of tasks and activities throughout each lecture and lab. You are expected to meaningfully contribute to in-class exercises and discussion.

You are expected to bring a laptop to each class so that you can take part in the in-class exercises. Please make sure your laptop is fully charged before you come to class as the number of outlets in the classroom will not be sufficient to accommodate everyone. See the U of A Libraries loaner technology if you need a loaner laptop.

Activities & Assessment

You will be assessed based on five components: application exercises, labs, exams, project, and teamwork.

Application exercises

Parts of some lectures will be dedicated to working on Application Exercises (AEs). These exercises which give you an opportunity to practice apply the statistical concepts and code introduced in the prepare assignment. These AEs are due by the end of the week of the corresponding lecture period (Sunday at midnight). To submit the AEs all you need to do is to push your work to your GitHub repo.

Because these AEs are for practice, they will be graded based on completion, i.e., a good-faith effort has been made in attempting all parts. Successful on-time completion of at least 80% of AEs (will result in full credit for AEs in the final course grade.

Labs

In labs, you will apply what you’ve learned in the videos and during lectures to complete data analysis tasks. You may discuss lab assignments with other students; however, lab should be completed and submitted individually. Lab assignments must be typed up using Quarto or Jupyter, all work must be pushed to your GitHub repository for the lab by the deadline.

Labs are due at 5pm AZ time on the indicated due date.

The lowest lab grade will be dropped at the end of the semester.

Exams

There will be two exams in this course. Each exam will be open-note take-home. Through these exams you have the opportunity to demonstrate what you’ve learned in the course thus far. The exams will focus on both conceptual understanding of the content and application through analysis and computational tasks. The content of the exam will be related to the content in videos and reading assignments, lectures, application exercises, and labs.

More detail about the exams will be given during the semester.

Project

The purpose of the project is to apply what you’ve learned throughout the semester to analyze an interesting data-driven research question. The project will be completed with in teams, and each team will present their work in the final exam session of the semester. The write-up will be due on the same day.

You cannot pass this course if you have not completed the project.

More information about the project will be provided during the semester.

Grading

The final course grade will be calculated as follows:

Category	Percentage
Labs	30%
Project	25%
Exam 1	10%
Exam 2	10%
Application Exercises	25%

While there are no specific points allocated to participation, we will be recording your participation (mainly via slack) in periodically throughout the semester, and this information will be used as “extra credit” if you’re in between two grades and a minor bump would help.

The final letter grade will be determined based on the following thresholds:

Letter Grade	Final Course Grade
A	>= 90
B	80 - 89.99
C	70 - 79.99
D	60 - 69.99
E	50-59.99
F	< 50

These are upper bounds for grade cutoffs, depending on the class performance the cutoffs may be lowered but they won’t be increased.

Five tips for success

Your success on this course depends very much on you and the effort you put into it. The course has been organized so that the burden of learning is on you. I will help you be providing you with materials and answering questions and setting a pace, but for this to work you must do the following:

1. Complete all the preparation work before class.

2. Ask questions. As often as you can. In class, out of class. Ask me, ask your friends, ask the person sitting next to you. This will help you more than anything else. If you get a question wrong on an assessment, ask us why. If you’re not sure about the lab, ask. If you hear something on the news that sounds related to what we discussed, ask. If the reading is confusing, ask.

3. Do the readings.

4. Do the lab. The earlier you start, the better. It’s not enough to just mechanically plow through the exercises. You should ask yourself how these exercises relate to earlier material, and imagine how they might be changed (to make questions for an exam, for example).

5. Don’t procrastinate. The content builds upon what was taught in previous weeks, so if something is confusing to you in Week 2, Week 3 will become more confusing, Week 4 even worse, etc. Don’t let the week end with unanswered questions. But if you find yourself falling behind and not knowing where to begin asking, come to office hours and work with a member of the teaching team to help you identify a good (re)starting point.

Course policies

Academic honesty

TL;DR: Don’t cheat!

Please abide by the following as you work on assignments in this course:

• Collaboration: Only work that is clearly assigned as team work should be completed collaboratively.

  • You may discuss lab assignments with other students; however, you may not directly share (or copy) code or write up with other students. For team assignments, you may collaborate freely within your team. You may discuss the assignment with other teams; however, you may not directly share (or copy) code or write up with another team. Unauthorized sharing (or copying) of the code or write up will be considered a violation for all students involved.

  • You may not discuss or otherwise work with others on the exams. Unauthorized collaboration or using unauthorized materials will be considered a violation for all students involved. More details will be given closer to the exam date.

  • For the project, collaboration within teams is not only allowed, but expected. Communication between teams at a high level is also allowed however you may not share code or components of the project across teams.

  • On individual assignments you may not directly share work (including code) with another student in this class, and on team assignments you may not directly share work (including code) with another team in this class.

• Online resources: I am well aware that a huge volume of code is available on the web to solve any number of problems. Unless I explicitly tell you not to use something, the course’s policy is that you may make use of any online resources (e.g., StackOverflow) but you must explicitly cite where you obtained any code you directly use (or use as inspiration). Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism.

• Use of generative artificial intelligence (AI): You should treat generative AI, such as ChatGPT, the same as other online resources. There are two guiding principles that govern how you can use AI in this course¹:

  1. Cognitive dimension: Working with AI should not reduce your ability to think clearly. We will practice using AI to facilitate—rather than hinder—learning.

  2. Ethical dimension: Students using AI should be transparent about their use and make sure it aligns with academic integrity.

  • ✅ AI tools for code: You may make use of the technology for coding examples on assignments; if you do so, you must explicitly cite where you obtained the code. Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism. You may use these guidelines for citing AI-generated content.

  • ❌ AI tools for narrative: Unless instructed otherwise, you may not use generative AI to write narrative on assignments. In general, you may use generative AI as a resource as you complete assignments but not to answer the exercises for you.

  You are ultimately responsible for the work you turn in; it should reflect your understanding of the course content.

If you are unsure if the use of a particular resource complies with the academic honesty policy, please ask a member of the teaching team.

College of Information Science Academic Integrity Policy

This policy agreed upon by faculty in the College of Information Science at the University of Arizona (InfoSci) applies in addition to the Dean of Students’ Code of Academic Integrity.

Students in courses at the U of A InfoSci are expected to maintain rigor in their academic performance with intent to learn, practice, and overcome challenges toward personal growth and enrichment. As future professionals in digital environments, InfoSci students are also expected to exercise transparency and integrity in collaborations and in the use of tools and resources that may aid completion in assignments for our courses.

Consider the following PROHIBITED practices in this course, unless the instructor has specifically written instructions or permission to do otherwise:

• Posting a question on an online site such as Chegg.com, and copying and pasting some or all of the response into an assessment

• Posting an assessment from the course on online sharing sites such as Course Hero. Aiding other students in violation of academic integrity is also a violation, and is potential copyright infringement.

• Generating and submitting, in whole or in part, text or code through Artificial Intelligence such as ChatGPT, QuillBot, and text summarizers

• Using, in whole or in part, computer code not written by the student (for example, from another student, a book, or the internet) in an assignment or project. This includes using such code in modified or unmodified form.

• Searching for solutions to projects or assignments on the internet or through other tools, when your instructor intended for you to learn the solution through exercises (e.g. Googling for the solution to a question on an assignment).

• Simultaneously submitting the same assignment as another student enrolled into the course without prior permission from the instructor

Exceptions: Clear Instructions will be Provided

In any cases in which this course requires or permits students to use practices in the list above, clear written instructions will specify the tools allowed or required, so students can be certain they are working as instructed. See the U of A InfoSci Academic Integrity Policy, the U of A Code of Academic Integrity and Syllabus policy for more information.

LLMs and ChatGPT

Large language models (LLMs) like ChatGPT are a type of artificial intelligence (AI) engine that can look like it generates the code you need for Python labs and short answer questions. You are encouraged to use ChatGPT to debug code and experiment. However, abuse of ChatGPT can be traced (e.g., failing to give credit or cite ChatGPT when it is used) which could result in your suspension or termination from the course and even your program of study. Keep in mind, too, that while the code may appear legitimate, early studies have shown ChatGPT is not all that accurate with sophisticated coding. Exercise your scholarly discretion and maintain a sense of integrity in your statistical learning journey.

See my additional policies on this subject above.

Late work & extensions

The due dates for assignments are there to help you keep up with the course material and to ensure the teaching team can provide feedback within a timely manner. We understand that things come up periodically that could make it difficult to submit an assignment by the deadline. Note that the lowest lab assignment will be dropped to accommodate such circumstances.

• Labs may be submitted up to 3 days late. There will be a 5% deduction for each 24-hour period the assignment is late.

• There is no late work accepted for application exercises, since these are designed to help you prepare for other assessments in the course.

• There is no late work accepted for exams.

• The late work policy for the project will be provided with the project instructions.

Waiver for extenuating circumstances

If there are circumstances that prevent you from completing a reading quiz or homework assignment by the stated due date, you may email me (gchism@arizona.edu) before the deadline to waive the late penalty. In your email, you only need to request the waiver; you do not need to provide explanation. This waiver may only be used for once in the semester, so only use it for a truly extenuating circumstance.

If there are circumstances that are having a longer-term impact on your academic performance, please let your academic dean know, as they can be a resource. Please let me know if you need help contacting your academic dean.

Regrade requests

Regrade requests must be submitted on GitHub within a week of when an assignment is returned. Regrade requests will be considered if there was an error in the grade calculation or if you feel a correct answer was mistakenly marked as incorrect. Requests to dispute the number of points deducted for an incorrect response will not be considered. Note that by submitting a regrade request, the entire question will be graded which could potentially result in losing points.

No grades will be changed after the project presentations.

“Incomplete” grade

The grade of “I” may be awarded only at the end of a term, when all but a minor portion of the course work has been satisfactorily completed. The grade of I is not to be awarded in place of a failing grade or when the student is expected to repeat the course; in such a case, a grade other than I must be assigned. Students should make arrangements with the instructor to receive an incomplete grade before the end of the term. If the incomplete is not removed by the instructor within one year the I grade will revert to a failing grade.

Tutoring

Tutoring can be found through the U of A Think Tank.

Attendance policy

Responsibility for class attendance rests with individual students. Since regular and punctual class attendance is expected, students must accept the consequences of failure to attend.

However, there may be many reasons why you cannot be in class on a given day, particularly with possible extra personal and academic stress and health concerns this semester. I am always available to personally catch you up from missed lectures. Please contact me directly about these additional “office hours”.

Note that attendance and participation is part of your grade as well.

Attendance policy related to COVID symptoms, exposure, or infection

Student health, safety, and well-being are the university’s top priorities. To help ensure your well-being and the well-being of those around you, please do not come to class if you have symptoms related to COVID-19, have had a known exposure to COVID-19, or have tested positive for COVID-19. If any of these situations apply to you, you must follow university guidance related to the ongoing COVID-19 pandemic and current health and safety protocols. If you are experiencing any COVID-19 symptoms, contact student health at (520) 621-9202. To keep the university community as safe and healthy as possible, you will be expected to follow these and the university guidelines on COVID-19 mitigation. Please reach out to me and your academic dean as soon as possible if you need to quarantine or isolate so that we can discuss arrangements for your continued participation in class.

Notify your instructor(s) if you will be missing up to one week of course meetings and/or assignment deadlines

If you must miss the equivalent of more than one week of class and have an emergency, the Dean of Students is the proper office to contact (DOS-deanofstudents@email.arizona.edu). The Dean of Students considers the following as qualified emergencies: the birth of a child, mental health hospitalization, domestic violence matter, house fire, hospitalization for physical health (concussion/emergency surgery/coma/COVID-19 complications/ICU), death of immediate family, Title IX matters, etc.

Please understand that there is no guarantee of an extension when you are absent from class and/or miss a deadline.

Accessibility

Accessibility and Accommodations: At the University of Arizona, we strive to make learning experiences as accessible as possible. If you anticipate or experience barriers based on disability or pregnancy, please contact the Disability Resource Center (520-621-3268, https://drc.arizona.edu) to establish reasonable accommodations.

Note: If you’ve read this far in the syllabus, email me a picture of your pet if you have one or your favorite meme!

Additional university policies

Additional policies can be found at this link (please read through them): https://catalog.arizona.edu/syllabus-policies

Safety on Campus and in the Classroom

For a list of emergency procedures for all types of incidents, please visit the website of the Critical Incident Response Team (CIRT): https://cirt.arizona.edu/case-emergency/overview

Also watch the video available at https://arizona.sabacloud.com/Saba/Web_spf/NA7P1PRD161/common/learningeventdetail/crtfy000000000003560

Important dates

• Monday, August 26: Classes begin, Monday schedule
• Monday, September 02: Labor Day, no class; Drop/add ends
• Wednesday, January 17: Drop/add ends
• Sunday, September 22: Last day to drop without a W (withdraw)
• Sunday, November 03: Last day to withdraw from a class online through UAccess
• Monday, November 11: Veteran’s Day, no class
• Wednesday, December 11: Last day of class, no registration changes can be made
• Friday, December 13: Project presentations, due by 5pm AZ time

For more important dates, see the full U of A Academic Calendar.

Graduate Student Resources

University of Arizona’s Basic Needs Resources page for graduate students: http://basicneeds.arizona.edu/index.html

Footnotes

1. These guiding principles are based on Course Policies related to ChatGPT and other AI Tools developed by Joel Gladd, Ph.D.