D2K Capstone Program:
DSCI 435 / 535 & COMP 449 / 549, Applied Machine Learning & Data Science Projects
Instructors: Arko Barman and Xinjie Lan
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.
What to Expect
We have projects that will use computer vision, natural language processing, signal processing, forecasting, statistical modeling, and data visualization to help solve problems in engineering, environmental science, neuroscience, sports, inequity, law, and more - even to sequence the genome of Sammy the Owl!
Projects are industry-sponsored (finance, energy, healthcare, tech), community impact projects (government and non-profits), and research projects (working with Rice or medical center faculty).
Who Should Apply
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.
Required course for the Data Science minor capstone, MDS capstone, and MECE data science track capstone.
Elective course for STAT senior capstone, CAAM senior capstone, STAT and CAAM grad students, COMP undergrad and grad students, and other students with advanced data science experience interested in conducting a real-world or research project.
In the course application, you can specify your project preferences which are used to assign projects and form teams.
Course Meeting: 5:00 PM - 6:30 PM on Mondays & Wednesdays
Intro-Level D2K Courses:
DSCI 101: Introduction to Data Science
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: 9:00 AM - 9:50 AM on Mondays, Wednesdays and Fridays