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INTRODUCTION TO �DATA SCIENCE

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– FARDINA FATHMIUL ALAM

(fardina@umd.edu)

CMSC/DATA 320

Fall 2026

Course Logistics

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THIS COURSE

You’ll learn to take data:

• Process it
• Visualize it
• Understand it
• Communicate it
• Extract value from it

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Course Website: https://cmsc320.github.io/

Course Textbook: https://ffalam.github.io/CMSC320TextBook/index.html (Some Chapters will be added eventually)

Piazza: https://piazza.com/umd/fall2026/cmsc3200301/home

ELMS: Everyone should be registered automatically.

Gradescope: Everyone registered in ELMS should be added automatically.

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Who Am I

• PhD in CS, George Mason University, VA
• Professor at UMD CS since 2023
• Program Director, Data Science Program, Science Academy
• Research Interests: Computational Biology, Generative AI, NLP, Data Science

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COURSE STAFFS

Instructor: Dr. Fardina Fathmiul Alam

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TAs: check course website

• Office location: Online and in person
• Office hours: Will be posted on Piazza/ELMS and the course website

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Please check ELMS and Piazza regularly for TA office hours and updates.

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PREREQUISITE KNOWLEDGE

Aimed at CMSC undergraduate – but likely accessible to others with programming experience and mathematical maturity.

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We do not assume:

• Experience with Python, NumPy, pandas, scikit-learn, PyTorch, matplotlib, etc …
• Deep statistics or any ML knowledge
• Database or distributed systems knowledge

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We do assume:

• You want to be here!

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PREREQUISITE KNOWLEDGE

This course is designed for CMSC/DATA undergraduates and assumes general programming and mathematical maturity.

You do not need prior experience with:

• pandas, NumPy, scikit-learn, PyTorch, or matplotlib
• Machine learning or advanced statistics
• Databases or distributed systems

Helpful:

• Basic programming experience
• Some familiarity with Python, since we will use it throughout the course
• Comfort using documentation to learn new tools as needed

Most important:

• Be willing to learn, experiment, and ask questions!

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How To Succeed in this Course

• Attend class
• Classes will normally meet in person.
• In exceptional circumstances, we may switch to an online format. Any change will be announced in advance.
• Participate and take notes.
• Start assignments early.
• Do the readings.
• Be comfortable with open-ended assignments and questions.
• Check the course website, ELMS, and Piazza regularly for updates.

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“Go to class! College isn’t that hard if you actually, you know, show up!”

– Andrew, Financial Analyst, University of Wisconsin Madison, Class of 1993, from The Advice I’d Give My College Freshman Self, PBS News Hour

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Course Setup

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Exam Dates Information

Location: IRB 0324

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1. Midterm Exam 1: Thursday, Oct 15 (Class time)
2. Midterm Exam 2: Thursday, Nov 19 (Class time)
3. Final Exam: Monday, Dec 21
1. Time: 1:30 p.m. – 3:30 p.m.
2. Reference: UMD Fall 2026 Final Exam Schedule

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Other Logistics

Slides

Will be posted on our course website (https://cmsc320.github.io/ ) daily basis (before the class)

• Memory tool, not a substitute for lectures
• Take your own notes
• Do the reading

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Assignments and Final Tutorial

Will be posted in ELMS and you need to submit the assignment in the Gradescope.

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Class Recording

Class sessions will be recorded and posted in Panepto in ELMS (Request basis).

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Piazza

• Use for course questions, clarifications, and discussion
• Search before posting
• Use a public post if the answer could help others
• Use a private post for personal or grade-specific matters
• You may post anonymously to classmates only
• Do not post assignment solutions or code

ELMS

• Homework/assignment instructions will be posted in ELMS
• Check ELMS for deadlines, announcements, and course materials

Gradescope

• Submit homework/assignments through Gradescope
• Make sure you submit the correct file/version
• If multiple submissions are allowed, the last submission will be graded

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Piazza, ELMS & Gradescope

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Rule of thumb:�If it could help the whole class, post publicly. If it is personal or grade-specific, post privately.

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How to Contact the Course Staff

• TAs: Will help you with Homework/ project details and course content.
• Best way to contact: Office hours, piazza
• Me: Will help you with course contents, data science related issues, career stuff, or high level course issues
• Best way to contact: Email, office hours
• Do not send me an email through ELMs
• Do not ask me questions over piazza
• If it is urgent, add [URGENT] to the subject line
• Email an instructor (TA or professor) with [CMSC320] in the email subject line.

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No TA office hours in first week of the semester.

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Course Logistic Support

We have a specific logistic form and a course email:

• Submit logistics, grading, and extension requests through the CMSC320 Logistics Form (Posted in Course Website https://cmsc320.github.io/ ).
• If you do not receive a response or your issue is not resolved within 48 hours, email:
• cmsc320gradingissues.dr.fardina@gmail.com
• For last-minute extension requests, use the same course email.

Please do not send these requests to my personal UMD email; they have a mysterious tendency to disappear into the abyss of my inbox. 😄

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Late Policy

Homework

• Up to 24 hours late: 15% penalty
• More than 24 hours late: 0 / not accepted
• Multiple submissions are allowed; only the last submission will be graded

Group Project/Tutorial

• Checkpoints 1 & 2: up to 24 hours late with a 20% penalty
• More than 24 hours late: 0 / not accepted
• Final submission / Checkpoint 3: no late submission allowed

Note: Any optional bonus activity, if offered, must be submitted by its posted deadline; late submissions will not be accepted.

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Extension Requests

Extension requests will be handled on a case-by-case basis.

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• Things that will probably get you an extension:
• Documented illness or mental health concerns
• Family emergencies or bereavement
• Things that are less convincing:
• Your partner/friend is in town
• You forgot you were taking this course
• Ask anyway if something genuinely unexpected comes up!

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How to request an extension in advance: Submit your request through the CMSC320 Logistics Form

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POTENTIAL BONUS OPPORTUNITIES

1. Top 3 Data Science Group Projects: The top three group projects in the course may receive special recognition and/or bonus points. However, bonus points are not guaranteed; if no project meets the expected standard, we reserve the right not to award them.
2. Tentative Additional Bonus Opportunities: Additional opportunities to earn bonus points may be offered on assignments, exams, or through course participation. For example, students who consistently attend class, actively participate, and provide helpful support to classmates on Piazza may be recognized. Any such opportunities will be announced during the semester.

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Important: Please do not rely on bonus opportunities when planning your grade, as they are tentative and may not be offered. Your primary focus should be on the regular graded course activities, including assignments, projects, exams, and other required work.

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SOME TECHNOLOGIES WE MIGHT USE (MOSTLY)

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IMPORTANT WALLS OF TEXT

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ANTI-HARASSMENT

The open exchange of ideas and the freedom of thought and expression are central to our aims and goals. These require an environment that recognizes the inherent worth of every person and group, fosters dignity, understanding, and mutual respect, and embraces diversity.

For these reasons, we are dedicated to providing a harassment-free experience for participants in and out of this class.

Harassment is unwelcome or hostile behavior, including speech that intimidates, creates discomfort, or interferes with a person’s participation or opportunity to participate.

(Adapted from ACM SIGCOMM’s policies)

Common Sense!

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• Respect everyone — students, TAs, and instructors
• Use respectful and appropriate language
• Open discussion is welcome
• Harassment is not
• Everyone should feel safe and respected when participating

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ACADEMIC INTEGRITY

Any assignment or exam that is handed in must be your own work (unless otherwise stated). However, talking with one another to understand the material better is strongly encouraged. Recognizing the distinction between cheating and cooperation is very important. If you copy someone else's solution, you are cheating. If you let someone else copy your solution, you are cheating, including posting solutions online in a public place. If someone dictates a solution to you, you are cheating.

Everything you hand in must be in your own words and based on your own understanding. If someone helps you understand the problem during a high-level discussion, you are not cheating. We strongly encourage students to help one another understand the material presented in class, in the book, and general issues relevant to the assignments.

When taking an exam, you must work independently. Any collaboration during an exam will be considered cheating. Any student who is caught cheating will be referred to the University Office of Student Conduct and may receive serious course consequences. Please don't take that chance — if you're having trouble understanding the material, please ask for help.

Common Sense!

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• Submit your own work
• Discussion is encouraged; copying is not
• Do not share or post solutions
• Exams and HWs must be completed independently
• When in doubt, ask the instructor or TA
• Cheating may result in a zero/F and referral to the Office of Student Conduct

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AI Tools Policy

Use of AI tools (e.g., ChatGPT, Copilot, Bard, Claude, etc.) is not permitted for assignments, exams, or projects in this course.�Assignments must reflect your own work, reasoning, and coding.

• Submissions generated or substantially assisted by AI tools will receive a score of 0.
• Academic violations will be reported through the university process.
• Applies to Google Colab and other online platforms — AI assistance must be turned off.

Exception:

• AI tools may only be used to brainstorm ideas for the final group project.
• If used, you must declare and cite the tool in project checkpoints.
• No AI-generated content/code may appear in the final deliverable.

Bottom Line: If you didn’t write it, code it, or think it → don’t submit it.

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• Rationale: Build your own skills, analysis, creativity, and voice.
• Consequences:�0 on the affected work + possible Honor Code referral.
• Questions? Email: fardina@umd.edu

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Next Topics

• Data Types (Chapter 2, Self Study)

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