Skip to content

Introduction to Data Science Curriculum

IDS-logo

(version 6.2 for LAUSD)

Robert Gould

Suyen Machado

Terri Anna Johnson

James Molyneux

Sponsors & Supporters

This curriculum was created under the auspices of the National Science Foundation, Mathematics and Science Partnership grant, "MOBILIZE: Mobilizing for Innovative Computer Science Teaching and Learning". Lead Principal Investigator: Robert Gould (UCLA, Statistics). sponsor

Contributing Authors

LAUSD: Monica Casillas and Heidi Estevez

UCLA: Amelia McNamara and Linda Zanontian

Acknowledgments and Special Thanks

Co-Principal Investigators: Deborah Estrin (UCLA, CENS), Joanna Goode (University of Oregon), Mark Hansen (UCLA, Statistics), Jane Margolis (UCLA, Center X), Thomas Philip (UCLA, Center X), Jody Priselac (UCLA, GSEIS), Derrick Chau (LAUSD), Gerardo Loera (LAUSD) and Todd Ullah (LAUSD); Mobilize Project Director: LeeAnn Trusela

LAUSD IDS Pilot Teachers

Robert Montgomery, Carole Sailer, Joy Lee, Monica Casillas, Roberta Ross, Velia Valle, Jose Guzman, Pamela Amaya, Arlene Pascua, Christopher Marangopoulos

This material is based upon work supported by the National Science Foundation under Grant Number 0962919.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0

For additional information related to IDS visit: https://www.idsucla.org

Mobilize

mobilize

Mobilize, an innovative partnership between UCLA and LAUSD, was funded in 2010 by the National Science Foundation to develop barrier-breaking curriculum in science, mathematics, and computer science to teach students to think creatively, constructively, and critically about the role of data in science and in everyday life. The Mobilize curricula center around Participatory Sensing campaigns, through which students use their mobile devices to collect and share data about their community and their lives, and analyze these data to gain a greater understanding about their world. Mobilize broke barriers by teaching students to apply concepts and practices from computer science and statistics to learning science and mathematics, and it was uniquely dynamic in that each Mobilize class collects its own data, and each class has the opportunity to make unique discoveries. Across all Mobilize curricula, mobile devices are used not as gimmicks to capture students' attention, but as legitimate tools that bring scientific enquiry into their everyday lives. Since 2011, LAUSD high school mathematics, science, and computer science teachers have attended the summer institutes designed by the Mobilize grant to learn to use the participatory sensing (PS) methods, tools, and materials to deepen their knowledge of computer science (CS) concepts and to support student CS, math, and science learning.

First implemented in 2014 under the auspices of the Mobilize grant, Introduction to Data Science (IDS) began as a pilot program with 10 LAUSD mathematics teachers, and by the 5th printing of the curriculum in 2018 has expanded to 30+ schools in seven Southern California public school districts, serving over 4,000 students and counting. In addition to addressing the Common Core State Standards (CCSS) for High School Statistics and Probability IDS leads students to:

  • understand how data are used by professionals to address real-world problems;

  • understand that data are used in all facets of modern life;

  • understand how data support science to identify and tackle real-world problems in our communities;

  • analyze statistical graphics to identify patterns in data and to connect these patterns back to the real world;

  • understand that by treating photos, words, numbers, and sounds as data, we can gain insight into the real world;

  • learn to analyze data, including: posing questions that can be answered by considering relations among variables in a data set, using collected data to generate hypotheses for future data collection, critically evaluating shortcomings and strengths in the data and the data collection process, and informally evaluating hypotheses using data at hand.