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
Prerequisites/Other Requirements: C or Higher in one of the following courses: CIS 116 or CIS 123
English Prerequisite(s): None
Math Prerequisite(s): None
Course Corequisite(s): None
Academic Program Prerequisite: None
Consent to Enroll in Course: No Department Consent Required
Dual Enrollment Allowed?: Yes
Number of Times Course can be taken for credit: 1
Programs Where This Course is a Requirement:
Data Science, Certificate

Other Courses Where This Course is a Prerequisite: CIS231
Other Courses Where this Course is a Corequisite: None
Other Courses Where This course is included in within the Description: None

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.
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.

Approved for Online and Hybrid Delivery?:
Yes
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
Equivalent Courses:
None

Accepted GRCC Advanced Placement (AP) Exam Credit: None
AP Min. Score: NA
Name of Industry Recognize Credentials: None

Course-Specific Placement Test: None
Course Aligned with ARW/IRW Pairing: ARW 100 (IRW97/IRW98), IRW 99

Mandatory Department Assessment Measures:
None

Course Type:
Program Requirement- Offering designed to meet the learning needs of students in a specific GRCC program.
Course Format:
Lecture - 1:1
Total Lecture Hours Per Week: 3
People Soft Course ID Number: 105126
Course CIP Code: 11.9999
Maximum Course Enrollment: 24
High School Articulation Agreements exist?: No
Non-Credit GRCC Articulation Agreement With What Area: No
School: School of STEM
Department: Computer Information Systems
Discipline: CIS
First Term Valid: Fall 2022 (8/1/2022)
1st Catalog Year: 2022-2023
Name of Course Author:
Jonnathan Resendiz

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

Major Course Revisions: Prerequisite
Last Revision Date Effective: 20250224T19:33:36
Course Review & Revision Year: 2029-2030