STA/OPR 9750 - Course Learning Objectives

Official Course Description

STA 9750

This course provides an understanding of the principles and concepts of using computer tools for data analysis and visualization. Students will learn to use a scientific programming language (such as R) to import and export data from and into spreadsheets or other statistical software packages. They will gain experience in analyzing both quantitative and qualitative data, and statistical modelling techniques will be introduced. Written reports will prepare students for clear communication of their analysis in professional settings. This course is designed primarily for Masters’ students in statistics and quantitative methods and modeling (QMM), and those interested in carrying out sophisticated statistical analyses of data using statistical software.

OPR 9750

This course provides an understanding of the principles and concepts of using computer tools for data analysis. Students will learn to use the SAS programming language to handle the collection, editing and storing of large datasets, as well as to simulate data, import and export data from and into spreadsheets or other statistical software packages. They will gain experience in analyzing both quantitative and qualitative data, as well as repeated measure data. Written projects and class presentation will prepare students for clear communication of their analysis in professional settings. This course is designed primarily for statistics and quantitative methods and modeling (QMM) majors, PhD candidates, and those interested in carrying out sophisticated statistical analyses of data using statistical software.

Instructor’s Note: Contra the official OPR 9750 description, this course will be taught using R not SAS. STA 9750 and OPR 9750 will be jointly taught and graded. Please consult with your degree program director to determine which listing is appropriate for you.

Course Learning Objectives

Students successfully completing STA/OPR 9750 will be able to:

Effectively communicate the reuslts of data analyses.
Manipulate tabular data in R
Develop effective and compelling visualizations using standard statistical software
Manipulate `wild-caught’ data from web-based sources
Use computational approaches to statistical inference
Develop novel analytical products to convey actionable insights.

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
Mini Project #00	✓
Mini Project #01	✓	✓
Mini Project #02	✓	✓	✓
Mini Project #03	✓	✓	✓	✓
Mini Project #04	✓	✓	✓	✓	✓
Course Project	✓	✓	✓	✓	✓	✓

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 contributes to the following Program Learning Goals for the MS in Business Analytics:

MSBA Program Learning Goals
STA/OPR 9750 Learning Goal	MSBA 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 contributes to the following Program Learning Goals for the MS in Quantitative Methods & Modeling:

MSQMM Program Learning Goals
STA/OPR 9750 Learning Goal	MSQMM 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 contributes to the following Program Learning Goals for the MS in Statistics:

MS Statistics Program Learning Goals
STA/OPR 9750 Learning Goal	MS Stat Learning Goal	Description
✓	General Statistical Competence	Students will be able to apply appropriate probability models and statistical techniques when analyzing problems from business and 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.