Applied Business Analytics Certificate

Program Overview

Program Details

Degree

Certificate

School

School of Business

Department

School of Business Graduate Program

Available

On Campus & Online

Description

Success 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.

Learning Outcomes

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.

Target Audience

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.

Curriculum - Applied Business Analytics

MIS 630 Dealing with Data

This course deals with strategic uses of data, data structures, file organizations and hardware as determinants of planning for, and implementing, an enterprise-wide data management scheme. Major course topics include data as valuable enterprise resource, inherent characteristics of data, modeling the data requirements of an enterprise, data repositories and system development life cycles.

MIS 636 Data Warehousing and Business Intelligence

This course focuses on the design and management of data warehouse and business intelligence systems. The course is organized around the following general themes: knowledge discovery in databases, planning and business requirements, architecture, data design, implementation, business intelligence, deployment, maintenance and growth, and emerging issues. Practical examples and case studies are presented throughout.

MIS 637 Data Analytics and Machine Learning

This course will focus on data mining and knowledge discovery algorithms and their applications in solving business and operation problems. We concentrate on demonstrating how discovering the hidden knowledge in corporate databases helps managers make near-real time decisions. Methodological and practical aspects of knowledge discovery algorithms will be covered, including data preprocessing, k-nearest neighborhood algorithms, machine learning and decision trees, artificial neural networks, clustering, and algorithm evaluation techniques. Practical examples and case studies will be present throughout.

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

Effective visualization of complex data allows for useful insights, more effective communication and better decision-making. This course investigates methods for visualizing financial datasets from a variety of perspectives in order to best identify the right tool for a given task. Students will use a number of tools to refine their data and create visualizations, including R and associated visualization libraries, Ruby on Rails visualization tools, ManyEyes, HTML5 and CSS 3, D3.js and related javascript libraries, Google Chart Tools, Google Refine, and image-editing programs.

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.