CMSC H260: Foundations of Data Science

(Spring 2025)

Course Info | Schedule | Grading
Academic Integrity | Piazza | Accommodations | Title IX | Links
CMSC H260 Data Science

Course Information

Course: TuTh 10–11:30am
Professor: Sara Mathieson
Office: KINSC L302
Office hours: Mondays 12-1pm (in L302) and Wednesdays 4-5pm (over Zoom)
TAs: Edgar Leon, Rachel November, Danylo Shudrenko
TA hours: Mondays 8-10pm, Wednesdays 6-8pm, Thursdays 8-10pm (all in H110)
Peer Tutors: Darshan Mehta, Fejiro Anigboro, Lei Lei

The prerequisites for this course are Calculus I, Data Structures, and Discrete Mathematics (this last is a co-req).

This course will introduce core principles of learning from data. More and more decisions are being made by algorithms that operate on large datasets, and this course will give students the tools to understand and contribute to this process. Throughout we will emphasize the ethical use of data and analyze case studies of how data science has intersected with society. This course will have a significant theory component, covering introductory linear algebra, probability, statistics, modeling, information theory, and optimization. However, we will also implement these ideas (in Python) and apply them to concrete datasets from a variety of fields (including images, video, text, DNA, music, art, etc).

The language for this course is Python 3.

Textbook:

You do not need to purchase a textbook for this course. We will draw from several online textbooks, as well as supplemental online readings and research papers.


See the Schedule for each week's reading assignment. The schedule is tentative and subject to change throughout the semester.

Schedule (Tentative)

WEEK DAY ANNOUNCEMENTS TOPIC & READING LABS
1

Jan 21

  

Introduction to Data Science and Python

  • What can we learn from data?
  • Representing data
  • Crash course on Python
  • Numpy
  • Matplotlib (plotting in Python)
  • Classes and objects in Python
  • Dictionaries

Reading:

  • MML Chap 1

Tues:

Thurs (virtual asynchronous class):

Jan 23

  
2

Jan 28

  

Introduction to Modeling

  • What is a model?
  • Linear models
  • Polynomial models
  • Assessing model fit and complexity
  • Using models for prediction

Reading:

  • MML Chap 8.1, 8.2.1-8.2.3

Tues:

Thurs:

Lab 1: Computing and plotting in Python
Due: Wed Jan 29 at 11:59pm

Jan 30

  
3

Feb 04

  

Applied Linear Algebra and Optimization

  • Matrices and vectors
  • Representing data as a matrix
  • Matrix operations including dot products
  • Analytic solution for linear regression
  • Model fitting as a numerical optimization problem
  • Introduction to gradients
  • Gradient descent
  • Application to linear regression
  • Discussion of optimization in other contexts

Reading:

  • Daume Chap 7.1-7.6
  • (optional) MML Chap 2.1-2.2, 2.5, 2.7.1
  • (optional) MML Chap 9-9.2
  • (optional) MML Chap 7-7.1

Tues:

Thurs:

Lab 2: Modeling climate change
Due: Wed Feb 5 at 11:59pm

Feb 06

Last day to drop (Feb 07)
4

Feb 11

  

Evaluation Metrics

  • Precision and recall
  • Specificity and sensitivity
  • Confusion matrices
  • ROC curves
  • Introduction to probability
  • Bayes rule

Reading:

Tues:

Thurs:

Lab 3: Gradient descent
Due: Wed Feb 12 at 11:59pm

Feb 13

  
5

Feb 18

  

Ethics: Disparate Impact (+ review)

  • Probability in clinical trials
  • Introduction to algorithmic bias
  • Redundant encoding of protected features
  • Midterm I review

Reading:

Tues:

Thurs:

Lab 4: Evaluation Metrics
Due: Wed Feb 19 at 11:59pm

Feb 20

  
6

Feb 25

  

Probabilistic modeling I

  • Continue review
  • Begin: Naive Bayes algorithm

Reading:

  • MML Chap 6.1-6.3
  • Daume Chap 9.1-9.4

Tues:

Thurs:

Feb 27

  
7

Mar 04

  

Probabilistic modeling II

  • Continue Naive Bayes
  • Logistic regression

Reading:

Midterm 1
In-class Tues Mar 4

Mar 06

  
 

Mar 11

Spring Break

Mar 13
8

Mar 18

  

Information theory

  • Introduction to information theory
  • Entropy
  • Coding theory
  • Discuss applications of entropy in machine learning

Reading:

Tues:

Thurs:

Lab 5: Naive Bayes
Due: Wed Mar 19 at 11:59pm

Mar 20

  
9

Mar 25

  

Visualization

  • Principles of visualization data
  • Discrete vs. continuous data
  • Types of graphs (bar chart, scatter plot, heatmap, etc)
  • Visualizing graphs
  • Principal components analysis
  • Interactive visualization basics

Reading:

Tues:

Thurs:

Lab 6: Information Theory
Due: Wed Mar 26 at 11:59pm

Mar 27

  
10

Apr 01

  

Introduction to statistics

  • Introduction to statistics
  • Hypothesis testing
  • p-values
  • Normal distributions
  • Permutation testing
  • Bootstrap

Reading:

Tues:

Thurs:

Lab 7: Logistic Regression and Visualization + Project Proposal
Due: Wed Apr 2 at 11:59pm

Apr 03

  
11

Apr 08

  

Midterm II review

  • Review

Tues:

Thurs:

Lab 8: Statistics and Visualization
Due: Thurs Apr 10 at 11:59pm

Apr 10

  
12

Apr 15

  

Unsupervised learning

  • Clustering (K-means and Gaussian Mixture Models)
  • Dimensionality Reduction (PCA and t-SNE)

Reading:

  • Daume Chap 15 (K-means, PCA)
  • MML Chap 10 (PCA), Chap 11 (GMM)
  • (optional) GMM tutorial

Tues:

Midterm 2
In-class Thurs Apr 17

Apr 17

  
13

Apr 22

  

Intro to neural networks

  • Neural networks
  • Deep learning
  • Applications
  • Missing data

Reading:

Final Project

Apr 24

  
14

Apr 29

  

Project Presentations

  • Final project presentations

May 01

Last day to pass/fail (May 02)

Grading Policies

Grades will be weighted as follows:
35% Lab assignments
20% Midterm I
20% Midterm II
15% Final Project (including presentation)
10% Participation (including attendance and note-taking)

Quizzes and Exams

In lieu of reading quizzes this semester, we will have short excercises during class (to work on and discuss, not turn in). Be ready to work on these exercises by completing the weekly reading before class on Thursdays.

There will be two in-class midterms (85 minutes total). In lieu of a final exam, there will be a final project and associated presentation. You must pass at least one exam to pass the course overall.

Labs

Our labs are on Tuesdays. Lab assignments will generally be released Wednesday night and due the following Wednesday at midnight. Lab attendance is required, and missing labs will quickly affect your participation grade. There will sometimes be pair-programming warm-up exercises as part of the lab, and lab in general is a time to build community around the course and the material. Note that Wednesdays and Fridays I will be doing research off campus and unable to answer lab questions. Make use of office hours (both mine and the TAs) and Piazza.

Weekly Lab Sessions
Lab A 2—3pm Tuesdays Mathieson H110
Lab B 3—4pm Tuesdays Mathieson H110

Handing in labs: Lab assignments are submitted electronically and managed using github classroom. You may submit your assignment multiple times, but each submission overwrites the previous one and only the final submission will be graded. Some of the programming/lab assignments may be in pairs. There may also be some written assignments that will have specific instructions for handing in.


Late Policy: Each individual will be given 2 late days for the semester. A late day is a 24 hour extension from the original deadline. You can use up to one late days on any one assignment. This will encompass any reason - illness, interviews, many midterms in the same week, etc. Past these days, late assignments will not be accepted. You should budget your days to account for future illnesses or assignment deadlines for other courses. Even if you do not fully complete a lab assignment you should submit what you have done to receive partial credit. Late days count against both partners in a group lab.

For extensions beyond these 2 late days (in the case of an emergency or ongoing personal issue), please contact your Class Dean. If your Class Dean notifies me of the issues, then we can arrange an accommodation.


Academic Integrity

From the faculty:

In a community that thrives on relationships between students and faculty that are based on trust and respect, it is crucial that students understand a professor's expectations and what it means to do academic work with integrity. Plagiarism and cheating, even if unintentional, undermine the values of the Honor Code and the ability of all students to benefit from the academic freedom and relationships of trust the Code facilitates. Plagiarism is using someone else's work or ideas and presenting them as your own without attribution. Plagiarism can also occur in more subtle forms, such as inadequate paraphrasing, failure to cite another person's idea even if not directly quoted, failure to attribute the synthesis of various sources in a review article to that author, or accidental incorporation of another's words into your own paper as a result of careless note-taking. Cheating is another form of academic dishonesty, and it includes not only copying, but also inappropriate collaboration, exceeding the time allowed, and discussion of the form, content, or degree of difficulty of an exam. Please be conscientious about your work, and check with me if anything is unclear.

Please also note the CS Department Collaboration Policy.

More details for this course:

Under no circumstances may you hand in work done with (or by) someone else under your own name. Your code should never be shared with anyone; you may not examine or use code belonging to someone else, nor may you let anyone else look at or make a copy of your code. This includes, but is not limited to, obtaining solutions from students who previously took the course or code that can be found online. You may not share solutions after the due date of the assignment.

Discussing ideas and approaches to problems with others on a general level is fine (in fact, we encourage you to discuss general strategies with each other), but you should never read anyone else's code or let anyone else read your code. All code you submit must be your own with the following permissible exceptions: code distributed in class, code found in the course text book, and code worked on with an assigned partner. In these cases, you should always include detailed comments that indicates on which parts of the assignment you received help, and what your sources were.

Github copilot (or any other software for automaticallly generating code) is not allowed for this course, until the final project. The reasoning behind this decision is that code generation tools often create code that is not well understood by the user. Often this code becomes incorrect in the larger context of the program. However, for the final project you are welcome to use Github copilot, and you'll be asked to reflect on your experience.


Piazza

This semester we'll be using Piazza, an online Q&A forum for class discussion, help with labs, clarifications, and announcements. You will receive an email invitation to join CMSC H260 on Piazza. If you don't, please let me know.

Piazza is meant for questions outside of regular meeting times such as office hours, class, and lab. Please do not hesitate to ask and answer questions on Piazza, but keep in mind the following guidelines:

  1. Piazza should be used for ALL content and logistics questions outside of class, lab, and office hours. Please do not email me your code or extended questions about the assignments.
  2. If there is a personal issue that relates only to you, please email me.
  3. We encourage non-anonymous posts, but you may post anonymously (to your classmates, not the instructors).
  4. Do not post long blocks of code on Piazza - if you can distill the problem to 1-2 lines of code and an error message, that’s fine, but try to avoid giving out key components of your work.
  5. By the same token, when answering a question, try to give some guiding help but do not post code fixes or explicit solutions to the problem.
  6. Posting on Piazza counts toward your participation grade, both asking and answering!

Haverford Academic Accommodations Statement

For details about the accommodations process, visit the Access and Disability Services website.

We are committed to partnering with you on your academic and intellectual journey. We also recognize that your ability to thrive academically can be impacted by your personal well-being and that stressors may impact you over the course of the semester. If the stressors are academic, we welcome the opportunity to discuss and address those stressors with you in order to find solutions together. If you are experiencing challenges or questions related to emotional health, finances, physical health, relationships, learning strategies or differences, or other potential stressors, we hope you will consider reaching out to the many resources available on campus. These resources include CAPS (free and unlimited counseling is available), the Office of Academic Resources, Health Services, Professional Health Advocate, Religious and Spiritual Life, the Office of Multicultural Affairs, the GRASE Center, and the Dean’s Office. Additional information can be found here.

Additionally, Haverford College is committed to creating a learning environment that meets the needs of its diverse student body and providing equal access to students with a disability. If you have (or think you have) a learning difference or disability – including mental health, medical, or physical impairment – please contact the Office of Access and Disability Services (ADS) at hc-ads@haverford.edu. The Director will confidentially discuss the process to establish reasonable accommodations. It is never too late to request accommodations – our bodies and circumstances are continuously changing. Students who have already been approved to receive academic accommodations and want to use their accommodations in this course should share their accommodation letter and make arrangements to meet with me as soon as possible to discuss how their accommodations will be implemented in this course. Please note that accommodations are not retroactive and require advance notice in order to successfully implement.

If, at any point in the semester, a disability or personal circumstances affect your learning in this course or if there are ways in which the overall structure of the course and general classroom interactions could be adapted to facilitate full participation, please do not hesitate to reach out to us.

It is a state law in Pennsylvania that individuals must be given advance notice that they may be recorded. Therefore, any student who has a disability-related need to audio record this class must first be approved for this accommodation from the Director of Access and Disability Services and then must speak to me. Other class members need to be aware that this class may be recorded.

Haverford Title IX Statement

Haverford College is committed to fostering a safe and inclusive living and learning environment where all can feel secure and free from harassment. All forms of sexual misconduct, including sexual assault, sexual harassment, stalking, domestic violence, and dating violence are violations of Haverford’s policies, whether they occur on or off campus. Haverford faculty are committed to helping to create a safe learning environment for all students and for the College community as a whole. If you have experienced any form of gender or sex-based discrimination, harassment, or violence, know that help and support are available. Staff members are trained to support students in navigating campus life, accessing health and counseling services, providing academic and housing accommodations, and more.

The College strongly encourages all students to report any incidents of sexual misconduct. Please be aware that all Haverford employees (other than those designated as confidential resources such as counselors, clergy, and healthcare providers) are required to report information about such discrimination and harassment to the Bi-College Title IX Coordinator.

Information about the College’s Sexual Misconduct policy, reporting options, and a list of campus and local resources can be found on the College’s website here.


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