Graduate Certificate in Crime Analysis
Available on campus and online, the Graduate Certificate in Crime Analysis at Boston University’s Metropolitan College provides students with a set of courses that develops their ability to use and analyze a variety of data sources to inform the investigations, strategies, and policy decisions of criminal justice organizations. This set of skills is crucial to a growing field within law enforcement and related domains in criminal justice. Data-driven and intelligence-led approaches to crime have become the standard among contemporary criminal justice organizations. The certificate prepares students to fill in-house crime analyst roles or similar positions, while strengthening the skills of students working in investigations, management, and operations to utilize analysis more effectively.
Students who complete the Graduate Certificate in Crime Analysis will be able to:
- Understand the wide variety of data sources available for crime and intelligence analysis, including the methods of data collection, uses, strengths, and limitations.
- Prepare different sources of data for analysis (e.g. data reorganization, matching) for use in analysis processes.
- Conduct analysis using a variety of different techniques, including mapping and spatial analysis and other advanced techniques.
- Incorporate analyses into effective written and oral reports that are useful to investigation, strategy, and policy decisions within law enforcement organizations.
- Comprehend the ethical and legal rules and values that govern crime analysis within law enforcement organizations operating in a democratic society.
- Inform effective data-driven or intelligence-led investigations, strategies, and policies based on awareness of contemporary law enforcement and security approaches.
Graduate Certificate in Crime Analysis Program Options
Available on campus and in the following format:
Official transcripts of previous academic work, three letters of recommendation, personal statement, and résumé are required as part of the application.
The minimum passing grade for a course in the graduate certificate program is B– (2.7), but an average grade of B (3.0) must be maintained to be in good academic standing and satisfy the certificate requirements.
Application Fee Waiver
Current members of the American Jail Association (AJA) are eligible for a Graduate Application fee waiver ($25 for applications to the graduate certificate), and should email email@example.com with information confirming AJA membership.
Transfer of Credits to Degree Programs
All credits earned toward the Graduate Certificate in Crime Analysis may be applied to the Metropolitan College Master of Science in Criminal Justice program.
In addition to the below courses, students are also required to maintain an e-portfolio of the work they produce throughout the program. For more information, please visit this page.
(Four courses/16 credits)
MET CJ 591 Applied Analytical Methods
o Evidence-based and data-driven approaches to crime problems are the industry standard among criminal justice agencies and non-governmental organizations. This course will cover a variety of statistical "tools" from three broad areas: (1) descriptive statistics, (2) inferential statistics and hypothesis testing, and (3) measures of association. Students will learn how to develop research questions, describe and draw conclusions from quantitative data, and interpret statistical research findings, and be able to present these findings to a variety of audiences in a clear and accurate way -- to be able to "tell a story" with numbers. In addition, students will develop a proficiency working with large data sets and conducting analysis with a critical lens, using the analytical software -- Statistical Package for the Social Sciences (SPSS) -- commonly used in criminal justice and related fields. [ 4 cr. ]Fall 2020
MET CJ 612 Crime and Intelligence Analysis
o Contemporary law enforcement agencies regularly employ crime and intelligence analysis to develop and inform effective responses to crime. This course provides an in-depth examination of crime and intelligence analysis techniques. It also explores the role of the crime and intelligence analyst within law enforcement organizations and processes, the historical evolution of this approach, key legal and policy issues, and challenges to implementation. Students have the opportunity to apply these skills to case study simulations involving an array of common crime problems and cases using real-world examples and sources of information. [ 4 cr. ]Fall 2020
|A1||IND||Cronin||SCI 115||T||6:00 pm – 8:45 pm|
MET UA 654 GIS and Spatial Analysis
Geographic Information Systems for Planners provides an introduction to Geographic Information Systems (GIS) specifically with a focus on applications in urban planning. The role of spatial analysis in local, state and regional planning has steadily increased over the last decade with the infusion of windows-based GIS software such as ESRI ArcGIS. The class focus is to prepare students to feel comfortable communicating with other GIS users, research spatial data, and produce high quality digital maps in an applied learning environment. [ 4 cr. ]
Plus one elective from the following list:
MET CJ 801 Special Project in Criminal Justice
Individual faculty supervision of an independent student project demonstrating application of previous program coursework to a selected topic, issue, or theme in criminal justice. [ 4 cr. ]
MET CS 521 Information Structures with Python
This course covers the concepts of the object-oriented approach to software design and development using the Python programming language. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course students will be capable of applying software engineering principles to design and implement Python applications that can be used in conjunction with analytics and big data. Prerequisite: MET CS 300, or instructor's consent. [ 4 cr. ]Fall 2020
|A1||IND||Lu||STH 113||M||6:00 pm – 8:45 pm|
|A2||IND||Pinsky||COM 217||W||6:00 pm – 8:45 pm|
|A3||IND||Pinsky||KCB 102||R||12:30 pm – 3:15 pm|
|A4||IND||Aleksandrov||CAS 201||R||6:00 pm – 8:45 pm|
MET CS 526 Data Structures and Algorithms
This course covers and relates fundamental components of programs. Students use various data structures to solve computational problems, and implement data structures using a high-level programming language. Algorithms are created, decomposed, and expressed as pseudocode. The running time of various algorithms and their computational complexity are analyzed. Prerequisite: MET CS300 and either MET CS520 or MET CS521, or instructor's consent. [ 4 cr. ]Fall 2020
|A1||IND||Lee||CAS 208||M||6:00 pm – 8:45 pm|
MET CS 555 Data Analysis and Visualization with R
This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples. Recommended Prerequisite: MET CS 544 or equivalent knowledge, or instructor's consent. [ 4 cr. ]Fall 2020
|A1||IND||Raghu||CGS 527||W||6:00 pm – 8:45 pm|
|A2||IND||Alaghemandi||CAS 226||R||6:00 pm – 8:45 pm|
|A3||IND||Staff||HAR 408||R||12:30 pm – 3:15 pm|
MET CS 677 Data Science with Python
Students will learn major Python tools and techniques for data analysis. There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems. In addition, students will choose a topic for a final project and present it on the last day of class. Prerequisite: MET CS 521 or equivalent. Or, instructor's consent. [ 4 cr. ]Fall 2020
|A1||IND||Pinsky||PSY B51||M||6:00 pm – 8:45 pm|
|A2||IND||Kalathur||SOC B57||M||6:00 pm – 8:45 pm|
MET CS 699 Data Mining
The goal of this course is to study basic concepts and techniques of data mining. The topics include data preparation, classification, performance evaluation, association rule mining, and clustering. We will discuss basic data mining algorithms in the class and students will practice data mining techniques using data mining software. Students will use Weka and SQL Server or Oracle. Prereq: CS 546 and either CS 579 or CS 669. Or instructor's consent. [ 4 cr. ]Fall 2020
|A1||IND||Lee||CAS 201||T||6:00 pm – 8:45 pm|
View all graduate-level Criminal Justice courses.