Computer Information Systems: Database
10 courses · Mt. San Antonio College · 2026-2027 Catalog
Classes: Fall 2026 · Winter 2027
CISD 11 Database Management - Microsoft Access
- Units
- 3
- Hours
- Lecture: 54
- Corequisite
- CISD 11L
- Advisory
- CISB 11 and CISB 15
- Transfers to
- CSU
Design, creation, and management of relational databases using Microsoft Access. Basic database design, creation of tables, queries, forms, reports, and macros. Creation of custom graphical user interface and introduction to Visual Basic for Applications (VBA) code.
CISD 11L Database Management - Microsoft Access Laboratory
- Units
- 0.5
- Hours
- Lab: 27
- Corequisite
- CISD 11
- Transfers to
- CSU
Laboratory for CISD 11 - Database Management - Microsoft Access. Exercises focusing on design and development of a business database using Microsoft Access software, including creation of tables and relationships between tables, queries, forms, reports, macros, and an introduction to Visual Basics for Applications (VBA) programming language to make a fully-functioning, user-friendly Access database.
CISD 21 Database Management - Microsoft SQL Server
- Units
- 3
- Hours
- Lecture: 54
- Corequisite
- CISD 21L
- Advisory
- CISB 11 or CISB 15
- Transfers to
- CSU
Structured Query Language (SQL) and Transact-SQL for Microsoft SQL Server. Topics include creating database objects, retrieving and updating data, writing scripts, developing stored procedures and functions, developing triggers, and creating cursors. Student must be enrolled in CISD 21L, a concurrent lab co-requisite.
CISD 21L Database Management - Microsoft SQL Server Laboratory
- Units
- 0.5
- Hours
- Lab: 27
- Corequisite
- CISD 21
- Transfers to
- CSU
Laboratory for CISD 21 - Structured Query Language (SQL) and Transact-SQL for Microsoft SQL Server. Topics include creating database objects, retrieving and updating data, writing scripts, developing stored procedures, functions, triggers, and creating cursors. Student must be enrolled in CISD 21, a concurrent lecture co-requisite.
CISD 31 Database Management - Oracle
- Units
- 3
- Hours
- Lecture: 54
- Corequisite
- CISD 31L
- Advisory
- CISB 11 and CISB 15
- Transfers to
- CSU
Oracle database management system (DBMS) functions, concepts, and terms. Procedure Language/Structure Query Language (PL/SQL) is used to code, test, and implement stored procedures, functions, triggers, and packages. Relational database projects will be built using PL/SQL. Concurrent enrollment in CISD 31L is required.
CISD 31L Database Management - Oracle Laboratory
- Units
- 0.5
- Hours
- Lab: 27
- Corequisite
- CISD 31
- Transfers to
- CSU
Laboratory for CISD 31 - Oracle database management system (DBMS) functions, concepts, and terms. Procedure Language/Structured Query Language (PL/SQL) is used to code, test, and implement stored procedures, functions, triggers, and packages. Relational database projects will be built using PL/SQL. Concurrent enrollment in CISD 31 is required.
CISD 40 Database Design
- Units
- 3
- Hours
- Lecture: 54
- Transfers to
- CSU
Database design principles. Understanding database needs and functions; creating data models, entity-relationship (E-R), and Unified Modeling Language (UML) diagrams; using normalization rules and principles to create databases; learning basic database administrator objectives and tasks; and understanding the role of data warehousing and data mining.
CISD 41 Introduction to Data Science
- Units
- 3.5
- Hours
- Lecture: 54, Lab: 27
- Advisory
- CISP 10
- Transfers to
- CSU, UC
This course provides a comprehensive introduction to the entire data science lifecycle, equipping students with both the theoretical foundation and practical skills needed to tackle real-world data challenges. Students will learn to define data science problems, acquire and preprocess data, engineer features, explore and visualize datasets, select appropriate models, and make predictions. Key statistical concepts covered include descriptive statistics, probability, sampling methods, and inferential statistics. Throughout the course, students will gain hands-on experience with programming tools and libraries to implement these concepts. The course also introduces fundamental machine learning techniques, including basic algorithms such as linear regression and k-nearest neighbors' classification. Students will apply these methods to build and evaluate models, using industry-standard libraries and tools. By the end of the course, students will have the knowledge and practical expertise to independently design, execute, and evaluate complete data science projects, from problem definition through to solution deployment.
CISD 42 Big Data Integration and Processing
- Units
- 3.5
- Hours
- Lecture: 54, Lab: 27
- Prerequisite
- CISD 41
- Advisory
- CISP 10
Learn Big Data: why and where. Characteristics of Big Data and dimensions of scalability. Use Big Data frameworks and tools. Retrieve data from example database and Big Data management systems. Acquire and ingest Big Data. Get value out of Big Data by using a 5-step process to structure an analysis. Process Big Data using various technologies. Identify when a Big Data problem needs data integration. Integrate Big Data and warehouse data using various technologies. Describe the connections between data management operations and the Big Data processing patterns needed to utilize them in large-scale analytical applications.
CISD 43 Big Data Modeling and Analysis
- Units
- 3.5
- Hours
- Lecture: 54, Lab: 27
- Prerequisite
- CISD 41
- Advisory
- CISP 10
Introduces students to various Big Data management systems and analytical tools. Addresses data mining vs predictive analytics. Provides an exposure to data modeling, data mining, text mining, analytics, real-time analytics, and graph analytics from Big Data perspective.