MATH 611 Data Science and Analytics
The purpose of this course is to provide students with a sound conceptual understanding of the role that data science and analytics play in the decision-making process. The availability of massive amounts of data, improvements in analytic methodologies, and substantial increases in computing power have all come together to result in a dramatic upsurge in the use of data science and analytical methods. This course can be taken by students who have previously taken a course on basic statistical methods as well as students who have not had a prior course in statistics. Topics include models for summarizing, visualizing, and understanding historical data to assist in gaining insights for predicting possible future outcomes using descriptive, predictive and prescriptive data analytic techniques. Examples include applications in finance, human resources, marketing, health care, supply-chain, government and nonprofits, and sports.
Prerequisites: Bachelor's degree in mathematics, mathematics education, statistics or related field. Note: Graduate admission status required. Students whose prerequisites are not identified by the system should contact the Math and Statistics Department for an override at MATH@metrostate.edu. This is an overlap with DATA 611.
Special information
3 Graduate credits
Effective January 12, 2020 to present
Learning outcomes
General
- Describe mathematical and statistical methods in data science, and the theory behind them.
- Describe algorithms for various data science tasks, such as classification, regression, clustering, and recommendation
- Analyze data and identify important relations and patterns using data visualization techniques and tools.
- Analyze data with supervised and unsupervised machine learning algorithms.
- Construct and evaluate models using training, validation, and test sets.
- Use statistical software to analyze real-world data and communicate results and recommendations