Objectives

• Communicate highlights of the Syllabus
• Activity: Introductions

Course Information

• Number: MTH-391
• Title: Fundamentals of Data Science
• Section: A
• Term: Spring 2025
• Credits: 3
• Instructor: Dr. Quijano
• Schedule: Monday, Wednesday, Friday at 1:35 PM to 2:30 PM
• Location: Franz Hall 125

About the Instructor

Dr. Alex John Quijano

• Office: Buckley Center 279
• Email: quijano@up.edu
• Education and Profession: Applied Mathematics
• Teaching: Probability and statistics, data science, linear algebra, calculus, differential equations
• Research: Mathematical interdisciplinary research in data science, modeling of natural languages, social and biological systems
• Hobbies and Interests: Movies, anime, musicals, coffee
• Other Facts: Two cats

Modes of Communication

Teams

We will be using Teams as the main real-time communication tool for general announcements, question-answering discussions, and direct messages.

The Teams for this course is MTH-391A-sp25.

Class Website

The syllabus, tentative topics schedule, and other information are posted on the course website.

You can access the course website at mth-391a-sp25.

Note that the course website can be accessed in Teams.

Structure and Expectations

Class Structure

• Pre-reading
• Short lecture
• Worksheet activity
• Group presentations or discussions

Assignment Expectations

• Mini-Assignments
• Project Phases

Assignments

Mini-Assignments: Wednesday and Friday (due in class)

The purpose of the mini-assignments is for in-class group work and activities.

Project Phases: Every Monday (due next Monday)

The purpose of the project phases is to break the semester-long project into smaller tasks, allowing for incremental progress toward its completion.

Submission Guidelines

Moodle

We will be using the course Moodle page for submitting assignments and giving feedback.

The Moodle for this course is MTH-391A - sp25 - Fundamentals of Data Science. You are already listed in Moodle for this course using your UP account.

Resources

Textbooks

This course uses the following textbooks - which are free to access - for reading and practice.

• Baumer BS, Kaplan DT, orton, Nicholas J (2017). Modern data science, with R. Chapman and Hall/CRC. https://mdsr-book.github.io/mdsr3e/.
• Wickham H, Çetinkaya-Rundel M, rolemund, Garrett (2023). R for data, science. O’Reilly Media, Inc. https://r4ds.hadley.nz/.
• VanderPlas, Jake (2016). Python data science handbook: Essential tools, for working with data. O’Reilly Media, Inc., https://jakevdp.github.io/PythonDataScienceHandbook/.

Note that the textbook can be accessed in Teams.

Tools

R and Python

We will be using R and Python, as a computational tools, for some assignments. These tools are free and open-source.

We will use the Posit Cloud service, an online computing environment.

We will discuss more about these tools next time.

Academic Support

Help Hours

Dr. Alex John Quijano

• 4:00 PM - 5:00 PM Mondays to Fridays, Buckley Center 279
• One-to-One, Buckley Center 279 or in Teams
  Click on the link to sign-up for a 15-minute session.

My walk-in help hours start week 2.

Math Resource Center

The Math Resource Center offiers walk-in tutoring and appointment-based tutoring.

Activity

Introductions

On a piece of paper:

• Write your name, year, and major.
• Discuss your background experience in computer programming.
• Discuss your anxieties and fears about computer programming.
• Discuss your hobbies.
• Write an encouraging message to yourself six weeks from now.

Share what you wrote ot the class.

Submit your work.