CIS 364 From Data Warehousing to Big Data
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Computer and Information Science (CIS) Courses
- CIS 110 Introduction to Computer and Information Science
- CIS 111 Introduction to Web Programming
- CIS 113 Ruby Programming
- CIS 114 JavaScript/Ajax Programming
- CIS 117 Python Programming
- CIS 121 UNIX/Linux
- CIS 124 Foundations of Data Science
- CIS 127 HTML5 and CSS
- CIS 128 Mobile Web App Development
- CIS 129 Frameworks/Server-Side JavaScript
- CIS 132 Introduction to Databases
- CIS 133 NoSQL Databases
- CIS 135 Android Programming
- CIS 137 iOS/Swift Programming
- CIS 138 Internet of Things
- CIS 140 Big Data Analytics
- CIS 145 Introduction to DevOps
- CIS 151 Computer Networking
- CIS 254 Introduction to Object-Oriented Program Design
- CIS 255 (CS1) Programming Methods: Java
- CIS 256 (CS2) Data Structures: Java
- CIS 262 Discrete Mathematics for Computer Science
- CIS 264 Computer Organization and Systems Programming
- CIS 278 (CS1) Programming Methods: C++
- CIS 279 (CS2) Data Structures: C++
- CIS 363 Enterprise Database Management
- CIS 364 From Data Warehousing to Big Data
- CIS 380 PHP Programming
- CIS 400 Probability for Computer Scientists
- CIS 401 Introduction to Machine Learning
- CIS 402 Introduction to Artificial Intelligence
- CIS 479 Computer and Network Security
- CIS 482 Ethical Hacking
- CIS 493 Cloud Security Fundamentals
- CIS 500 Introduction to Cloud Computing
- CIS 501 (CS2) Data Structures: Python
- CIS 502 Applied Python Programming
- CIS 503 Data Visualization and Text Analysis in Python
Additional Content
Minimum Grade for Prerequisites
Unless otherwise indicated, a grade of C or higher is required for all prerequisite courses.
Course Description
Introduction to data warehousing architecture, data extraction, management, and load. Also covered are metadata management, dimensional modeling, data aggregation, data mining and Business Intelligence. Both SQL and NoSQL databases will be employed. Introduction to Big Data architecture, technologies and analytics. Selection, processing and querying of Big Data stores for disparate data sets are also covered. Other topics such as Cloud computing, security management, machine learning, Agile methodology and Big Data tools will be introduced.
- Lecture hours/semester: 48-54
- Lab hours/semester: 48-54
- Homework hours/semester: 96-108
