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)
- Machine Learning
- Deep Learning
Specialization Choice (3 units, take one of the following classes)
- Enterprise Distributed Systems
- Enterprise Application Development
- Cloud Technologies
- Virtualization Technologies
- Software Engineering Processes
- Software Quality Assurance and Testing
- Computer Network Design
- Network Programming and Applications
- Network Security
- CMPE 219 Cybersecurity Clinics with HCI
- Software Security Technologies
