DSCI 101 Introduction to Data Science
Instructor: Lorenzo Luzi
Course Description:
DSCI 101 is an introductory level course where students learn about the fundamentals and principles of data science by working on real world data using python. During the semester, class will meet three times each week: a lecture, a demo, and a lab. Lectures will cover course material, concepts, and introduce tools students need to complete the weekly assignments. The coding demos entail the professor guiding the students through some data science and coding basics. The lab will be devoted to students working on assignments by themselves, or with peers, with guidance from the instructor and teaching assistants. These assignments are designed to assess students’ understanding and check their progress as they move forward along their data science journey.
Course content includes foundations in managing and analyzing data; 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 to solve data science problems using real-world data.
- Gain fluency in basic programming skills in python with a focus on statistical modeling and machine learning.
- Learn how to read and understand programming documentation
- Use applied statistical knowledge to analyze data, derive data summaries, build predictive models, and make scientific inferences.
- 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.
Questions?
Contact Lorenzo Luzi at luzi@rice.edu.