Intro to Data Science

DSCI 101 Introduction to Data Science

Instructor: Su Chen

Course Description:

This is an introductory level course where students learn about fundamentals and principles in data science by completing semester-long data analysis projects in teams.  Each team will select a real-world dataset and data science challenge aligned with students’ interests.

During the semester, class will meet for three times each week. Lectures will cover course material and introduce tools students need to complete the weekly assignments which allow students to apply the knowledge and techniques to their team’s dataset. One lecture each week will be devoted to students working on these assignments as a team under the guidance of the instructor. These assignments are designed to assess students’ understanding and check their progress as they move forward along the data science pipeline. Students will give presentations as they complete certain project milestones and receive feedback and guidance for the next step.

At the end of the semester, each student team will produce a final project report and give a formal presentation on their work. Course content includes foundations in managing and analyzing data; data mining techniques and tools; exploratory data analysis and data visualization; applied statistical methods and inference; machine learning algorithms and predictive models.  

This course will use Python and also teach fundamentals of Python programming.

Course Objectives:


Students completing this course will be able to:

  • Define and explain key concepts in the data science pipeline and work as a team to complete data science life cycle and analyze real-world data.
  • Gain fluency in basic programming skills in Python with a focus on statistical modeling and machine learning.
  • Use applied statistical knowledge to analyze data, derive data summaries, build predictive models, and make scientific inference.
  • Interpret modeling results and communicate their findings to both a general and a technical audience.

Prerequisites:


This is a non-calculus based course with no prior background in statistics or programming required.

Meeting Times:

9:00 AM - 9:50 AM on Mondays, Wednesdays and Fridays

Locations:

MXF 251

Questions?

Contact Su Chen at sc131@rice.edu.