
Applied Business Analytics Certificate
Program Details
Degree
CertificateDepartment
School of Business Graduate ProgramAvailable
On campusSuccess in the workplace today comes from having better business intelligence that allows you to make smarter decisions in real time. This has long been the territory of specialized analysts, whose models and projections helped drive R&D, marketing, strategy, supply chains and so on. Today, the ubiquity of analytical tools and limitless amounts of data mean business analytics have gone from a specialized subset of skills to an expectation for aspiring leaders.
The Applied Business Analytics graduate certificate at Stevens is designed to equip students with the skills needed to mine, manage and make decisions using data. A blend of programming, analytics and visualization skills gives managers the tools and perspective needed to lead a team of digital natives in more quickly identifying opportunities and setting strategies.
Upon completing this certificate, students will be able to:
Navigate the challenges associated with scraping, cleaning and processing data.
Communicate findings and insights through visualizations that empower impactful storytelling.
Create innovative solutions using algorithmic tools and methods.
Understand technical recommendations and effectively turn them into strategy.
Through an understanding of programming and data cleansing, be able to think critically about data-supported conclusions and challenge findings from a practical perspective.
This certificate is best suited to professionals with business experience who are working in technical environments, as well as leaders who want to upskill themselves with an in-demand set of abilities that will help them create value throughout their careers.
MIS 630 Dealing with Data
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MIS 636 Data Warehousing and Business Intelligence
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MIS 637 Data Analytics and Machine Learning
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BIA 652 Multivariate Data Analysis
This course introduces basic methods underlying multivariate analysis through computer applications using R. Students will master multivariate analysis techniques, including principal components analysis, factor analysis, structural equation modeling, multidimensional scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant function analysis and logistic regression, as well as other methods used for dimension reduction, pattern recognition, classification and forecasting. Students will build expertise in applying these techniques to real data through class exercises and a project, and learn how to visualize data and present results.
FE 550 Data Visualization Applications
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BIA 654 Experimental Design
This course covers fundamental topics in experimentation, including hypothesis development, operational definitions, reliability and validity, measurement, and variables, as well as design methods, such as sampling, randomization and counterbalancing. The course also introduces the analysis associated with various experiments. At the end of the course, students present a project, which consists of designing an experiment, collecting data and trying to answer a research question.