CST 231 - Data Science Essentials

Credits: 3
Instructional Contact Hours: 3

Introduces students to advanced data science concepts and skills. Demonstrates the use of tools and techniques of data science. Introduces gather, clean, model, and analyzing data. Practices skills in math, programming, and statistics to create predictive models.

Prerequisite(s): CST 230 and MTH 225 (each with “C” or better)
Corequisite(s): None
Lecture Hours: 45 Lab Hours: 0
Meets MTA Requirement: None
Pass/NoCredit: Yes

Outcomes and Objectives

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. Use algorithms to test data.
3. Apply cross-validation.
Model data.
1. Explain the differences between conceptual, logical, and physical modeling.
2. Describe the two data modeling techniques.
3. Use Entity Relationship Modeling.
4. Use Unified Modeling Language.
5. Create a data model.
Visualize data
1. Discuss data visualization.
2. Evaluate data visualization libraries.
3. Discuss data visualization using applications.
4. Create data visualizations.
Apply Data analysis.
1. Define data analysis.
2. Perform data analysis.
3. Use data analysis for advising and predictions.
Interpret data.
1. Use data plotting functions.
2. Create interactive charts.
3. Enhance charts.
4. Understand the results of a chart.