CIS 231 - Applied Data Science

Description
Introduces students to advanced data science concepts and skills. Demonstrates the application of tools and techniques of data science, mathematical and computational. In a project-driven environment, students work to create a large-scale data science and machine learning deliverable. This final project provides an in-depth opportunity to apply data science and get a feel for what working on a large-scale project is really like. Students will define and solve a problem end-to-end from data requirements, to identifying requirements by formulating hypotheses, and finally presenting their insights using visualization.

Credit Hours: 3
Contact Hours: 3
Prerequisites/Other Requirements: C or Higher in one of the following courses: CIS230 or CIS 210
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, Data Science, A.A.A.S.

Other Courses Where This Course is a Prerequisite: None
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:

Import, clean, manipulate, and validate data to prepare it for meaningful analysis and modeling.
Evaluate statistical and data models for accuracy, appropriateness, and reliability.
Design data models using conceptual, logical, and physical approaches, including ER diagrams or UML representations.
Create effective data visualizations by selecting appropriate libraries, plotting functions, and interactive techniques to communicate insights.
Apply the full data analysis lifecycle—from problem definition through extraction, cleansing, modeling, visualization, and presentation—to a real-world project.

Course Outline:

Obtain data.
1. Import data.
2. Manipulate data.
3. Structurally process data.
4. Polish data.
Scrub data.
1. Identify bad data.
2. Modify bad data.
3. Remove bad data.
4. Ensure data is accurate.
Explore data.
1. Inspect data and its properties.
2. Utilize algorithms to test data.
3. Apply cross-validation.
Model data.
1. Compare and contrast conceptual, logical, and physical modeling.
2. Explain data models using Entity Relationship Modeling and Unified Modeling Language
3. Utilize Entity Relationship Modeling.
4. Utilize Unified Modeling Language.
5. Create a data model.
Visualize data
1. Explain data visualization.
2. Evaluate data visualization libraries.
3. Analyze data visualization using applications.
4. Create data visualizations.
Interpret data.
1. Use data plotting functions.
2. Create interactive charts.
3. Enhance charts.
4. Explain the results of a chart.
Apply Data analysis.
1. Define data analysis.
2. Perform data analysis.
3. Use data analysis 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
Name of Industry Recognize Credentials: None

Course-Specific Placement Test: None
Course Aligned with ARW/IRW Pairing: ARW 100 (Previously IRW97/IRW98), IRW 101 (Previously IRW 99)

Mandatory Department Assessment Measures:
None

GRCC Course Type:
Program Requirement: Meets the learning needs of students in a specific GRCC program.
Course Format:
Lecture - 1:1
Total Lecture Hours Per Week: 3
Total Fieldwork Hours Per Week: None
People Soft Course ID Number: 105134
Course CIP Code: 11.9999
Maximum Course Enrollment: 24
Course Software Utilized: Python

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: Winter 2023 (1/1/2023)
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: 20260220T14:29:36
Course Review & Revision Year: 2030-2031