Big Data & Business Analytics

 

Big Data and Business Analytics (BDBA) Major Requirements

Learn more about this major

The Big Data and Business Analytics (BDBA) major is designed to teach the theoretical and practical aspects of big data and business analytics through a curriculum focused on a mathematical (statistical), technical (including architecture and analytics), and communication components required in the business analytics field. The goal of the BDBA major is to educate students who can successfully fulfill the roles and responsibilities of the rapidly growing field related to business analytics. Through the BDBA curriculum, graduates will be equipped with the knowledge, skills, and abilities (KSA's) to obtain employment in a position such as a business analyst.

The BSBA in Big Data and Business Analytics requires completion of a minimum of 21 credit hours (7 classes) in Big Data and Business Analytics. A cumulative grade point average of at least 2.0 in the Big Data and Business Analytics major and a cumulative grade point average of 2.0 overall must be maintained to graduate.

Degree Requirements - 124 credits

Students can earn a bachelor of science in business administration with this major. See the requirements for the bachelor of science in business administration degree.

Required Courses (5 courses, 16 credits)

 

Credits:

3.00

Description:

Provides students with a comprehensive introduction to the core concepts applications and tools of data acquisition preparation querying analytics and data management. Students gain hands-on experience using real data to perform these functions. Topics include: data life cycle big data analytics data collection preparation organization and storage aggregation and summary and presentation/visualization. Students use tools such as MS Excel MS Access SQL and SAS Visual Analytics.

Prerequisites:

STATS-240 or STATS-250 or Instructor Permission

Credits:

3.00

Description:

Provides an understanding of the business potential of big data; how to build and maintain data warehouses and how to analyze and use this data as a source for business intelligence and competitive advantage. Students study data mining concepts and the use of analytics tools and methods for producing business knowledge. Topics include: extraction transformation and loading; decision support systems; analytics text web and data mining models as well as data presentation/visualization including dashboards and scorecards. Students build a data warehouse and practice the extraction and filtering process used to produce high quality data warehouses. Students will use tools such as MS Excel Tableau SQL and SAP Business Warehouse.

Prerequisites:

ISOM-130, ISOM-230, and STATS-240 or STATS-250 or Instructor Permission

Credits:

3.00

Description:

When companies make decisions they do so with the future in mind and essentially are predicting that their decisions will achieve desired results. Predictive analytics allow people to ask and answer questions that can predict demand and/or outcomes and obtain results that lead to reasoned action. This course develops students' capability in applying the core concepts and techniques of predictive analytics for opportunity identification and risk assessment within the context of organizational decision-making. Students will use data-driven approaches to develop predictive analytical models. Students will create and use data models and techniques apply trendlines to fit models to data perform what-if analysis construct data tables evaluate scenarios apply forecasting techniques simulation and risk analysis. Students will learn to use various presentation and visualization tools to communicate results. Topics include: predictive analytics life cycle opportunity/issue identification data preparation modeling analysis forecasting simulation risk assessment and operationalization of predictive analytics.

Prerequisites:

STATS-250 or STATS-240 or MATH-255 or permission of instructor

Credits:

4.00

Description:

This course begins with a brief review of statistical methods including probability theory estimation and hypothesis testing. This background is used in the construction estimation and testing of econometric models. The consequences of a misspecified model where the assumptions of a classical regression model are violated are studied and the appropriate remedial measures are suggested. Other topics include dummy variables binary choice models and autoregressive models. Emphasis is on applied aspects of econometric modeling. There is extensive use of statistical software for data analyses. Normally offered every year.

Choose one of the following:

Prerequisites:

STATS-240 or STATS-250

Credits:

3.00

Description:

Introduces a detailed overview of statistical learning for data mining inference and prediction in order to tackle modern-day data analysis problems. This course is appropriate for students who wish to learn and apply statistical learning tools to analyze data and gain valuable hands-on experience with R. Statistical learning refers to a vast set of tools for modeling and understanding complex datasets. Exciting topics include: Regression Logistic Regression Linear Discriminant Analysis Cross-Validation Bootstrap Linear/Non-Linear Model Selection and Regularization Support Vector Methodology and Unsupervised Learning via Principal Components Analysis and Clustering Methods. Students learn how to implement each of the statistical learning methods using the popular statistical software package R via hands-on lab sessions.

Prerequisites:

ISOM-210

Credits:

3.00

Description:

Provides an understanding of the role of information and databases in information systems and their role as an organizational resource. Students learn to design databases using normalization and entity-relationship diagrams develop data models and to build applications with database management systems such as MS Access and SQL. Techniques are examined and applied to realistic business problems through hands-on exercises and projects.

Elective Courses (2 courses, 6 credit minimum)

Choose two (2) courses from the following list. You may also take a 300-level or higher, 3-credit course with your advisor's approval.

Prerequisites:

STATS-240 or STATS-250

Credits:

3.00

Description:

Introduces a detailed overview of statistical learning for data mining inference and prediction in order to tackle modern-day data analysis problems. This course is appropriate for students who wish to learn and apply statistical learning tools to analyze data and gain valuable hands-on experience with R. Statistical learning refers to a vast set of tools for modeling and understanding complex datasets. Exciting topics include: Regression Logistic Regression Linear Discriminant Analysis Cross-Validation Bootstrap Linear/Non-Linear Model Selection and Regularization Support Vector Methodology and Unsupervised Learning via Principal Components Analysis and Clustering Methods. Students learn how to implement each of the statistical learning methods using the popular statistical software package R via hands-on lab sessions.

Credits:

3.00

Description:

Students will analyze and evaluate privacy risks facing individual and organizational data and then design and evaluate solutions to protect the data. The course starts by introducing students to basic data privacy principles and the deteriorating state of privacy with frequent data breaches and identity theft explosion. The course then explores the disruption to privacy caused by emerging technologies like mobile cloud big data and social media and the consequences. Different privacy solutions including privacy enhancing technologies like Tors Onions and encryption will be introduced. Various US Data privacy laws like HIPAA are explored and then compared to the European general data privacy regulation (GDPR) regime. The course ends by introducing different data privacy best practices and the "Privacy by Design" paradigm.

Credits:

3.00

Description:

Develops problem solving and basic programming skills through a variety of business application assignments. Introduces fundamental control and data structures using the Python programming language. Students learn about the concepts of modern business programming principles. The course builds skills in the areas of programming logic data structures control structures and system development. Testing and debugging techniques and the writing of well-structured code are emphasized.

Prerequisites:

ISOM-210

Credits:

3.00

Description:

Provides an understanding of the role of information and databases in information systems and their role as an organizational resource. Students learn to design databases using normalization and entity-relationship diagrams develop data models and to build applications with database management systems such as MS Access and SQL. Techniques are examined and applied to realistic business problems through hands-on exercises and projects.

Prerequisites:

ISOM-210 and at least 54 credits

Credits:

3.00

Description:

Introduces Cybersecurity fundamental principles from a risk management approach both at the national and global levels. Common types of computer attacks and counter-attacks are addressed. Security technologies such as biometrics firewalls intrusion detection systems and cryptography systems will be analyzed and several hands-one lab exercises on the same are used to connect theory to practice and provide experiential learning. Best practices for Risk analysis and business continuity planning and common frameworks like the CIA triangle and the defence in depth solution are applied to different scenarios.

Prerequisites:

ISOM-210 and at least 54 credits

Credits:

3.00

Description:

Provides a conceptual as well as a mechanical understanding of enterprise integration and enterprise software business process reengineering and strategies for maximizing benefits from enterprise systems. Students lean to examine complex issues in organizational changes including implementation challenge; risks costs and benefits; learning and knowledge management. Hands-on lab projects on the ERP System (provided by SAP) are utilized to reinforce understanding of important enterprise systems and business process concepts. This course is part of the SAP Student Recognition Certificate Program.

Prerequisites:

CMPSC-F132 and 1 of the following: STATS-240, STATS-250, MATH-134, MATH-165, MATH-164 or MATH-255.

Credits:

4.00

Description:

The field of data science is emerging at the intersection of the fields of social science and statistics information and computer science and design. Data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. This course serves as a project-based introduction to data science in Python language covering data organization and retrieval statistical data processing and data visualization.

Prerequisites:

MKT-210 and either MKT-220 or MKT-H221

Credits:

3.00

Description:

In this course students will learn a digitally driven approach to marketing analytics an exciting field undergoing explosive growth and high demand. An emphasis will be placed on the practical methods used to measure manage and analyze consumer information. Topics covered will include making sense of the digital media landscape demand forecasting and predictive analytics performance evaluation and Google Analytics. Upon graduation of this course students will have gained a set of skills and certification that directly translates to modern marketing practices.

Information Systems/Big Data and Business Analytics Practicum

Practical information systems and data analytics experience prepares students for real-world challenges in the workplace. All IS and BDBA majors much complete 150 hours of approved professional information systems and big data business analytics experience before graduation. The 150 hours of work experience may be obtained in one or more positions as an intern, part- or full-time employee or volunteer. Prior approval of your position by the IS Practical Experience Coordinator is required. This is accomplished by completing the IS Practicum Approval Form.

Most students satisfy this graduation requirement by completing ISOM 560: IS Practicum, a non-credit, tuition-free, pass/fail course. Student should enroll in ISOM 560 the semester when they expect to complete their 150 hours or the subsequent semester. Students may also satisfy this practicum requirement by enrolling in ISOM 520: IS Internship (1 to 3 credits based on the number of hours worked). ISOM 520 requires junior standing and is a graded course that can only be used as a free elective (cannot be used as a major elective).

Prerequisites:

ISOM-210, 1 required ISOM major course, at least a 3.0 GPA, and Instructor Permission

Credits:

0.00- 3.00

Description:

An internship may be used to satisfy the IS major practical experience requirement of a minimum of 150 hours of information systems/information technology experience. Most internships will exceed 150 hours and may be paid or unpaid. Prior approval of your position by the IS Practical Experience Coordinator is required. This is accomplished by completing the IS Practicum Approval Form with an internship description. The internship description includes the job description the number of hours of work the number of credits grading criteria and any other requirements. Students should enroll in ISOM 520 prior to starting their internship. This is a graded course and cannot be used as a major elective. Students may decide to register for this free elective course as pass fail (see http://www.suffolk.edu/business/departments/11704. php). Prerequisites: Practical Experience Coordinator's Approval Required and Junior Standing minimum ISOM GPA of 3.0 and minimum overall GPA of 2.5.

Prerequisites:

ISOM-210, 1 required ISOM major course, at least 54 credits, and Instructor Permission

Credits:

0.00

Description:

All Information Systems majors are required to complete 150 hours of information systems/information technology experience. The 150 hours of work experience may be obtained in one or more positions as an intern part- or full-time employee or volunteer. Prior approval of your position by the IS Practical Experience Coordinator is required. This is accomplished by completing the IS Practicum Approval Form. Students should enroll in ISOM 560 no earlier than the semester when they expect to complete the 150 hours. Student should log their work tasks and accomplishments. Prerequisites: Practical Experience Coordinator's Approval Required

Learning Goals & Objectives

Learning goals and objectives reflect the educational outcomes achieved by students through the completion of this program. These transferable skills prepare Suffolk students for success in the workplace, in graduate school, and in their local and global communities.

Learning Goals
Learning Objectives
Students will… Upon completion of the program, each student should be able to...
Demonstrate understanding of theories and concepts of big data and analytics.
(Knowledge)
  • Identify the main theories and concepts of big data and analytics.
  • Understand the architecture and technical infrastructure of business intelligence and big data analytics.
  • Understand analytical techniques used to generate descriptive and predictive models.
Demonstrate skills in utilizing business analytics technology and techniques.
(Skill)
  • Utilize many of the most popular business intelligence tools in industry.
  • Utilize the architectural technologies used in generating business intelligence and big data analytics.
  • Manage, extract, transform and load data as well as provide data visualizations.
Demonstrate the ability to generate, evaluate and communicate business analytics solutions.
(Ability)
  • Access data and develop business intelligence solutions.
  • Develop analytical models using data mining tools and be able to communicate the results to decision makers.
  • Analyze, develop and critically assess data mining and predictive analysis models and projects.
  • Evaluate analytical models from various industries and business functions.
  • Communicate models developed using big data and analytical tools.
  • Have a multidisciplinary perspective of the mathematical, technical and communication knowledge, and skill set.

Big Data and Business Analytics Minor Requirements

Learn more about this minor

Big Data and Business Analytics Minor for Business Students (3 courses, 9 credits)

Students are required to take the following:

Credits:

3.00

Description:

Provides students with a comprehensive introduction to the core concepts applications and tools of data acquisition preparation querying analytics and data management. Students gain hands-on experience using real data to perform these functions. Topics include: data life cycle big data analytics data collection preparation organization and storage aggregation and summary and presentation/visualization. Students use tools such as MS Excel MS Access SQL and SAS Visual Analytics.

Prerequisites:

STATS-240 or STATS-250 or Instructor Permission

Credits:

3.00

Description:

Provides an understanding of the business potential of big data; how to build and maintain data warehouses and how to analyze and use this data as a source for business intelligence and competitive advantage. Students study data mining concepts and the use of analytics tools and methods for producing business knowledge. Topics include: extraction transformation and loading; decision support systems; analytics text web and data mining models as well as data presentation/visualization including dashboards and scorecards. Students build a data warehouse and practice the extraction and filtering process used to produce high quality data warehouses. Students will use tools such as MS Excel Tableau SQL and SAP Business Warehouse.

Prerequisites:

ISOM-130, ISOM-230, and STATS-240 or STATS-250 or Instructor Permission

Credits:

3.00

Description:

When companies make decisions they do so with the future in mind and essentially are predicting that their decisions will achieve desired results. Predictive analytics allow people to ask and answer questions that can predict demand and/or outcomes and obtain results that lead to reasoned action. This course develops students' capability in applying the core concepts and techniques of predictive analytics for opportunity identification and risk assessment within the context of organizational decision-making. Students will use data-driven approaches to develop predictive analytical models. Students will create and use data models and techniques apply trendlines to fit models to data perform what-if analysis construct data tables evaluate scenarios apply forecasting techniques simulation and risk analysis. Students will learn to use various presentation and visualization tools to communicate results. Topics include: predictive analytics life cycle opportunity/issue identification data preparation modeling analysis forecasting simulation risk assessment and operationalization of predictive analytics.

Big Data and Business Analytics Minor for College of Arts & Sciences Students (5 courses, 16 credits)

Required Course:

Prerequisites:

Take concurrently with SBS-100. Transfer sections do not require the co-requisite.

Credits:

3.00

Description:

This course introduces students to foundational concepts in business including functional areas the life cycle competition stakeholders and ethical considerations. Students develop critical thinking by learning and using a problem solving process through a business situation analysis model to analyze various situations that confront managers and founders of small medium and large organizations. Students will also develop tools for analysis allowing them to critically view business in a new and thoughtful way. The class culminates with student- teams presenting a detailed analysis and recommendations to a panel of executives and persuading them that the recommended strategy is not only feasible but also practical for the stakeholders involved.

After SBS-101 Business Foundations, CAS students are required to take an approved statistics course and the following three (3) courses:

Credits:

3.00

Description:

Provides students with a comprehensive introduction to the core concepts applications and tools of data acquisition preparation querying analytics and data management. Students gain hands-on experience using real data to perform these functions. Topics include: data life cycle big data analytics data collection preparation organization and storage aggregation and summary and presentation/visualization. Students use tools such as MS Excel MS Access SQL and SAS Visual Analytics.

Prerequisites:

STATS-240 or STATS-250 or Instructor Permission

Credits:

3.00

Description:

Provides an understanding of the business potential of big data; how to build and maintain data warehouses and how to analyze and use this data as a source for business intelligence and competitive advantage. Students study data mining concepts and the use of analytics tools and methods for producing business knowledge. Topics include: extraction transformation and loading; decision support systems; analytics text web and data mining models as well as data presentation/visualization including dashboards and scorecards. Students build a data warehouse and practice the extraction and filtering process used to produce high quality data warehouses. Students will use tools such as MS Excel Tableau SQL and SAP Business Warehouse.

Prerequisites:

ISOM-130, ISOM-230, and STATS-240 or STATS-250 or Instructor Permission

Credits:

3.00

Description:

When companies make decisions they do so with the future in mind and essentially are predicting that their decisions will achieve desired results. Predictive analytics allow people to ask and answer questions that can predict demand and/or outcomes and obtain results that lead to reasoned action. This course develops students' capability in applying the core concepts and techniques of predictive analytics for opportunity identification and risk assessment within the context of organizational decision-making. Students will use data-driven approaches to develop predictive analytical models. Students will create and use data models and techniques apply trendlines to fit models to data perform what-if analysis construct data tables evaluate scenarios apply forecasting techniques simulation and risk analysis. Students will learn to use various presentation and visualization tools to communicate results. Topics include: predictive analytics life cycle opportunity/issue identification data preparation modeling analysis forecasting simulation risk assessment and operationalization of predictive analytics.

*In addition to the courses listed above, students are required to take an approved statistics course before taking ISOM 230 and ISOM 330. For more information, please email the Information Systems and Operations Management Department or call 617-573-8331.

Accelerated Degrees

If you’re earning an undergraduate business degree at Suffolk or another U.S. institution, you may qualify to earn both your Bachelor’s and Master’s degrees in just 5 years.

Cybersecurity Minor for Business Majors

A business student may choose to minor in Cybersecurity by completing the following requirements:

Required Courses

Credits:

3.00

Description:

Students will analyze and evaluate privacy risks facing individual and organizational data and then design and evaluate solutions to protect the data. The course starts by introducing students to basic data privacy principles and the deteriorating state of privacy with frequent data breaches and identity theft explosion. The course then explores the disruption to privacy caused by emerging technologies like mobile cloud big data and social media and the consequences. Different privacy solutions including privacy enhancing technologies like Tors Onions and encryption will be introduced. Various US Data privacy laws like HIPAA are explored and then compared to the European general data privacy regulation (GDPR) regime. The course ends by introducing different data privacy best practices and the "Privacy by Design" paradigm.

Prerequisites:

ISOM-210 and at least 54 credits

Credits:

3.00

Description:

Introduces Cybersecurity fundamental principles from a risk management approach both at the national and global levels. Common types of computer attacks and counter-attacks are addressed. Security technologies such as biometrics firewalls intrusion detection systems and cryptography systems will be analyzed and several hands-one lab exercises on the same are used to connect theory to practice and provide experiential learning. Best practices for Risk analysis and business continuity planning and common frameworks like the CIA triangle and the defence in depth solution are applied to different scenarios.

Elective Courses

Choose one of the following:

Credits:

3.00

Description:

Presents an in-depth study of corporate crime and financial fraud. Examines accounting devices and schemes employed to defraud stakeholders failure of industry watchdogs and the regulatory and legislative environment. Topics include: corporate governance corporate finance corporate compliance programs ethical misconduct by outside legal accounting investment and banking professionals Sarbanes Oxley Act Foreign Corrupt Practices Act Organizational Sentencing guidelines mail fraud wire fraud money laundering conspiracy securities violations qui tam litigation(whistleblowers)and financial accounting crimes.

Credits:

3.00

Description:

Study of the varieties of fraud including financial statement fraud fraud against organizations consumer fraud bankruptcy fraud tax fraud and e-commerce fraud. The causes prevention detection and investigation of fraud are explored. Examination of famous past frauds with hands-on cases are used to apply these concepts and to understand the resolution of fraud in the legal system.

Other electives per instructor permission. 

FinTech Minor for BDBA Majors

Required Courses (3 courses, 12 credits)

BDBA students must take:

Credits:

3.00

Description:

Provides a comprehensive introduction to mobile app technology and design concepts. This is an introductory course and assumes no prior programming experience. Students learn how to design build and optimize cross-platform mobile app using HTML5 standards. Students will also learn how to convert HTML5 apps into native apps for various mobile platforms. Students use CSS3 JavaScript and several JavaScript frameworks and techniques such as jQuery jQuery Mobile and AJAX. In addition students will use Web services such as Google Maps and Web Application Programming Interfaces (Web APIs) to integrate content into their apps.

Prerequisites:

STATS-240 or STATS-250

Credits:

3.00

Description:

Introduces a detailed overview of statistical learning for data mining inference and prediction in order to tackle modern-day data analysis problems. This course is appropriate for students who wish to learn and apply statistical learning tools to analyze data and gain valuable hands-on experience with R. Statistical learning refers to a vast set of tools for modeling and understanding complex datasets. Exciting topics include: Regression Logistic Regression Linear Discriminant Analysis Cross-Validation Bootstrap Linear/Non-Linear Model Selection and Regularization Support Vector Methodology and Unsupervised Learning via Principal Components Analysis and Clustering Methods. Students learn how to implement each of the statistical learning methods using the popular statistical software package R via hands-on lab sessions.

Prerequisites:

Take FIN-200. GPA of 3.0 or higher required.

Credits:

3.00

Description:

This course introduces students to the terminology current FinTech themes future challenges and opportunities related to the application of technology to financial services. With an emphasis on case studies and guest lectures the class will discuss datafication alternative finance innovative business models algorithmic trading data-driven decision making mobile-only services robo advisers machine learning artificial intelligence crypto currencies Blockchain RegTech InsureTech cybersecurity and the rise of TechFin's. This course is equivalent to an Honors-level course and should count towards the SBS Honors Program and Finance Honors Program requirements.

Elective Courses (1 course, 3 credits)

And take one from the following list (no double counting of electives is allowed without substitution):

Credits:

3.00

Description:

Students will analyze and evaluate privacy risks facing individual and organizational data and then design and evaluate solutions to protect the data. The course starts by introducing students to basic data privacy principles and the deteriorating state of privacy with frequent data breaches and identity theft explosion. The course then explores the disruption to privacy caused by emerging technologies like mobile cloud big data and social media and the consequences. Different privacy solutions including privacy enhancing technologies like Tors Onions and encryption will be introduced. Various US Data privacy laws like HIPAA are explored and then compared to the European general data privacy regulation (GDPR) regime. The course ends by introducing different data privacy best practices and the "Privacy by Design" paradigm.

Credits:

3

Description:

Equips students with the principles, methodology and skills required to define, develop and deploy a fully functional dynamic web application. Students learn to customize the content, appearance, and delivery of their website using industry-standard web development tools. Class discussion will focus on web development issues for organizations as well as the role played by development tools such as HTML5, CSS3, and PHP scripting. Each class will include hands-on lab work. A term project is used to wrap the course content together.

Prerequisites:

ISOM-210(formerly ISOM-310)

Credits:

3.00

Description:

Covers the concepts techniques and tools used in the analysis and design of business information systems. Topics include: the system development cycle modeling prototyping and project management. Additionally the course focuses upon using Object Oriented analysis and design techniques including the UML. Emphasizes the analysis of business operations as well as the interaction between information systems professionals and end-users. A term project applying these concepts and techniques is required.

Prerequisites:

ISOM-210 and at least 54 credits

Credits:

3.00

Description:

Introduces Cybersecurity fundamental principles from a risk management approach both at the national and global levels. Common types of computer attacks and counter-attacks are addressed. Security technologies such as biometrics firewalls intrusion detection systems and cryptography systems will be analyzed and several hands-one lab exercises on the same are used to connect theory to practice and provide experiential learning. Best practices for Risk analysis and business continuity planning and common frameworks like the CIA triangle and the defence in depth solution are applied to different scenarios.

Prerequisites:

GPA of 3.0 or higher required; previous programming course, or instructor approval.

Credits:

3.00

Description:

In this project-based course students will apply programming language such as Python or R to model financial situations and derive appropriate predictions and policy recommendations. Students will learn to use large data queries natural language processing machine learning data management and other relevant concepts to apply FinTech to achieve process improvements and explore innovations in financial services.

Prerequisites:

FIN-200

Credits:

3.00

Description:

The course introduces students to the management of international financial-services firms and methods through which financial institutions manage risk. The course focuses on concepts and basic tools for identifying measuring and managing risks such as interest rate risk credit risk liquidity risk market risk and operational risk. The course also introduces key regulations and important ethical issues in the financial-services industry.

Cybersecurity Concentration

For IS and BDBA Majors only, To receive this concentration, must take the following courses as their major electives.

Required Courses

Credits:

3.00

Description:

Students will analyze and evaluate privacy risks facing individual and organizational data and then design and evaluate solutions to protect the data. The course starts by introducing students to basic data privacy principles and the deteriorating state of privacy with frequent data breaches and identity theft explosion. The course then explores the disruption to privacy caused by emerging technologies like mobile cloud big data and social media and the consequences. Different privacy solutions including privacy enhancing technologies like Tors Onions and encryption will be introduced. Various US Data privacy laws like HIPAA are explored and then compared to the European general data privacy regulation (GDPR) regime. The course ends by introducing different data privacy best practices and the "Privacy by Design" paradigm.

Prerequisites:

ISOM-210 and at least 54 credits

Credits:

3.00

Description:

Introduces Cybersecurity fundamental principles from a risk management approach both at the national and global levels. Common types of computer attacks and counter-attacks are addressed. Security technologies such as biometrics firewalls intrusion detection systems and cryptography systems will be analyzed and several hands-one lab exercises on the same are used to connect theory to practice and provide experiential learning. Best practices for Risk analysis and business continuity planning and common frameworks like the CIA triangle and the defence in depth solution are applied to different scenarios.

Elective Courses (Choose one of the following)

Credits:

3.00

Description:

Presents an in-depth study of corporate crime and financial fraud. Examines accounting devices and schemes employed to defraud stakeholders failure of industry watchdogs and the regulatory and legislative environment. Topics include: corporate governance corporate finance corporate compliance programs ethical misconduct by outside legal accounting investment and banking professionals Sarbanes Oxley Act Foreign Corrupt Practices Act Organizational Sentencing guidelines mail fraud wire fraud money laundering conspiracy securities violations qui tam litigation(whistleblowers)and financial accounting crimes.

Credits:

3.00

Description:

Study of the varieties of fraud including financial statement fraud fraud against organizations consumer fraud bankruptcy fraud tax fraud and e-commerce fraud. The causes prevention detection and investigation of fraud are explored. Examination of famous past frauds with hands-on cases are used to apply these concepts and to understand the resolution of fraud in the legal system.

Other electives per instructor permission. 

FinTech Concentration for BDBA Majors

For BDBA Majors only. To receive the FinTech Concentration, BDBA majors must take the following 3 (three) courses as their major electives:

Required Courses (3 courses, 12 credits)

BDBA students must take the following courses:

Credits:

3.00

Description:

Provides a comprehensive introduction to mobile app technology and design concepts. This is an introductory course and assumes no prior programming experience. Students learn how to design build and optimize cross-platform mobile app using HTML5 standards. Students will also learn how to convert HTML5 apps into native apps for various mobile platforms. Students use CSS3 JavaScript and several JavaScript frameworks and techniques such as jQuery jQuery Mobile and AJAX. In addition students will use Web services such as Google Maps and Web Application Programming Interfaces (Web APIs) to integrate content into their apps.

Prerequisites:

STATS-240 or STATS-250

Credits:

3.00

Description:

Introduces a detailed overview of statistical learning for data mining inference and prediction in order to tackle modern-day data analysis problems. This course is appropriate for students who wish to learn and apply statistical learning tools to analyze data and gain valuable hands-on experience with R. Statistical learning refers to a vast set of tools for modeling and understanding complex datasets. Exciting topics include: Regression Logistic Regression Linear Discriminant Analysis Cross-Validation Bootstrap Linear/Non-Linear Model Selection and Regularization Support Vector Methodology and Unsupervised Learning via Principal Components Analysis and Clustering Methods. Students learn how to implement each of the statistical learning methods using the popular statistical software package R via hands-on lab sessions.

Prerequisites:

Take FIN-200. GPA of 3.0 or higher required.

Credits:

3.00

Description:

This course introduces students to the terminology current FinTech themes future challenges and opportunities related to the application of technology to financial services. With an emphasis on case studies and guest lectures the class will discuss datafication alternative finance innovative business models algorithmic trading data-driven decision making mobile-only services robo advisers machine learning artificial intelligence crypto currencies Blockchain RegTech InsureTech cybersecurity and the rise of TechFin's. This course is equivalent to an Honors-level course and should count towards the SBS Honors Program and Finance Honors Program requirements.

Elective Course (1 course, 3 credits)

Choose one from the following:

Credits:

3.00

Description:

Students will analyze and evaluate privacy risks facing individual and organizational data and then design and evaluate solutions to protect the data. The course starts by introducing students to basic data privacy principles and the deteriorating state of privacy with frequent data breaches and identity theft explosion. The course then explores the disruption to privacy caused by emerging technologies like mobile cloud big data and social media and the consequences. Different privacy solutions including privacy enhancing technologies like Tors Onions and encryption will be introduced. Various US Data privacy laws like HIPAA are explored and then compared to the European general data privacy regulation (GDPR) regime. The course ends by introducing different data privacy best practices and the "Privacy by Design" paradigm.

Credits:

3

Description:

Equips students with the principles, methodology and skills required to define, develop and deploy a fully functional dynamic web application. Students learn to customize the content, appearance, and delivery of their website using industry-standard web development tools. Class discussion will focus on web development issues for organizations as well as the role played by development tools such as HTML5, CSS3, and PHP scripting. Each class will include hands-on lab work. A term project is used to wrap the course content together.

Prerequisites:

ISOM-210(formerly ISOM-310)

Credits:

3.00

Description:

Covers the concepts techniques and tools used in the analysis and design of business information systems. Topics include: the system development cycle modeling prototyping and project management. Additionally the course focuses upon using Object Oriented analysis and design techniques including the UML. Emphasizes the analysis of business operations as well as the interaction between information systems professionals and end-users. A term project applying these concepts and techniques is required.

Prerequisites:

ISOM-210 and at least 54 credits

Credits:

3.00

Description:

Introduces Cybersecurity fundamental principles from a risk management approach both at the national and global levels. Common types of computer attacks and counter-attacks are addressed. Security technologies such as biometrics firewalls intrusion detection systems and cryptography systems will be analyzed and several hands-one lab exercises on the same are used to connect theory to practice and provide experiential learning. Best practices for Risk analysis and business continuity planning and common frameworks like the CIA triangle and the defence in depth solution are applied to different scenarios.

Prerequisites:

GPA of 3.0 or higher required; previous programming course, or instructor approval.

Credits:

3.00

Description:

In this project-based course students will apply programming language such as Python or R to model financial situations and derive appropriate predictions and policy recommendations. Students will learn to use large data queries natural language processing machine learning data management and other relevant concepts to apply FinTech to achieve process improvements and explore innovations in financial services.

Prerequisites:

FIN-200

Credits:

3.00

Description:

The course introduces students to the management of international financial-services firms and methods through which financial institutions manage risk. The course focuses on concepts and basic tools for identifying measuring and managing risks such as interest rate risk credit risk liquidity risk market risk and operational risk. The course also introduces key regulations and important ethical issues in the financial-services industry.

Big Data and Business Analytics Undergraduate Courses

Prerequisites:

Restricted to students with less than 54 credits. Students with more than 54 credits needing to fulfill their CI requirement should seek approval from the Undergraduate Advising Office.

Credits:

3.00

Description:

Demystifies the creative process by introducing students to creative practice as a disciplined approach to problem-solving and innovation. Students will be encouraged to synthesize existing ideas images concepts and skill sets in original way embrace ambiguity and support divergent thinking and risk taking.

Prerequisites:

3.3 GPA or higher

Credits:

3.00

Description:

Provides students with a comprehensive introduction to the core concepts applications and tools of data acquisition preparation querying analytics and data management. Students gain hands-on experience using real data to perform these functions. Topics include: data life cycle big data analytics data collection preparation organization and storage aggregation and summary and presentation/visualization. Students use tools such as MS Excel MS Access SQL and SAS Visual Analytics.

Credits:

3.00

Description:

Provides students with a comprehensive introduction to the core concepts applications and tools of data acquisition preparation querying analytics and data management. Students gain hands-on experience using real data to perform these functions. Topics include: data life cycle big data analytics data collection preparation organization and storage aggregation and summary and presentation/visualization. Students use tools such as MS Excel MS Access SQL and SAS Visual Analytics.

Prerequisites:

MATH-128 or higher and STATS-240 or STATS-250

Credits:

3.00

Description:

Introduces fundamental quantitative methods of using data to make informed management decisions. Topics include: decision modeling decision analysis regression forecasting optimization and simulation as it applies to the study and analysis of business problems for decision support in finance marketing service and manufacturing operations. Practical business cases and examples drawn from finance marketing operations management and other management areas are used to provide students with a perspective on how management science is used in practice. Excel spreadsheets are used extensively to implement decision models.

Prerequisites:

WRI-101 or WRI-H103 and SBS-101 and at least a 3.3 GPA

Credits:

3.00

Description:

Examines the rise of information-enabled enterprises and the role of information technologies/information systems (IT/IS) and e-commerce as key enablers of businesses and social changes globally. Topics include: the effective application of IT/IS to support strategic planning managerial control operations and business process integration in the digital economy IT/IS related issues of ethics and piracy and security in the information society.

Prerequisites:

WRI-101 and ENT-101 and at least 24 completed credits

Credits:

3.00

Description:

Examines the rise of information-enabled enterprises and the role of information technologies/information systems (IT/IS) and e-commerce as key enablers of businesses and social changes globally. Topics include: the effective application of IT/IS to support strategic planning managerial control operations and business process integration in the digital economy IT/IS related issues of ethics and piracy and security in the information society.

Credits:

3.00

Description:

Provides a comprehensive introduction to mobile app technology and design concepts. This is an introductory course and assumes no prior programming experience. Students learn how to design build and optimize cross-platform mobile app using HTML5 standards. Students will also learn how to convert HTML5 apps into native apps for various mobile platforms. Students use CSS3 JavaScript and several JavaScript frameworks and techniques such as jQuery jQuery Mobile and AJAX. In addition students will use Web services such as Google Maps and Web Application Programming Interfaces (Web APIs) to integrate content into their apps.

Prerequisites:

STATS-240 or STATS-250 or Instructor Permission

Credits:

3.00

Description:

Provides an understanding of the business potential of big data; how to build and maintain data warehouses and how to analyze and use this data as a source for business intelligence and competitive advantage. Students study data mining concepts and the use of analytics tools and methods for producing business knowledge. Topics include: extraction transformation and loading; decision support systems; analytics text web and data mining models as well as data presentation/visualization including dashboards and scorecards. Students build a data warehouse and practice the extraction and filtering process used to produce high quality data warehouses. Students will use tools such as MS Excel Tableau SQL and SAP Business Warehouse.

Prerequisites:

STATS-240 or STATS-250

Credits:

3.00

Description:

Introduces a detailed overview of statistical learning for data mining inference and prediction in order to tackle modern-day data analysis problems. This course is appropriate for students who wish to learn and apply statistical learning tools to analyze data and gain valuable hands-on experience with R. Statistical learning refers to a vast set of tools for modeling and understanding complex datasets. Exciting topics include: Regression Logistic Regression Linear Discriminant Analysis Cross-Validation Bootstrap Linear/Non-Linear Model Selection and Regularization Support Vector Methodology and Unsupervised Learning via Principal Components Analysis and Clustering Methods. Students learn how to implement each of the statistical learning methods using the popular statistical software package R via hands-on lab sessions.

Credits:

3.00

Description:

Students will analyze and evaluate privacy risks facing individual and organizational data and then design and evaluate solutions to protect the data. The course starts by introducing students to basic data privacy principles and the deteriorating state of privacy with frequent data breaches and identity theft explosion. The course then explores the disruption to privacy caused by emerging technologies like mobile cloud big data and social media and the consequences. Different privacy solutions including privacy enhancing technologies like Tors Onions and encryption will be introduced. Various US Data privacy laws like HIPAA are explored and then compared to the European general data privacy regulation (GDPR) regime. The course ends by introducing different data privacy best practices and the "Privacy by Design" paradigm.

Credits:

3

Description:

Equips students with the principles, methodology and skills required to define, develop and deploy a fully functional dynamic web application. Students learn to customize the content, appearance, and delivery of their website using industry-standard web development tools. Class discussion will focus on web development issues for organizations as well as the role played by development tools such as HTML5, CSS3, and PHP scripting. Each class will include hands-on lab work. A term project is used to wrap the course content together.

Prerequisites:

ISOM-210(formerly ISOM-310)

Credits:

3.00

Description:

Covers the concepts techniques and tools used in the analysis and design of business information systems. Topics include: the system development cycle modeling prototyping and project management. Additionally the course focuses upon using Object Oriented analysis and design techniques including the UML. Emphasizes the analysis of business operations as well as the interaction between information systems professionals and end-users. A term project applying these concepts and techniques is required.

Credits:

3.00

Description:

Develops problem solving and basic programming skills through a variety of business application assignments. Introduces fundamental control and data structures using the Python programming language. Students learn about the concepts of modern business programming principles. The course builds skills in the areas of programming logic data structures control structures and system development. Testing and debugging techniques and the writing of well-structured code are emphasized.

Prerequisites:

SBS-101 and ISOM-201 and at least 54 credits

Credits:

3.00

Description:

Introduces concepts and tools for managing operations in service/ manufacturing organizations where inputs such as raw material labor or other resources into finished services and/or goods. Strategic and tactical issues of operations management (OM) including: operations strategy product and process design capacity planning quality management inventory management queueing theory and work force management are addressed. Quantitative models analytical tools and case studies are used to analyze operational problems that business managers face in both local and global settings.

Prerequisites:

SBS-101, ISOM-201, at least a 3.3 GPA, and at least 54 credits

Credits:

3.00

Description:

Introduces concepts and tools for managing operations in service/ manufacturing organizations where inputs such as raw material labor or other resources into finished services and/or goods. Strategic and tactical issues of operations management (OM) including: operations strategy product and process design capacity planning quality management inventory management queueing theory and work force management are addressed. Quantitative models analytical tools and case studies are used to analyze operational problems that business managers face in both local and global settings.

Prerequisites:

ISOM-210

Credits:

3.00

Description:

Provides an understanding of the role of information and databases in information systems and their role as an organizational resource. Students learn to design databases using normalization and entity-relationship diagrams develop data models and to build applications with database management systems such as MS Access and SQL. Techniques are examined and applied to realistic business problems through hands-on exercises and projects.

Prerequisites:

ISOM-130, ISOM-230, and STATS-240 or STATS-250 or Instructor Permission

Credits:

3.00

Description:

When companies make decisions they do so with the future in mind and essentially are predicting that their decisions will achieve desired results. Predictive analytics allow people to ask and answer questions that can predict demand and/or outcomes and obtain results that lead to reasoned action. This course develops students' capability in applying the core concepts and techniques of predictive analytics for opportunity identification and risk assessment within the context of organizational decision-making. Students will use data-driven approaches to develop predictive analytical models. Students will create and use data models and techniques apply trendlines to fit models to data perform what-if analysis construct data tables evaluate scenarios apply forecasting techniques simulation and risk analysis. Students will learn to use various presentation and visualization tools to communicate results. Topics include: predictive analytics life cycle opportunity/issue identification data preparation modeling analysis forecasting simulation risk assessment and operationalization of predictive analytics.

Prerequisites:

ISOM-210 and at least 54 credits

Credits:

3.00

Description:

Introduces Cybersecurity fundamental principles from a risk management approach both at the national and global levels. Common types of computer attacks and counter-attacks are addressed. Security technologies such as biometrics firewalls intrusion detection systems and cryptography systems will be analyzed and several hands-one lab exercises on the same are used to connect theory to practice and provide experiential learning. Best practices for Risk analysis and business continuity planning and common frameworks like the CIA triangle and the defence in depth solution are applied to different scenarios.

Credits:

3.00

Description:

This course gives a comprehensive introduction to project management. Projects provide businesses a time-delimited tool for improving expanding and innovating - the primary means for converting strategy into action. Project management success differentiates top performing firms. The course will focus on discussion and analysis of business situations that convey core project management skills. In particular this course focuses on the challenge of managing projects in today's complex high-pressure work environments. This course can be credited toward PMI Project Management Professional (PMP)(R)certification. PMP(R) and (PMBOK(R)Guide) are registered marks of the Project Management Institute Inc.

Prerequisites:

ISOM-313, ISOM-314, and ISOM-423 and at least 84 credits

Credits:

3.00

Description:

Explores the issues and approaches in managing the information systems function in organizations and how the IS function integrates/supports/enables various types of organizational capabilities. It takes a management perspective in exploring the acquisition development and implementation of plans and policies to achieve efficient and effective information systems. The course addresses issues relating to defining the high level IS infrastructure and the systems that support the operational administrative and strategic needs of the organization. The remainder of the course is focused on developing an intellectual framework that will allow leaders of organizations to critically assess existing IS infrastructures and emerging technologies as well as how these enabling technologies might affect organizational strategy. The ideas developed and cultivated in this course are intended to provide an enduring perspective that can help leaders make sense of an increasingly globalized and technology intensive business environment.

Prerequisites:

ISOM-210 and at least 54 credits

Credits:

3.00

Description:

Provides a conceptual as well as a mechanical understanding of enterprise integration and enterprise software business process reengineering and strategies for maximizing benefits from enterprise systems. Students lean to examine complex issues in organizational changes including implementation challenge; risks costs and benefits; learning and knowledge management. Hands-on lab projects on the ERP System (provided by SAP) are utilized to reinforce understanding of important enterprise systems and business process concepts. This course is part of the SAP Student Recognition Certificate Program.

Prerequisites:

ISOM-210 or ISOM-201 and Instructor Permission

Credits:

1.00- 3.00

Description:

Independent study allows students to expand their classroom experience by completing research in an area of interest not already covered by Suffolk courses. The student designs a unique project and finds a full-time faculty member with expertise in that topic who agrees to sponsor it and provide feedback as the proposal is refined. A well designed and executed research project broadens and/or deepens learning in a major or minor area of study and may also enhance a student's marketability to potential future employers. Students cannot register for an Independent Study until a full proposal is approved by the faculty sponsor department chair and academic dean. Many Independent study proposals require revisions before approval is granted; even with revisions independent study approval is NOT guaranteed. Students are strongly encouraged to submit a proposal in enough time to register for a different course if the proposal is not accepted. For complete instructions see the SBS Independent/Directed Study Agreement and Proposal form available online.

Prerequisites:

ISOM-210, 1 required ISOM major course, at least a 3.0 GPA, and Instructor Permission

Credits:

0.00- 3.00

Description:

An internship may be used to satisfy the IS major practical experience requirement of a minimum of 150 hours of information systems/information technology experience. Most internships will exceed 150 hours and may be paid or unpaid. Prior approval of your position by the IS Practical Experience Coordinator is required. This is accomplished by completing the IS Practicum Approval Form with an internship description. The internship description includes the job description the number of hours of work the number of credits grading criteria and any other requirements. Students should enroll in ISOM 520 prior to starting their internship. This is a graded course and cannot be used as a major elective. Students may decide to register for this free elective course as pass fail (see http://www.suffolk.edu/business/departments/11704. php). Prerequisites: Practical Experience Coordinator's Approval Required and Junior Standing minimum ISOM GPA of 3.0 and minimum overall GPA of 2.5.

Prerequisites:

ISOM-210, 1 required ISOM major course, at least 54 credits, and Instructor Permission

Credits:

0.00

Description:

All Information Systems majors are required to complete 150 hours of information systems/information technology experience. The 150 hours of work experience may be obtained in one or more positions as an intern part- or full-time employee or volunteer. Prior approval of your position by the IS Practical Experience Coordinator is required. This is accomplished by completing the IS Practicum Approval Form. Students should enroll in ISOM 560 no earlier than the semester when they expect to complete the 150 hours. Student should log their work tasks and accomplishments. Prerequisites: Practical Experience Coordinator's Approval Required

Prerequisites:

Take STATS-240 or STATS-250; SBS Honors or 3.3 GPA required.

Credits:

1.00

Description:

Do you ever wonder if a player is really "red hot"? Why don't those sports ranking polls ever agree? How can I pick a better fantasy football team? This challenge course covers the mathematical and statistical concepts and techniques used to assess performance data to provide support for decision making. Topics include mathematical statistical data analysis and modeling.