DATA ANALYTICS – A.A.S.
Trocaire College’s Data Analytics A.A.S. degree program prepares graduates to assume entry and midlevel management roles that oversee the identification, analysis, and interpretation of volumes of data that are collected from a wide variety of sources. Graduates of the program are prepared to identify patterns and relationships in large data sets, to resolve business questions and make data-driven decisions, and effectively communicate informed tactical and strategic business objectives. Careers include data analyst, data scientist, database administrators, and statistical assistants.
Program Learning Outcomes
All students completing this program are expected to achieve the General Education outcomes described in the General Studies section of the catalog as well as the following learning objectives:
Describe the purpose, potential uses, and methods of data collection and analyses in a variety of industries.
Apply data mining methodologies.
Apply programming to the extract, transfer, and load (ETL) process.
Demonstrate competency with data science practices and methodologies using the Cross-Industry Standard Process for Data Mining (CRISP_DM).
Use common data analysis and management tools (e.g., SQL, DBMS applications, etc.) demonstrate proficiency designing, creating, querying and managing databases for analytic processing.
Validate patterns and relationships in large data sets using statistical tools.
Create and modify customizable tools for data analysis and visualization per the evaluation of characteristics of the data and the nature of the analysis.
Demonstrate ability to manage a project from the design stage to the final report.
Work collaboratively with team members in assembling, analyzing and reporting findings.
Produce clear, written reports of data findings.
Data Analytics – A.A.S. Curriculum
First Year – 1st Semester | ||
Course Number | Course Title | Credits |
|---|---|---|
DA101 | Introduction to Data Science | 3 |
DA102 | Data Analysis | 3 |
DA103 | SQL for Data Analysis | 3 |
*GS100 or | College Seminar or | 1 - 3 |
MA120 | Statistics I | 3 |
PH107 | Logical Reasoning and Decision Making | 3 |
Total Credits |
| 16 |
First Year – 2nd Semester | ||
Course Number | Course Title | Credits |
DA105 | Big Data Architecture | 3 |
DA106 | Problem Solving, Decision-Making, & Computer Application in Business | 3 |
DA200 | Statistical Methods in Data Science | 3 |
PH215 | Logic | 3 |
PSY101 | General Psychology | 3 |
Total Credits |
| 15 |
Second Year – 1st Semester | ||
Course Number | Course Title | Credits |
BU300 | Project Management | 3 |
DA104 | Data Mining | 3 |
DA202 | Data Visualization and Business Intelligence | 3 |
PH206 | Ethics in Data Science | 3 |
BIOEL | Biology Elective | 3 |
Total Credits |
| 15 |
Second Year – 2nd Semester | ||
Course Number | Course Title | Credits |
GS320 | Research Methods and Designs | 3 |
DA201 | Data Analysis with R | 3 |
DA203 | Advanced Data Visualization | 3 |
DA204 | Capstone Experience in Data Science | 3 |
EN101 | English Composition | 3 |
Total Credits | 15 | |
Total Program Credits | 61 |
*GS100 College Seminar or GS102 College Success must be taken at the main campus
Additional Degree Requirements
A minimum grade of “C” in BU300, DA101, DA102, DA103, DA104, DA105, DA106, DA200, DA201, DA202, DA203, DA204, GS100 or GS102, GS320, MA120 and a Quality Point Average of 2.0.
BIO & BIOEL Course Descriptions
BU Course Descriptions
CNA Course Descriptions
DA Course Descriptions
EN Course Descriptions
GS Course Descriptions
MA Course Descriptions
PH Course Descriptions
PSY Course Descriptions