Data Science

Specialization Overview

The Data Science specialization prepares students and professionals to investigate and summarize real-world data of all sizes, ask the right questions, find informative answers, and create visualizations that effectively communicate their results. Through a combination of theory and practical data analysis, students learn the foundations of extracting knowledge from data, verifying the utility of the information, and scaling their analysis to Big Data. The program emphasizes teamwork throughout the curriculum, as an essential part of preparing students for working in industry.

The specialization focuses on a variety of techniques and methods for analyzing data, including data preprocessing, exploratory analysis, unsupervised and supervised inference and learning, association analysis and pattern mining, Web search, text mining, recommender systems, social network and sentiment analysis, hypothesis testing, image recognition, time series analysis, deep learning, and data visualization. Students learn and practice the entire analytics process, from translating real-world objects into data to presenting information gleaned from the analysis.

Required Specialization Core (6 units, take both of the following classes)

Specialization Choice (3 units, take one of the following classes)


  • CMPE 239 is replaced, with expanded content, by CMPE 255 and CMPE 256. The CMPE 239 class will no longer be offered starting Fall 2017.
  • CMPE 239 completed during Spring 2017 can be substituted for CMPE 255 for degree credit.
  • CMPE 239 completed prior to Spring 2017 cannot be used for degree credit concurrently with either CMPE 255 or CMPE 256 because of the substantial overlap between the classes.
  • CMPE 274 Business Intelligence Technologies taken during the Spring 2014 or Fall 2014 semesters can be used as a specialization core or specialization choice class.