Course Description: Focuses on teaching the fundamentals of R and SQL. Introduces the topic of numerical optimization, and review the concepts of linear algebra and calculus.

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Course Description: Introduction to the process of analyzing raw data to gain profitable business insight. Applications selected across organizational functions include prediction, process improvement, and general operational decision-making.

Course Description: Review the evolution of analytics in business, how to assemble and manage analytics teams, and the decision life-cycle. Emphasis on structuring communications to improve buy-in from peers and non-quantitatively-inclined colleagues.

Course Description: Examine leadership, communication, and project management within the business, legal and societal contexts in which analytics is applied. Emphasis on privacy, data security, responsibility, and ethics.

Course Description: Synthesize data-rich business challenges using analytic frameworks and techniques for modeling business problems. Emphasis on modeling uncertainty, optimizing multiple criteria, and building consensus.

Course Description: Introduction to the extraction, assembly, storage and organization of data in IT systems.

Course Description: Introduction to business applications involving standard, streaming, and network data. Emphasis on scalable technologies for processing and analyzing big data for diverse applications.

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Course Description: Learn computational reasoning about data representations by mapping conceptual data models to relational structures and analyzing database architectures and design trade-offs.

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Course Description: Introduction to various optimization methods in the domain of logistics and supply chains.

Course Description: Extract insights using visualization tools in R, Python, ManyEyes, HTML/CSS, etc. Standard (histograms, boxplots, and dashboards) and specialized (3D, animation, word clouds) formats are covered.

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Course Description: Introduction to statistical reasoning and inference extraction from large data-sets. Learn to obtain preliminary insights and form initial hypotheses through exploratory data analysis (EDA).

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Course Description: Continue exploring statistical reasoning using maximum likelihood estimation, Bayesian models, nonparametric models, Monte Carlo Markov Chain, time series, model specification, model selection, and dimension reduction.

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Course Description: Using spreadsheets and specialized modeling tools, explore structured problem solution through meta-heuristics, Monte Carlo simulation, and mathematical optimization.

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Course Description: Construct algorithms for learning from data and analyze the process for deriving business intelligence. Coverage of supervised and unsupervised learning, neural networks, etc.

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Course Description: Students explore contemporary and emerging domains for high-yield applications of analytics. Topics: social network analytics, search analytics, health care analytics, Internet of things, supply chain/operations analytics, and marketing analytics.

Course Description: Students form teams, scope their project in light of team capability and business opportunity, create a preliminary structure and solution approach for the core problem, and assess data quality and project risks.

Course Description: Building on problems chosen in BAX 461, teams refine the business opportunity and draw insights from exploratory data analysis.

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Course Description: Focus on completing project deliverables by polishing statistical and non-statistical quantitative analysis, generating insights for technical and business stakeholders, integrating proposed solutions into partner workflows and organizations, and disseminating the findings and outcomes through presentations and publications.

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Course Description: Project teams complete analysis, plan deployment and obtain client buy-in. Culminates in a project presentation, preferably including representatives from the client organization.

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Course Description: Students develop an understanding of how to position themselves as strategic partners in a company’s talent management efforts. Explore a range of topics related to people analytics, including hiring and selection, performance evaluation, training/development, promotion, compensation, social networks, diversity, and retention.

Course Description: Covers the foundations of cloud computing models including (Iaas) Infrastructure as a Service, (Paas) Platform as a Service, and Software as a Service (SaaS).

Course Description: Covers the four layers of Machine Learning in the cloud: AI services, ML services, ML Engines & Frameworks, and Infrastructure & Serverless Environments and how to implement solutions on all of the layers by using the best abstraction for the task at hand.