MCCCD Program Description

Data Complexity

Course: CIS317

First Term: 2023 Fall

Lec + Lab 3.0 Credit(s) 4.0 Period(s) 4.0 Load
Subject Type: Occupational
Load Formula: T- Lab Load

Description: Intricacies of enterprise data and how to apply knowledge (background, concepts and experiences from prior courses) and skills (Integrated Development Environment (IDE) tools, and programming languages) on complex integrated and non-integrated disparate datasets, by testing, designing, and creating algorithms that lead to improved decision making.

MCCCD Official Course Competencies
1. Demonstrate understanding of the intricacies of data complexity. (I, II, III) 2. Use concepts and experiences from prior programming knowledge to investigate and solve data complexity problems. (I, II, III, V, VI, VII) 3. Apply programming skills using integrated development environment (IDE) on complex integrated and non-integrated, disparate datasets and cases to improve decision making. (IV, V, VII) 4. Test existing algorithms using Python, C# or R. (V, VII) 5. Apply programming lifecycle to new algorithms using Python, C# or R. (V, VII) 6. Analyze the efficiency of searching and sorting algorithms to determine more efficient models or classifiers to be used in solving problems or cases. (VII)
MCCCD Official Course Competencies must be coordinated with the content outline so that each major point in the outline serves one or more competencies. MCCCD faculty retains authority in determining the pedagogical approach, methodology, content sequencing, and assessment metrics for student work. Please see individual course syllabi for additional information, including specific course requirements.

MCCCD Official Course Outline
I. Enterprise data complexity A. File characteristics B. Data types C. Transience D. Availability II. Data complexity matrixes III. Data acquisition IV. Data abstraction V. Data engineering VI. Data modeling A. Structures B. Parsimony C. Dimensionality VII. Testing and programming Machine Learning (ML) algorithms A. Problem solving B. Efficiency C. Classification D. Feature selection E. Model parameters F. Linear and logistic regression G. Record linkage H. Matching I. Accuracy J. Performance

MCCCD Governing Board Approval Date: August 23, 2022

All information published is subject to change without notice. Every effort has been made to ensure the accuracy of information presented, but based on the dynamic nature of the curricular process, course and program information is subject to change in order to reflect the most current information available.