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.
- Understand the difference between descriptive, predictive and prescriptive analytics.
- Analyze data and identify important relations and patterns using data visualization techniques and tools.
- Apply descriptive data mining or unsupervised learning techniques such as cluster analysis, association rules, and text mining.
- Classify a categorical response or estimate a continuous response using predictive data mining or supervised learning methods including logistic regression, k-nearest neighbors, and classification/regression trees.
- Construct and evaluate models using training, validation and test sets.
- Use software to analyze real-world data and communicate results and recommendations.