Big Data & Business Analytics

Companies across all industries are using sophisticated technologies to collect and understand data to manage their operations. As a result, the demand for skilled analytics professionals is growing fast in all types of organizations—regardless of industry, size, or ownership structure. Be ready to capitalize on this opportunity by gaining specialized, practical knowledge and skills in Suffolk's big data and business analytics programs.

Your array of business analytics program options includes undergraduate majors and minors, a graduate certificate, and accelerated and dual graduate degrees.

The 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.

Big Data and Business Analytics (BDBA) Major

Learn more about this major

Required Courses (5 courses, 15 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, cockpits 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, MicroStrategy (Salesforce), 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.

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 Java programming language. Students learn about the concepts of object-oriented / event-driven programming principles. The course builds skills in the areas of programming logic, Class and Object concepts, and system development. Testing and debugging techniques and the writing of well-structured code are emphasized.

Prerequisites:

ISOM-210 (formerly ISOM-310); This course was formerly ISOM-423

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 Java programming language. Students learn about the concepts of object-oriented / event-driven programming principles. The course builds skills in the areas of programming logic, Class and Object concepts, and system development. Testing and debugging techniques and the writing of well-structured code are emphasized.

Prerequisites:

ISOM-210 (formerly ISOM-310); This course was formerly ISOM-423

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(formerly ISOM-310) and at least 54 credits

Credits:

3.00

Description:

Introduces the basics of information security & privacy including the legal and ethical issues. 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 lab exercises on the same are used to connect theory to practice. Best practices for planning and auditing security and privacy will also be covered.

Prerequisites:

ISOM-210(formerly ISOM-310) 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 (or MKT 318 or MKT 319)

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.

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

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, cockpits 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, MicroStrategy (Salesforce), 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)

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.

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, cockpits 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, MicroStrategy (Salesforce), SQL and SAP Business Warehouse.

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-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.

Big Data and Business Analytics Undergraduate 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:

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.

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, cockpits 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, MicroStrategy (Salesforce), 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:

3.00

Description:

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

Prerequisites:

ISOM-210 (formerly ISOM-310); This course was formerly ISOM-423

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(formerly ISOM-310) and at least 54 credits

Credits:

3.00

Description:

Introduces the basics of information security & privacy including the legal and ethical issues. 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 lab exercises on the same are used to connect theory to practice. Best practices for planning and auditing security and privacy will also be covered.

Prerequisites:

ISOM-210(formerly ISOM-310) 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 (or MKT 318 or MKT 319)

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.