STA 9890 - Course Learning Objectives

Official Course Description - Statistical Learning for Data Mining

This course applies multiple regression techniques to the increasingly important study of very large data sets. Those techniques include linear and logistic model fitting, inference, and diagnostics. Methods with special applicability for Big Data will be emphasized, such as lasso and ridge regression. Issues of model complexity, the bias-variance tradeoff, and model validation will be studied in the context of large data sets. Methods that rely less on distributional assumptions are also introduced, including cross-validation, bootstrap resampling, and nonparametric methods. Students will learn dimension reduction methods, correlation analysis, and random forests.

Course Learning Objectives

Students successfully completing STA 9890 will be able to:

  1. Identify Key ML Tasks and Trade-Offs
  2. Use Regression and Classification Tools to Develop Interpretable and Accurate Predictive Models
  3. Develop and Apply Ensemble Learning Strategies
  4. Accurately Assess Model Performance Using CV, Hold-Out, Stability, and Bootstrap Techniques
  5. Use Unsupervised Methods to Find Meaningful Structure in Data
  6. Apply Statistical Machine Learning Methods to Novel Data Structures and Types
  7. Generate and Communicate Insights via Statistical Machine Learning Methods

The following course elements contribute to these goals:

Contribution of Course Elements to Learning Goals
Learning Goal 1 Learning Goal 2 Learning Goal 3 Learning Goal 4 Learning Goal 5 Learning Goal 6 Learning Goal 7
Test #01 ✓ ✓ ✓
Research Report #01 ✓ ✓ ✓
Test #02 ✓ ✓ ✓ ✓
Research Report #02 ✓ ✓ ✓ ✓
Test #03 ✓ ✓ ✓ ✓
Research Report #03 ✓ ✓ ✓ ✓ ✓
Course Competition ✓ ✓ ✓ ✓ ✓ ✓
In-Class Presentation ✓ ✓ ✓

Program Learning Goals

This course contributes to the program learning goals of several MS programs offered by the Zicklin School of Business.

MS in Business Analytics

This course is intended to contribute to the following Program Learning Goals for the MS in Business Analytics:

Learning Goal MS BA Learning Goal Description
Data Management Students will be able to apply methods, tools, and software for acquiring, managing/storing, and accessing structured and unstructured data. Students will also demonstrate knowledge of the strategic uses of data.
✓ Foundational Statistical / Quantitative Skills Students will be able to prepare data for statistical analysis, perform basic exploratory and descriptive analysis as well as employ foundational statistical techniques needed to analyze data.
✓ Advanced Statistical/Quantitative Skills Students will be able to build and interpret advanced predictive models. Students will be able to combine business rules and mathematical models to optimize business decisions from data.
✓ Ethical Awareness Students will be able to articulate an understanding of ethical issues in all phases of business analytics with particular emphasis on the new possibilities afforded by the emergence of big data.
✓ Professional Communication Students will be able to explain complex analytical models and their results orally and in writing to technical and non-technical/lay audiences.
✓ Knowledge Integration Students will be able to apply the three key types of analytics (descriptive, predictive, and prescriptive) in a business domain to add value to business decision-making.

MS in Quantitative Methods & Modeling

This course is intended to contribute to the following Program Learning Goals for the MS in Quantitative Methods & Modeling:

Learning Goal MS QMM Learning Goal Description
✓ Operations Research & Mathematical Modeling Students will be able to effectively model, evaluate, and solve quantitative (business) problems using quantitative modeling methods (e.g., deterministic and probabilistic operations research techniques).
✓ Statistics Students will be able to correctly apply appropriate statistical methods when defining, solving, and analyzing problems.
✓ Technology Competency Students will be able to use current technological tools, including spreadsheets and specialized software, when solving problems.
✓ Professional Communication Students will be able to effectively communicate their problem solving methods and solutions to technical and non-technical audiences.

MS in Statistics

This course is intended to contribute to the following Program Learning Goals for the MS in Statistics:

Learning Goal MS Statistics Learning Goal Description
✓ General Statistical Competence Students will be able to apply appropriate probability models and statistical techniques when analyzing problems form business and the other fields.
✓ Statistical Practice Students will become familiar with the standard tools of statistical practice for multiple regression, along with the tools of a subset of specialized statistical areas such as multivariate analysis, applied sampling, time series analysis, experimental design, data mining, categorical analysis, and/or stochastic processes.
✓ Technology Competency Students will learn to use one or more of the benchmark statistical software platforms, such as SAS or R.