Advanced D2K Courses:
DSCI 435 / 535 & COMP 449 / 549, Applied Machine Learning & Data Science Projects
Instructors: Arko Barman and Andersen Chang
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.
We will have 9 projects in the fall covering a wide variety of problems, including computer vision, natural language processing, deep learning applications, web scraping, time series analysis, and signal processing.
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).
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 the Data Science minor capstone, the MDS capstone, an elective capstone for Stat seniors, CAAM seniors, and MECE students as well as an elective for ELEC, COMP, STAT and CAAM undergrads and grads.
You can register for the course now and a course application and project preference form will be due the first week of classes to determine projects and teams.
Course Meeting: 5:00 PM - 6:30 PM on Mondays & Wednesdays
Location: RZR 119
COMP 680 Stats Computing Data Science
Instructor: Su Chen
Description: Probability and statistics are essential tools in computer science and data science. They are at the heart of areas such as efficiency analysis of algorithms and randomized algorithms and central to fields like bioinformatics, social informatics, and, of course, machine learning. Furthermore, probability and statistics are essential for data science, as they are the foundation for quantifying uncertainty and assessing support for hypotheses and derived models. This course covers topics in probability and statistics, including probability and random variables, basic stochastic processes, basic descriptive statistics, and various methods for statistical inference and measuring support.
Course Meeting: 2:00PM - 3:15PM on Mondays and Wednesdays
Location: DCH 1042
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
Location: MXF 251
View student projects from the previous D2K Showcase:
Contact the course instructors with any inquiries.
Join us for the Data Science Welcome Party on Tuesday, September 7 at 5:30pm (CST) to connect with our D2K faculty, student clubs and learn many ways to get involved with data science at Rice!