B.S. Honors Program
The Data Science B.S. with Honors provides students with an opportunity to deepen their knowledge of data science and its applications, engaging in advanced learning, independent studies, and research.
In addition to meeting all requirements for the BS in Data Science, the student must:
- Maintain a GPA of at least 3.5 in coursework in the major.
- Choose a concentration, describe it in the honors proposal, and have it approved by their faculty advisor. Submit the honors proposal by the final study list deadline at least two quarters before they intend to graduate (typically autumn quarter during the senior year).
- Complete 15 units of relevant upper-level coursework, as outlined in the honors proposal, including 6 units of independent work.
Here are some guidelines on concentration, class choices, and the honors proposal.
- Given the interdisciplinary nature of Data Science and of the major, achieving greater depth and understanding in one area of data science will typically require taking additional courses. These upper level classes will be chosen in collaboration with the major advisor and will constitute a coherent concentration in an area of interest. Note that the chosen area can be in the foundational disciplines of data science (allowing students to engage more closely in the development of new methods) or in applications (giving students the opportunity to become intimately familiar with a class of the scientific/operational challenges that might be addressed with data science).
- At least 6 of the 15 units need to be obtained with an educational experience that requires substantial independent work: a small group seminar (e.g. BIODS 290 or Stats 319), a research-based course, an independent reading/research course (e.g. DATASCI 199)
- The faculty supervising the student in this independent effort will be the “research advisor” for the student and will need to be identified in the honors plan.
- While not required to, students can elect to write a thesis, under the supervision of their research advisor, after obtaining the program approval.
- If a student is doing a thesis, they can take DATASCI 120 (Data Narratives) while they write their thesis. This course can count toward the 15 honors units and double-count as the WIM. This is the only instance in which a course can double count between a regular major requirement and honors requirements.
- If doing a thesis, and with the major advisor’s approval, up to 15 research units can be used to complete the upper level course requirement. If not doing a thesis, students who are interested in completing more than 6 units of independent work need to obtain permission from the major advisor.
- Assemble a final portfolio which showcases the student’s achieved ability to think independently and creatively using the tools of data science. Specific instructions for the portfolio will be provided to students (including timeline for submission and guidelines). The portfolio will have two components:
- Documentation of the independent work experience (poster, video of oral presentation, paper or honors thesis, etc.). Note that support for thesis writing (if this option is selected) will be provided in the class DATASCI 120 (Data Narratives).
- A self-reflection essay, in which students must do the following:
- Explain their choice for honors concentration topic, how it relates to their interests, and how it fits into Data Science.
- Describe how each course selected added to the student’s knowledge and understanding in the area chosen for honors concentration. How did the skill and knowledge acquired via the honors curriculum contribute to the student’s understanding of data science and their self-positioning in this field?
- Participate in the annual Data Science Capstone Showcase in May or June, where all students who have completed a capstone experience will share their learnings with posters, oral presentations, or other media.
Honors Concentration Examples
Computational Neuroscience and Artificial Intelligence
- CS 231N: Convolutional Neural Networks for Visual Recognition (4 units)
- CS 228: Probabilistic Graphical Models (4 units)
- PSYCH 164: Brain Decoding (3 units)
- CS 199: Independent Work (6 units, spread over 2 quarters)
Computational Policy: Risk Assessment in the Criminal Justice System (with Thesis)
- MS&E 330: Law, Order, & Algorithms (3 units)
- MS&E 408: Directed Reading and Research (9 units, spread over 3 quarters)
- LAW 806: The Future of Algorithms (3 units)
Energy & Environmental Systems Modeling
- ENERGY 291: Optimization of Energy Systems (4 units)
- GEOLSCI 240: Data Science for Geoscience (3 units)
- MS&E 394: Advanced Methods in Modeling for Climate and Energy Policy (3 units)
- ESS 292: Directed Individual Study in Earth Systems Science (6 units)
Frontiers in Social Science Research: Processing of Emerging Data Forms
- CS 224N: Natural Language Processing (4 units)
- CS 231N Convolutional Neural Networks for Visual Recognition (4 units)
- EE 368: Digital Image Processing (3 units)
- DATASCI 199: Independent Study (6 units, spread over 2 quarters)
Machine Learning for Content Recommendation
- CS 246: Mining Massive Data Sets (4 units)
- CS 236: Deep Generative Models (3 units)
- CS 229: Machine Learning (4 units)
- CS 199: Independent Research (6 units, spread over 2 quarters)
- STATS 240: Statistical Methods in Finance (3 units)
- MATH 238: Mathematical Finance (3 units)
- ECON 139D: Directed Reading (3 units)
- MS&E 448: Big Financial Data and Algorithmic Trading (3 units)
- DATASCI 199: Independent Study (3 units)
Optimization for Randomized Control Trials (with Thesis)
- STATS 199: Independent Study (16 units, spread over 4 quarters)
Social and Ethical Implications of Machine Learning Algorithms
- CS 230: Deep Learning (4 units)
- LINGUIST 280: From Languages to Information (4 units)
- INTLPOL 358: Business, Social Responsibility, and Human Rights (3 units)
- DATASCI 199: Independent Study (6 units, spread over 2 quarters)
- STATS 100: Mathematics of Sports (3 units)
- ECON 139D: Directed Reading on Sports Economics (3 units)
- GSBGEN 360: Sports Business Management (4 units)
- GSBGEN 339: Negotiation Dynamics in Sports, Entertainment, and Media (4 units)
- STATS 199: Independent Study (3 units)
- STATS 371: Applied Bayesian Statistics (3 units)
- CS 234: Reinforcement Learning (3 units)
- MATH 256A: Partial Differential Equations (3 units)
- STATS 199: Independent Study (6 units, spread over 2 quarters)
How to Declare Honors
1. Ensure you meet the GPA requirement.
- Make sure you have a GPA of at least 3.5 in coursework in the major. This means that when you look at your grades earned in all major classes, the average is at least 3.5. (It's okay if there's an occasional class with a lower grade, as long as the average is at least 3.5.)
2. Create and submit your honors proposal. (Requires approval from faculty advisor and research advisor.)
- Choose a concentration.
- Carefully read the information and instructions for the Data Science B.S. Honors Proposal. Follow those instructions to draft your proposal.
- Consult with your data science major faculty advisor, finalize your plan, and get their approval. When they approve your plan, they will sign your proposal.
- Find a research advisor for the independent work you plan to do as part of your honors program. Have a conversation with them and get their approval. When they approve your plan, they will sign your proposal.
- Submit your Honors Proposal via this Google form by the final study list deadline at least two quarters before you intend to graduate.
3. Declare Honors in Axess.
- After you’ve submitted your proposal, declare Honors in Axess.
- Email the datasciencemajor-inquiries [at] lists.stanford.edu (student services team) to let them know so that they can approve your proposal and declaration.