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Graduate Business Analytics Certificate

Business Analytics

Today, more than ever before, businesses realize the importance of Business Analytics.

Ease of data capture has led to increased availability of data. Simultaneously, our ability to analyze data has been amplified by the growth in power of computers and the easy availability of powerful software for data analysis.

While some technology giants have led the way in this, most organizations are now aware of the possibilities and are looking to raise their game. Unlike in the past, every employee in a business must be able to approach data intelligently and to glean insights. There is just too much data floating around for it to be the exclusive domain of expert analysts and data scientists.

The Certificate in Business Analytics is designed for students who are seeking to learn data analytics concepts with an emphasis on how those tools can be applied to solve business problems.

Academic Approach to Analytics

Our hands-on courses empower students to:

  • Ask good questions to explore any dataset
  • Answer data questions using statistical and technical tools
  • Understand business processes and related data
  • Connect data analyses and the results back to business

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Offered primarily online, the Graduate Certificate in Business Analytics is a 12-credit program which can be completed in one calendar year. The program culminates with a semester-long capstone project for students to tackle real problems using everything they’ve learned throughout the program.

I. Required Courses (9 Credits)

Course Credits
BSAN 7001 Intro to Data Analytics and Business Intelligence 3
BSAN 7011 Exploratory Data Analysis and Data Visualization 3
BSAN 7031 Databases and SQL 3

II. Elective Course (3 credits)
Any BSAN course at the 7000-level or above (3 credits). 

Students completing the Certificate in Business Analytics who are subsequently admitted to the M.B.A. program are eligible to apply all certificate courses to fulfill M.B.A. degree elective requirements. Similarly, students who are subsequently admitted to the M.S. in Business Analytics program are eligible to apply all certificate courses to fulfill M.S. in Business Analytics degree requirements.

Because the certificate is not a degree, students who complete the certificate program do not participate in University Commencement Exercises.

The Business Analytics Certificate also offers customizable cohort options for individual organizations.

Course Information

  •   Course Delivery
    • All courses will be conducted online.
    • Weekly synchronous recitation sessions for every course where students can interact live with faculty members.
    • Recitation sessions are also recorded for the benefit of students who are unable to attend or would like to revisit the discussions.
    • Faculty members are available during office hours in addition to outside office hours for one-on-one video chats with students on Microsoft Teams to address any questions.
    • Students can also communicate with Instructors via email.
    • Small class sizes (< 30) enable faculty to provide individualized attention to students.
  •   Course Descriptions

    BSAN 7001 – Introduction to Data Analytics - Business Intelligence
    Business decision-making should, when possible, rely on data and the conclusions that can be drawn from that data. This course is an introduction to business data analytics; it covers descriptive statistics, data visualization, probability basics, and relationships between two or more variables. One focus is on learning and contrasting traditional statistical approaches (inference) and “big data” approaches. Much of the course will entail the use of Excel, as spreadsheet software is arguably the most commonly available and most frequently used tool for analyzing business data. We will equally be using R – a popular, open-source, statistical package.

    BSAN 7011 - Exploratory Data Analysis and Visualization
    In the initial stages of a data analysis project, analysts must often deal with large and unfamiliar data sets. By asking good questions and finding answers in the data, they arrive at useful insights – and this captures the core of exploratory data analysis (EDA). EDA often serves as a precursor to the process of building predictive models. Equally often, EDA yields significant insights that prove to be very useful in themselves. This course covers the art and science of EDA. Through numerous examples, the course will develop participants’ ability to formulate interesting and important questions. Answering these questions generally involves significant slicing, dicing, aggregating and reshaping of the data; this course will equip participants with the requisite skills. EDA relies heavily on data visualization and the course will equip participants with the skills to generate, and effectively present, evocative graphs that tell stories. The course will equip participants with a framework to enable them to ask the right questions and with the skills to explore and find answers.

    BSAN 7021 - Predictive Analytics
    In most business situations, being able to determine, with reasonable accuracy, the value of some unknown can be beneficial. For example, it would be useful for a company to know if a prospective customer would default on payments (classification), or to know the number of units of a product that it might be able to sell during the next quarter at a given store (regression). Quite often, even seemingly inaccurate estimates of such unknowns can lead to large monetary gains for a company if the new knowledge can lead to a discernable difference in performance. This is the domain of predictive modeling – using historical data to determine the value of an unknown. The course covers both classification and regression techniques. The course will equip participants with the ability to identify situations that could benefit from predictive models, to identify the data requirements and work with others to obtain the data, to manipulate the data into a form usable for predictive analysis, and to build, evaluate, present and deploy the models.

    BSAN 9000 - Capstone Project
    All students will complete a rigorous, semester-long Capstone project that will rely on and integrate the knowledge gained over the duration of the program. The Capstone experience will give students an immersive experience where they will take charge of a project and see it through from start to end. During the individual courses, students learn everything in a carefully scaffolded and structured environment. In contrast, in this course, students will face a more unstructured, realistic scenario, similar to those that data analysts encounter in their work. This unstructured environment will help students integrate their structured learnings and mature in the process. Their learning is also enhanced because of the sense of ownership that they feel in doing projects of their choice. Students will be expected to come up with their own projects (with some guidance) and then to execute the entire process. Project teams will identify a problem of significance and use the techniques learned in the MS in Business Analytics to suggest solutions. Students will go through several iterations of work within each phase, based on discussions with the instructors.

  •   Why Choose Our Program?
    • Business Analytics Certificate issued by Seton Hall University, which is ranked by U.S. News & World Report among the best colleges in the nation and recognized as one of the "Top Colleges in America" and one of the “Top Regional Colleges” by the Princeton Review, as well as a Forbes "Top College in America."
    • Certificate program is only 12-credits (4 Courses) and can be completed within one year
    • Receive personalized attention and dedicated faculty
    • Online with optional weekly synchronous session
    • Customizable cohort options for individual organizations
    • Apply certificate credits to M.S. in Business Analytics

Faculty Listing

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Viswa Viswanathan
Associate Professor of Computing and Decision Sciences
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Penina Orenstein
Associate Professor
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Rob Weitz
Rob Weitz
Associate Professor
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David Rosenthal
Associate Professor
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