Learning Objectives

Students will:

Learning Outcomes

learn techniques of statistical modeling

Presented with data, students will choose the appropriate modeling technique, build the model, check validity of the model and revise if necessary, and employ the model for estimation and prediction.

acquire solid data analysis skills

Students will summarize and present data in meaningful ways, test for relationships within data, test hypotheses, and carry out modeling techniques as described above

learn to communicate their results effectively to others, including non-experts

Students will propose and carry out projects, presenting results in written and/or oral form.

understand the analysis process

Given the intent of a study, students will identify data needed,  the appropriate instruments needed to collect the data, and the means of analysis necessary to carry out the study

have the technical knowledge required to understand the meaning of publications containing statistical results

Students will interpret statistics commonly used in applied research (in various fields)  and critique their use and interpretation. Students will define and critique commonly occurring experimental and sampling techniques, and their uses.

have hands-on experience with analyzing diverse data types, using modern statistical computer tools.

Students will use the modern statistical computing environments  SAS and R to carry out the analysis of data.

Know what it means to have “good quality data”

Be aware of  the importance of information as an organizational asset, and the importance of data quality in assessing the value of information

Gain a practical methodology for achieving measurable data quality in information technology based on best practices in national and global literature.

Learn a structured approach for finding incorrect data in a record set too large for the records to be examined one by one

Explain the different perspectives from which data is judged, and the dimensions by which its quality may be measured

In the context of real-data projects articulate and explain the value and effect of correct and clean data for decision support

Carry out data-quality analyses of large data sets

Know how to design and implement a database, and use managements systems effectively, understand major model of databases.

Students will describe and design databases using entity-relationship modeling, design normalized databases, write complex queries in SQL, use a DBMS to create, store and retrieve information.

(Elective) Students will gain an overview of the state of the art in data mining methods and a have a hands-on application of these methods to describe and make predictions about sets of data.

Students will examine real world data mining questions using approaches such as the use of statistical and linear models, decision trees, association rules, instanced-based learning, clustering and Bayesian classifiers.

(Elective) Students will learn the project management skills needed to effectively manage development projects that involve computer hardware, software and telecommunications technology

Working in groups, students will examine real-life case studies, analyze them and discuss how they relate to course material. Successful and unsuccessful projects will be examined.

Students will write a group research project.

Students will complete a comprehensive assignment using key features of Microsoft Project.