CST 230 - Programming for Data Science

Credits: 3
Instructional Contact Hours: 3

Introduces programming techniques to perform data retrieval, data wrangling, 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.

Prerequisite(s): CST 159 and CST 173 (each with “C” or better)
Corequisite(s): N/A
Lecture Hours: 45 Lab Hours: 0
Meets MTA Requirement: None
Pass/NoCredit: Yes

Outcomes and Objectives

Explore functions for data science.
1. Describe libraries.
2. Explain functions.
3. Explore popular data science libraries.
4. Use functions for data science.
Use collection types.
1. Define collection types.
2. Differentiate between lists, tuples, dictionaries, and arrays.
3. Use 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. Use data visualization
Introduce Machine learning.
1. Explain machine learning.
2. Explore machine learning techniques.
3. Prepare data requirements for machine learning.
4. Use machine learning for advising and predictions.