Tailor Your MBA to Fit Your Goals
The rigorous Customized MBA curriculum at Yeshiva University’s Sy Syms School of Business is built on three core pillars to ensure that you can adapt to an ever-changing business landscape: Entrepreneurship, Core Business Skills and Navigating Relationships. From data to marketing to accounting, you’ll experience all aspects of business and have the freedom to dive deeper into your interests—all through the lens of an strategic mindset.
With a full, well-rounded skill set and business perspective, you’ll graduate with a dynamic vision to lead in any organization.
Build cross-functional mastery of business skills with nine core courses covering topics from corporate finance to marketing strategy, and managerial accounting. Then, customize your degree with elective courses on entrepreneurship, negotiations, and crisis management.
Customized On Campus MBA Classes
Core Courses
Course Description:
This course introduces students to the assumptions, principles, and practice of financial and managerial accounting. Students analyze income statements, balance sheets, and statements of cash flows with emphasis on equity valuation, risk analysis, cost management, and operational planning.
Rationale:
This course develops a rigorous foundation in analyzing financial information for managerial decision-making. By interpreting financial statements and cost structures, students learn to evaluate operational performance, assess financial risk, and support planning and control decisions using quantitative evidence.
Industry Example:
An MBA graduate leading a mid-sized community or nonprofit organization, with 400–500 member families, can use this course to make strategic decisions around revenue, staffing, and growth. The same frameworks apply to professional services firms where leaders manage stakeholders and performance without formal authority. .
Course Description:
This capstone course challenges student teams to design and implement real-world field experiments with external partner organizations. Students apply tools from strategy, behavioral economics, and data science to test interventions and produce research-quality analyses and practitioner-focused recommendations.
Rationale:
The course integrates analytical reasoning, statistical analysis, and strategic thinking through applied experimentation. Students learn to frame business problems as testable hypotheses, analyze data rigorously, and translate results into actionable organizational recommendations.
Industry Example:
A retail organization in the garment industry collaborates with a student team to test alternative pricing or promotion strategies across locations. Students design and conduct experiments whose data guide rollout decisions.
Course Description:
This course provides a conceptual and analytical foundation in corporate financial management. Topics include financial statement analysis, time value of money, valuation of bonds and stocks, capital budgeting, risk, and cost of capital, with extensive use of Excel-based quantitative methods.
Rationale:
The course strengthens quantitative financial decision-making skills by applying valuation models, risk analysis, and capital budgeting techniques to real investment decisions.
Industry Example:
A financial manager evaluates competing real estate investment projects by building capital budgeting models for each project, comparing net present values across projects, and analyzing sensitivity to changes in interest rates and cash flows in each of them.
Course Description:
This course examines leadership as both a historical human endeavor and a modern organizational capability. It integrates leadership theory, trust, emotional intelligence, and ethics to help students develop self-awareness, interpersonal skill, and principled decision-making.
Rationale:
The course applies research-based leadership frameworks and behavioral models to complex organizational challenges. Students learn to analyze interpersonal dynamics, ethical dilemmas, and leadership effectiveness using structured assessment and reflection.
Industry Example:
A department head uses data from employees’ real and simulated strategic decisions, emotional intelligence assessments and online engagement to diagnose behaviors and intentions that are undermine trust within the organization and design targeted development initiatives to enhance trust.
Course Description:
This course focuses on data-driven and quantitative approaches to marketing decision-making. Students apply statistical analysis to market research, segmentation, targeting, pricing, and promotion in order to design and evaluate effective marketing strategies.
Rationale:
The course emphasizes analytical evaluation of marketing decisions through customer data, statistical models, and performance metrics.
Industry Example:
A marketing team designs surveys to find explanations to decreased sales in certain locations. The data from both surveys and sales is then used to segment customers and design experiments that inform pricing strategies. Subsequently, the marketing team proposes indicators to assess campaign effectiveness.
Course Description:
This course equips students with foundational statistical and computational tools for data-driven decision-making. Topics include descriptive and inferential statistics, regression, probability, and introductory machine learning and generative AI applications for business leadership.
Rationale:
The course provides core analytical competencies needed to interpret data, manage uncertainty, and apply predictive techniques to organizational decisions.
Industry Example:
An operations leader of an e-commerce platform uses regression and forecasting models to anticipate demand based on different prices, political, and environmental conditions. The results from these models inform pricing, staffing, marketing budgets, and inventory levels.
Course Description:
This course introduces strategic decision-making with an emphasis on competitive advantage, game theory, and corporate strategy. Students analyze how firms create and sustain value using empirical, analytical, and global strategic frameworks.
Rationale:
The course develops structured analytical thinking for evaluating competitive environments, strategic trade-offs, and long-term value creation.
Industry Example:
A technology firm anticipates competitor responses using dynamic prisoners’ dilemma and winner-take-all models of competition before entering a new international market.
ACCOUNTING FOCUS TRACKS
Course Description:
This course examines the design, use, and control of accounting information systems (AIS) and their role in data-driven decision-making. Students study system architecture, internal controls, ERP systems, data modeling, and information security, with hands-on exposure to databases, SQL, and emerging technologies supporting accounting, auditing, and operational efficiency.
Rationale:
The course builds analytical understanding of how accounting data flows through organizational systems and how controls ensure data reliability and security.
Industry Example:
An internal audit team evaluates ERP controls and uses SQL queries to identify anomalies and propose processes to improve reporting accuracy.
Course Description:
This course focuses on transforming complex data into clear visual and analytical narratives for managerial decision-making. Using tools such as R, Tableau, and Python, students combine statistical reasoning, data visualization, and storytelling to communicate insights effectively to diverse business stakeholders.
Rationale:
The course strengthens advanced statistical reasoning and the ability to translate analytical results into actionable insights for decision-makers.
Industry Example:
A consulting team working for a credit card provider builds statistical models to summarize clients buying behavior encoded in large datasets. The models lead to patterns communicated through visual and written reports that help executives identify operational inefficiencies and minimize fraud.
Course Description:
This course explores emerging issues shaping the accounting profession, including blockchain, cryptocurrency, fintech applications, and advanced accounting research using the FASB Codification. Students gain hands-on experience with professional research methods and examine the legal, economic, and technological forces redefining accounting practice.
Rationale:
The course develops analytical adaptability by engaging students with emerging technologies and regulatory frameworks using structured research and evaluation.
Industry Example:
A consulting team from a big financial consulting firm assesses the accounting implications of and proposes methods to properly account for cryptocurrency transactions to ensure compliance and transparency.
Course Description:
This course examines the evolving role of corporate accountability in capital markets, with emphasis on intangible assets, non-GAAP metrics, and sustainability reporting. Students analyze emerging disclosure frameworks and apply statistical modeling to assess the relationship between financial, ethical, and strategic performance.
Rationale:
The course integrates quantitative analysis with ethical evaluation to assess organizational performance beyond traditional financial metrics.
Industry Example:
An investment official evaluates sustainability disclosures and uses non-GAAP metrics to communicate long-term firm risk to C-level executives at her firm.
BUSINESS ANALYTICS FOCUS TRACKS
Course Description:
This course develops students’ ability to translate complex datasets into compelling visual stories that support business decisions. Using R, Tableau, and Python, students apply best practices in visualization design, storytelling, and data communication across functional areas.
Rationale:
The course strengthens analytical communication by enabling students to present quantitative findings clearly and effectively.
Industry Example:
A healthcare organization collects patient flow data from various data sources, ensures anonymity of the records, and extract statistical regularities that are then used to inform the organization’s operational choices to improve operational efficiency indicators such as patient recovery and satisfaction, and employee turnover.
Course Description:
This course introduces statistical modeling and machine learning techniques used in data mining and knowledge discovery. Students learn to explore large datasets, build predictive models, and apply supervised and unsupervised learning methods to real-world business problems in marketing, finance, and operations.
Rationale:
The course develops predictive analytics skills through applied modeling and evaluation of large datasets.
Industry Example:
A retailer team predicting demand uses pre-trained transformers to anticipate each customer’s next purchase, within the set of repeat buyers.
Course Description:
Building on foundational analytics coursework, this course deepens students’ understanding of advanced machine learning methods, including forecasting, ensemble models, reinforcement learning, and deep learning. Emphasis is placed on evaluating model performance and applying predictive analytics to complex, industry-specific decisions.
Rationale:
The course advances analytical sophistication by focusing on model performance, scalability, and real-world application.
Industry Example:
A global maritime logistics firm applies advanced forecasting models to optimize supply chain planning for intercontinental shipping routes. These forecasts incorporate both temporal and spatial dimensions, prescribing precise staffing needs and efficient allocation of assets across global operations.
Course Description:
This course emphasizes the communication of data-driven insights through effective visualization and narrative techniques. Students use R, Tableau, and Python to design clear, persuasive data stories that support strategic and managerial decision-making.
Rationale:
The course builds the ability to translate analytical results into decision-ready insights for diverse stakeholders.
Industry Example:
The strategy team at a well-funded firm developing Large Language Models collects data from neural network weights to enhance model interpretability. The billions of parameters are visualized in two-dimensional planes, enabling clearer insights. Communicating these model weights to senior leadership helps them to identify the clients’ tasks and occupations where their models outperform competitors’ models.
Course Description:
This course provides an applied introduction to data mining and predictive analytics for managerial decision-making. Students learn to translate business problems into data science questions and apply regression, classification, and clustering techniques to extract actionable insights across business functions.
Rationale:
The course emphasizes analytical framing and disciplined application of predictive techniques.
Industry Example:
The economics team at a large retailer's online platform uses machine learning to forecast which customers are likely to stop using the platform. Identifying behaviors and characteristics enables the team to create a churn risk score for each customer. The operations and marketing teams use the score to improve customer retention.
FINANCE FOCUS TRACKS
Course Description:
This course examines advanced topics in corporate finance with a focus on capital structure, valuation, payout policy, and financing decisions. Students build and analyze financial models to evaluate investment opportunities, assess financial risk, and support strategic decisions related to mergers, acquisitions, and corporate growth.
Rationale:
The course deepens quantitative financial analysis and modeling skills for complex corporate decisions.
Industry Example:
A corporate development team at a private equity firm evaluates acquisition targets using valuation and scenario analysis.
Course Description:
This course introduces quantitative and computational methods essential for modern financial analysis. Students apply probability, statistics, optimization, machine learning, and AI-driven tools using Python to solve real-world problems in portfolio optimization, forecasting, risk modeling, and financial decision-making.
Rationale:
The course builds advanced computational fluency for solving complex financial problems.
Industry Example:
An asset manager applies optimization algorithms to high-frequency trading data to construct dynamically efficient portfolios. The portfolio weights are then tracked to inform the firm’s decisions on long-term investment strategies.
Course Description:
This course provides a rigorous introduction to investment analysis and portfolio management. Topics include asset pricing, risk and return, diversification, market efficiency, and modern portfolio theory, with hands-on applications using statistical and computational tools to construct and evaluate investment portfolios.
Rationale:
The course strengthens analytical understanding of risk-return tradeoffs and portfolio construction.
Industry Example:
A wealth advisor designs surveys and collects data on clients’ risk and value-investing preferences. The advisor then uses the data to construct diversified portfolios aligned with client objectives.
Course Description:
Building on foundational investment theory, this course explores advanced topics in fixed income, derivatives, and active portfolio management. Students apply valuation models, hedging strategies, and performance attribution techniques to design and evaluate sophisticated investment strategies.
Rationale:
The course advances analytical rigor in evaluating complex financial instruments and strategies.
Industry Example:
A hedge fund designs derivative-based hedging strategies to manage market risk and comply with the company’s risk management policies.
STRATEGY AND ENTREPRENEURSHIP FOCUS TRACKS
Course Description:
This course examines how organizations use experimentation and causal inference to drive strategic decision-making. Students learn to design, implement, and analyze experiments that uncover cause-and-effect relationships across business functions, with emphasis on hypothesis testing, randomization, ethical considerations, and scaling experimentation in real organizational settings.
Rationale:
The course develops disciplined analytical reasoning through experimentation and causal analysis.
Industry Example:
An online technology firm runs controlled experiments to test responses to the challenge that high usage does not translate into high sales. Responses include creating a tier service from free to premium and gift cards for referred customers. The results of the experiment guide resource allocation between the product mix.
Course Description:
This course introduces game theory as a framework for analyzing strategic interactions in competitive and cooperative environments. Students apply concepts such as Nash equilibrium, signaling, bargaining, and mechanism design to real-world problems in pricing, negotiation, market entry, auctions, and competitive strategy.
Rationale:
The course builds formal analytical reasoning by modeling strategic interactions.
Industry Example:
An airline models competitors’ mutual pricing and routing decisions. The results from the model identify the less contested routes that command the highest willingness to pay from buyers.
Course Description:
This course explores how machine learning and artificial intelligence shape strategic decision-making. Students gain hands-on experience with foundational algorithms and evaluate how data-driven models create competitive advantage, while addressing ethical, organizational, and governance challenges associated with AI adoption.
Rationale:
The course integrates analytical modeling with strategic evaluation of AI-driven decisions.
Industry Example:
A digital movie streaming platform uses large datasets of online consumer behavior to predict next term behavior and improve user engagement through recommendation algorithms.
Course Description:
This course focuses on the use of data and analytics to inform workforce and talent decisions. Students apply statistical modeling and experimentation to hiring, retention, performance, and job design, while addressing ethical considerations and bias in people-related data.
Rationale:
The course applies quantitative analysis to workforce strategy and talent management.
Industry Example:
A global human resource management firm collects data from hundreds of local businesses. It uses predictive analytics to identify drivers of employee turnover.
