1. Course Structure
Course Description:
What is the description of your course?
(e.g. course catalog course description)
Strategic application of technology to data analysis. Introduction to cutting edge software including predictive and spatial analytics. Principles of data
visualization. Application of analytics and visualization in solving justice and public safety problems. This course gives a strong introduction to data collection,
analysis and production of usable information output. Students are exposed to software and strategies related to data analysis for the purpose of creating
actionable intelligence. Throughout the course, students will learn the importance of aligning the use of information technologies.
Assigned Textbook(s)
Please provide the Textbook Citation(s)
including the ISBN-10 and ISBN-13
Required Textbooks:
Strickland, J. (2015). Predictive Analytics using R. Lulu.com
ISBN: 9781312841017
This book is about predictive analytics. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this a survey of
predictive modeling. A predictive model is a statistical model or machine learning model used to predict future behavior based on past behavior.
In order to use this book, one should have a basic understanding of mathematical statistics — it is an advanced book. Some theoretical foundations are laid out
but not proven, but references are provided for additional coverage. Every chapter culminates in an example using R. R is a free software environment for
statistical computing and graphics. You may download R, from a preferred CRAN mirror at http://www.r-project.org/.
The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications:
uplift modeling and time series. One could use this a textbook with problem solving in R—but there are no “by-hand” exercises.
Major Project
Will there be a major project? If so, please
provide a brief synopsis of the project –
briefly describe the deliverables, the
objective of the assignment, and an
overview of the project structure.
Weeks 7 & 8: Project capstone for modeling and analysis of cyber-crime data using R for weeks 2 through 5. Complete analysis including data mining, data
reduction, data modeling (predictive), model analysis and interpretation. Must contain executive summary and support analysis results. Students will find a
source of cyber-crime data on Course Web (provided by author). Dependent variables include binary, categorical and interval. Independent variables include:
Age (13-100), Dwelling (House or Apartment, City Rural (City or Rural), Computer (Yes or No), Tablet (Yes or No), Cell_Phone (Yes or No), HH_Income
(HH=household/ High, Medium or Low), No of_CC (CC=credit cards/0-5), Social_Net (Yes or No) and Social_Net_Acct (FB=Facebook or LI=LinkedIn), plus twenty
more.
George Washington University – Analytics for Strategic Cyber Operations Master's Program