Today more than ever, companies are using advanced technologies to manage their operations. This massive increase in data and information has created a high demand for skilled analytics professionals in all industries.

Employers are looking for individuals who can gather, analyze, interpret, share, and apply data in a meaningful way. These skillsets help businesses improve decision making, cut costs, and identify new business opportunities.

The minor in big data and business analytics complements all majors across the Sawyer Business School and College of Arts & Sciences. It includes three required courses, which combine data management with data mining techniques, predictive analytics, and visualization.

Big Data & Business Analytics Minor Requirements

Big Data and Business Analytics Minor, 3 courses, 9 credits

Students are required to take the following:

  • ISOM-130 Data Science and 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.

    Term:

    Offered Both Fall and Spring

  • ISOM-230 Big Data, Business Intelligence and 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.

    Term:

    Offered Both Fall and Spring

  • ISOM-330 Applied Predictive Analytics

    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.

    Term:

    Offered Both Fall and Spring

Big Data and Decision Analysis minor for CAS students, 5 courses, 16 credits

  • ENT-101 Business Foundations

    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.

    Term:

    Offered Both Fall and Spring

    Type:

    Arts Admin Minor Elective

  • ISOM-130 Data Science and 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.

    Term:

    Offered Both Fall and Spring

  • ISOM-230 Big Data, Business Intelligence and 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.

    Term:

    Offered Both Fall and Spring

  • ISOM-330 Applied Predictive Analytics

    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.

    Term:

    Offered Both Fall and Spring

 *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 contact Information Systems and Operations Management Department, ISOM@suffolk.edu or 617-573-8331.