Courses | Capstone Projects | Experiential Learning

D2K Academics

Want to make an impact by working on real-world data science problems? Apply for the advanced or intro-level data science courses for Fall 2021!

Advanced D2K Courses:

DSCI 400, Data Science Capstone Lab

Instructor: Arko Barman

Description: In this project-based course, student teams will choose, define, and execute semester-long data-science and machine-learning research projects. These projects may be selected from a variety of disciplines and industries, where freedom is given in defining the projects. The course is about learning best practices in data science and machine learning while finding a suitable curiosity-driven project to build these methods and systems around.

  • Intended for DSCI minor students and non-seniors who have some background in data science and want to learn how to design a data science pipeline to answer questions and tell a story through data.

Prerequisites: (DSCI 301 OR STAT 315 OR STAT 310 OR ECON 307) AND (DSCI 302 OR COMP 330) AND (DSCI 303 OR STAT 413 OR COMP 540) AND DSCI 304

Course Meeting: 1:30 PM - 2:50 PM Tuesdays & Thursdays

Location: Online

DSCI 435 / 535 & COMP 449 / 549, Applied Machine Learning & Data Science Projects

Instructors: Genevera Allen, Su Chen and Arko Barman

Description: In this course, students will work on interdisciplinary teams to solve a real-world data science project with clients from a variety of industries and disciplines.

  • Intended for seniors (some advanced juniors), professional masters, and beginning PhD students who have advanced coursework and expertise in some area of data science and want to complete a challenging, real-world data science project.

  • Counts as a DSCI minor capstone, Stat senior capstone, MECE capstone, CAAM senior capstone, and as an elective for ELEC, COMP, STAT, and CAAM undergrad and grad students.

  • We anticipate having about 15 projects in the spring. These are industry-sponsored (finance, energy, healthcare, tech), community impact projects (government and non-profits), and research projects (working with Rice or medical center faculty).

  • A course application and project preference form will be due the first week of classes to determine projects and teams. Students who do not meet the course prerequisites may be asked to enroll in DSCI 400.

Course Meeting: 5:00 PM - 6:30 PM on Mondays & Wednesdays

Location: Online

Learn more on the Data Science Capstone page >>

View past projects from the D2K Capstone program >>

DSCI 415 / 515, Data Science Consulting

Instructors: Arko Barman and Su Chen

Description: Students in this course advise clients from Rice and beyond in a data science consulting clinic, learn best practices in consulting, and gain exposure to a variety of real data science problems.

  • Intended for undergrad and grad students (especially PhD students) with advanced coursework and expertise in some areas of data science.

  • Great exposure to practical and applied data science problems with many opportunities for collaborative research projects or further consulting.

Prerequisites: STAT 405 OR COMP 140 OR CAAM 210

Course Meeting:

1:30 PM - 4:10 PM on Mondays

1:30 PM - 2:25 PM on Wednesdays

Location: Online

Visit the D2K Virtual Consulting Clinic >>

Intro-Level D2K Courses:

DSCI 101: Introduction to Data Science (NEW)

Instructor: Su Chen

Description: Students learn the fundamentals of data science and Python programming while working on teams to solve real data science challenges, design a data science pipeline, and derive and communicate valuable insights from data.

  • Intended for undergrads and no prior statistics, math, or programming experience is required.

  • Gain fluency in basic programming skills in Python with a focus on statistical modeling and machine learning.

Prerequisites: No prior background in statistics or programming required.

Course Meeting: 9:45 AM - 10:40 AM on Mondays, Wednesdays and Fridays

Location: Face-to-Face

Learn more here.


Questions?

Contact the course instructors or email d2k@rice.edu with any inquiries.