Common Course Outline

Catalog Description

Course Description

This course is the culmination of the Information Technology degree. Students will demonstrate their capabilities in the form of a capstone project. The capstone project enables students to showcase the concepts learned throughout the degree program and apply what they have learned. The course combines guided professional development with independent technical work, allowing students to showcase their ability to analyze, implement, and communicate data analysis. It is expected that this class will be taken concurrently with or after completing DSCI 2257.

Date Last Revised (Month, year)

March, 2024

Outline of Major Content Areas

1. Analysis of large data sets
2. Principles of machine learning
3. Classification Algorithms
4. Prediction Algorithms
5. Data Modeling
6. Model evaluation
7. Analysis of model fit and outcomes
8. Communication of results
9. Professional development for data analysis careers
10. Resume writing for data analytics roles
11. Technical interview preparation
12. Preparing for certifications

Learning Outcomes (General)

The student will be able to:

1. Apply machine learning techniques.
2. Apply searching and sorting.
3. Apply methods of data preparation.
4. Clean and tag data for machine learning models.
5. Develop a solution for supervised/unsupervised models.
6. Apply and describe problem-solving approaches.
7. Present findings of the analysis and model fit.
8. Demonstrate interview skills specific to data analysis positions

Learning Outcomes (MnTC)

NA

Methods for Evaluation of Student Learning

Methods may include but are not limited to:

• Capstone Project

RCTC Core Outcome(s)

This course contributes to meeting the following RCTC Core Outcome(s):

Critical Thinking

Students will think systematically and explore information thoroughly before accepting or formulating a position or conclusion.

Special Information (if any)

None