Accounting and accountants are information providers. By combining data analytics with financial insights provided by accounting standards, accounting analytics is a powerful combination. Students will use foundational knowledge to apply to specific settings of financial accounting, managerial accounting, auditing, and tax questions. Why choose Accounting Analytics? Students will able to use a wide variety of data sources from a company’s own financial reporting system to economy-wide sources, apply analytics:
- Descriptive: What happened last period to company earnings?
- Diagnostic: Why did the reported results differ from expectations?
- Predictive: Which firms do we expect will go bankrupt?
- Prescriptive: What is the expected level of breakeven sales?
Bioinformatics students combine foundational knowledge of biological systems, computational skills, and critical thinking to draw conclusions from diverse data types, including biological, medical and geographic. Students will also enhance their verbal and written communication skills for conveying findings in technical and non-technical forms. Students will learn the basic principles of biology and will be prepared to work with diverse data sources and data types, including tackling the computational challenges of biological datasets.
This concentration combines foundational knowledge of biomedical systems, computational skills, and critical thinking to draw conclusions from diverse biomedical and healthcare data types.
A Data Science degree with a concentration in Business Data Analytics will help the student develop the ability to adapt analytics concepts to interpret and communicate findings and implications to senior decision-makers. This variety of data and methods will answer the following questions:
- What are the data telling us?
- What stories does the data reveal to us?
- All this data; how do we make sense of it?
- How can we put the data to work for us?
- How can we apply my knowledge outside the classroom?
Computational Analytics will emphasize on programming and analytical skills as well as computer science principles. Students will apply state-of-the-art technologies for data storage, manipulation, security and privacy, retrieval, mining, machine learning, AI, and cloud computing. There is emphasis on large, heterogeneous, streaming, and noisy data. Students will use foundational knowledge and apply critical thinking skills to problem identification and solving.
A student studying data science statistics provides statistically driven decision making with emphasis on mathematical theory that underlies the models and programming.
Geospatial Data Analytics prepares students to acquire, integrate, analyze and interpret complex location-based data and the unique analytical methods that they require. Geospatial competencies as defined in the US Department of Labor’s Geospatial Competency Model including:
- Geospatial foundations
- Geospatial data
- Data modeling
- Design aspects
- Cartography and visualization
- GIS&T and society
This concentration will teach students to integrate multidisciplinary knowledge in data analytics to analyze complex business problems and discover insights that can improve operations decisions in complex environments and systems. Students will build predictive models that support better informed decisions in designing, managing and fine-tuning operation procedures to maximize productivity and quality while minimizing time and cost such as allocation of resources, production, inventory, quality control, etc.
Social Data Analytics will provide students with skills for conveying findings to diverse audiences and decision makers like NGOs, government agencies and policy makers.
Supply Chain Analytics will Combine foundational knowledge of seamless supply chain, with computational skills and critical thinking to derive insights and conclusions. These data scientists will use techniques to derive insight-driven actions that improve supply chain efficiency and effectiveness.