Nov 23, 2024

Catalog 2022-2023

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Catalog 2022-2023 [ARCHIVED CATALOG]

Data Science

Important Information

Short Description

Type of Credential

Associate of Science (A.S.)

Program Code

134

Contacts and Additional Information

Department Chair:

Wendy Chin
443-840-4879 or wchin@ccbcmd.edu

Program Coordinators:

CCBC Catonsville, Melissa Akhimiemona
443-840-5156 or makhimiemona@ccbcmd.edu

CCBC Essex/Dundalk, James Braman
443-840-1727 or jbraman@ccbcmd.edu

Additional Information:

Opportunities for Prior Learning Assessment (PLA) - Several General Education classes offer PLA. See Portfolio, Departmental Exam, CLEP, DSST, Advanced Placement, and International Baccalaureate.
Articulation and Transfer Agreements - While there are no program-specific agreements, see other types of agreements such as guaranteed admission, block transfer, and discounts.

Semester Sequence

This is a suggested full-time schedule for a student who has completed any developmental course work and has no transfer credits. Refer to the College catalog for specific requirements in selecting General Education Courses .

Courses Needed for This Program*

General Education Requirements and Electives - 29 Credits

General Education Requirements:

BIOL 110 - Biology I: Molecules and Cells 4 Credit(s).
CHEM 107 - Fundamentals of Chemistry 3 Credit(s).
or
ENVS 101 - Introduction to Environmental Science 3 Credit(s).
CMNS 101 - Fundamentals of Communication 3 Credit(s).
CSIT 111 - Logic and OO Design 3 Credit(s).
ENGL 101 - College Composition I 3 Credit(s).
MATH 153 - Introduction to Statistical Methods 4 Credit(s).
PHIL 101 - Introduction to Philosophy 3 Credit(s).
PSYC 105 - Multicultural Psychology 3 Credit(s). (fulfills diversity requirement)
SOCL 101 - Introduction to Sociology 3 Credit(s).

Program Requirements - 31 Credits

Program Requirements:

CSIT 142 - Introduction to MIS 3 Credit(s).
CSIT 154 - Database Concepts 4 Credit(s).
CSIT 210 - Introduction to Programming 4 Credit(s).
CSIT 211 - Advanced Programming 4 Credit(s).
CSIT 251 - Data Visualization 3 Credit(s).
CSIT 255 - Fundamentals of Data Science 4 Credit(s).
CSIT 260 - Introduction to Machine Learning 3 Credit(s).
MATH 163 - Pre-Calculus I 3 Credit(s).
MNGT 136 - Business Analytics 3 Credit(s).

Total Number of Credits Required for Degree: 60*

Notes

*Credit students who are new to college (no successfully completed transferable college credits from other institutions) are required to take ACDV 101 - Academic Development: Transitioning to College . This 1-credit course is designed to be taken in the first semester at CCBC. Students must provide an official transcript(s) from an accredited institution to document successful completion of college coursework for the ACDV 101 requirement to be waived.

Program Description

This degree is designed for transfer to a four-year institution in numerous fields. Students develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Students apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets.

Program Outcomes

Upon successful completion of this degree, students will be able to:

Transfer to a four-year institution for a degree in Data Science or related field.
Develop and design well-written code to manipulate data for analysis.
Design and implement solutions in the context of the discipline.
Apply fundamental data science concepts and algorithms to preform modeling and exploratory data analysis.
Explain the Data Analytic Life Cycle in the development of computer programs.
Demonstrate the ability to critically examine, interpret, organize, and describe multiple forms of data.
Demonstrate appropriate ethical and professional conduct related to technology and data science practices.
Demonstrate statistical reasoning in everyday life using real world data.
Construct a solution to real world problems using problem solving methods.