CIS 230 - Programming for Data Science

Description
Introduces programming techniques to perform data retrieval, data clean-up, data modeling, data analysis, and data visualization. Demonstrates the basic coding skills that will apply to data science projects. Introduces concepts of machine learning models to generate predictions and recommendations.

Credit Hours: 3
Contact Hours: 3

School: School of STEM
Department: Computer Information Systems
Discipline: CIS

Course Review & Revision Year: 2026-2027

Course Type:
Program Requirement- Offering designed to meet the learning needs of students in a specific GRCC program.
Course Format:
Lecture - 1:1

General Education Requirement: None
General Education Learner Outcomes (GELO):
NA

Course Learning Outcomes:
1. Describe and demonstrate how to utilize, load and run libraries, modules, and/or functions for data science.

2. Apply and solve problems using array-like data structures.

3. Define and implement data wrangling by retrieving, organizing, and cleaning data.

4. Apply and solve problems using data and statistical models.

5. Identify and utilize data visualization techniques and libraries to solve problems and/or create a narrative.

6. Describe and demonstrate usage of supervised and unsupervised machine learning tecnniques.

Approved for Online Delivery?: Yes

Course Outline:

Explore functions for data science.
1. Describe libraries.
2. Explain functions.
3. Explore popular data science libraries.
4. Utilize functions for data science.
Manipulate collection types.
1. Define collection types.
2. Differentiate between lists, tuples, dictionaries, and arrays.
3. Utilize lists and tuples.
4. Create dictionaries.
5. Implement arrays.
6. Apply collection type functions.
Perform data wrangling.
1. Explain data wrangling.
2. Explore data mapping.
3. Retrieve various data.
4. Organize data.
5. Clean data.
Create data models.
1. Describe data modeling.
2. Develop statistical models.
3. Explore predictive modeling.
4. Discuss advanced models.
Visualize data.
1. Describe data visualization.
2. Explore data visualization libraries.
3. Utilize data visualization
Introduce Machine learning.
1. Explain machine learning.
2. Explore machine learning techniques.
3. Prepare data requirements for machine learning.
4. Implement machine learning for advising and predictions.

Mandatory CLO Competency Assessment Measures:
None

Name of Industry Recognize Credentials: None

Instructional Strategies:
Lecture: 10-40%

Facilitated discussion: 0-20%

Group work: 0-10%

Applied work: 30-60%

Mandatory Course Components:
1. At least 15 Programming Projects and Activities

Academic Program Prerequisite: None
Prerequisites/Other Requirements: CIS 116 ( C or Higher )
English Prerequisite(s): None
Math Prerequisite(s): None
Course Corerequisite(s): None
Course-Specific Placement Test: None
Course Aligned with IRW: IRW 97, IRW 98, IRW 99
Consent to Enroll in Course: No Department Consent Required

Total Lecture Hours Per Week: 3
Faculty Credential Requirements:
Master’s Degree (GRCC general requirement), Professionally qualified through work experience in field (Perkins Act or Other) (list below)
Faculty Credential Requirement Details: The instructor must possess knowledge of the current operating environment, 4000 hours of programming experience, knowledge of the programming environment, a good background in object oriented programing, and, above all, be able to clearly explain all topics covered in the course so that the student will be able to understand the concepts taught

Maximum Course Enrollment: 24
Equivalent Courses: None
Dual Enrollment Allowed?: Yes
Number of Times Course can be taken for credit: 1
First Term Valid: Fall 2022 (8/1/2022)
Programs Where This Courses is a Requirement:
Data Science, Certificate
1st Catalog Year: 2022-2023
People Soft Course ID Number: 105126
Course CIP Code: 11.9999

Name of Course Author:
Jonnathan Resendiz

High School Articulation Agreements exist?: No
Non-Credit GRCC Agreement exist?: No
Corporate Articulation Agreement exist?: No