Learning Outcomes: 1. Apply fundamental skills in structuring business information.
Audience: Undergraduate

2. Organize, search and analyze business data.
Audience: Undergraduate

3. Apply advanced skills in data summarization and analysis.
Audience: Undergraduate

4. Visualize and present information.
Audience: Undergraduate

5. Apply introductory programming skills.
Audience: Undergraduate

6. Explain recent developments in ‘data informed’ business problem solving.
Audience: Undergraduate

Learning Outcomes: 1. Demonstrate a basic ability to self-assess and reflect on their personality, identities, strengths, values, and personal ethics.
Audience: Undergraduate

2. Explain the importance of continuous self-assessment as a method for improving approaches to work and interactions with others.
Audience: Undergraduate

3. Demonstrate the foundational understanding of leadership development as related to business now and in the future.
Audience: Undergraduate

4. Demonstrate the foundational understanding of career development as related to business.
Audience: Undergraduate

5. Explain the importance of leading ethically, inclusively, and authentically in individual and group settings.
Audience: Undergraduate

Learning Outcomes: 1. Critically reflect on improving study skills, career planning, and ethical perspectives to promote success in business.
Audience: Undergraduate

2. Demonstrate professional communications skills in written and spoken formats in individual and group settings.
Audience: Undergraduate

3. Identify business sectors, industries, and WSB majors that align with your desired future goals.
Audience: Undergraduate

Learning Outcomes: 1. Explain important statistics commonly used in business
Audience: Undergraduate

2. Apply basic probability concepts and models to uncertainty in business contexts
Audience: Undergraduate

3. Use data to estimate unknown business variables, test hypotheses, and learn with simple regression
Audience: Undergraduate

4. Use Excel to analyze data, create visualizations, and answer business questions
Audience: Undergraduate

5. Create a basic business proposal using statistics and data analytics
Audience: Undergraduate

Learning Outcomes: 1. Analyze and communicate with data in business environments
Audience: Undergraduate

2. Implement and interpret output from common statistical methodologies
Audience: Undergraduate

3. Integrate data from multiple data sources
Audience: Undergraduate

4. Create compelling data visualizations
Audience: Undergraduate

5. Solve and interpret basic optimization and simulation models
Audience: Undergraduate

6. Integrate models of uncertainty into business decision-making
Audience: Undergraduate

Learning Outcomes: 1. Demonstrate the ability to understand and gain empathy into human (customer/consumer/user) experience of product, services and systems through systematic inquiries.
Audience: Undergraduate

2. Exhibit the ability to be creative, collaborative, and divergent thinkers who can generate and visually communicate multiple ideas.
Audience: Undergraduate

3. Demonstrate ability to iterate proposed solutions toward innovation.
Audience: Undergraduate

4. Explore questions around cultural and intellectual exchange between business and design such as “What can business learn from design, and vice versa?”, or “How might the incorporation of design thinking help human-centered business to grow and flourish?
Audience: Undergraduate

5. Illuminate and expand on existing touch points between design thinking and “business thinking”, including, but not limited to, marketing/new product design and development, entrepreneurship and venture creation, operations and new process design
Audience: Undergraduate

Learning Outcomes: 1. Describe how the interplay between firms, governments, and market systems shape economic, societal and environmental outcomes, as well as tradeoffs within.
Audience: Undergraduate

2. Explain varieties of capitalism across nations and compare with United States capitalism over time.
Audience: Undergraduate

3. Analyze the role of business, governments, and civil society in managing free markets to balance the wellbeing of firms with the wellbeing of society and the natural environment.
Audience: Undergraduate

Learning Outcomes: 1. Recognize the legal implications of transactions and events that impact the business environment.
Audience: Undergraduate

2. Describe the American legal system and how the American court system works.
Audience: Undergraduate

3. Apply the technical legal rules covered in the course regarding negligence, intellectual property, contract law, agency law, and employment law.
Audience: Undergraduate

4. Analyze and assess risks and opportunities inherent in business transactions and in everyday life.
Audience: Undergraduate

Learning Outcomes: 1. Develop “statistical literacy,” meaning that you can interpret statistics frequently used in current events, industry reports, and so on
Audience: Undergraduate

2. Distinguish between descriptive and inferential statistics, and apply skills such as data summarization, hypothesis testing, and regression analysis, using Excel
Audience: Undergraduate

3. Apply the core concepts of probability to decision-making under uncertainty, including an introduction to simulation
Audience: Undergraduate

4. Synthesize your knowledge with quantitative business cases
Audience: Undergraduate

5. Create effective communication of data analyses in written, visual, and/or oral formats
Audience: Undergraduate

Learning Outcomes: 1. Explain the principles of different regression techniques for different forms of data
Audience: Undergraduate

2. Discriminate between types of explanatory variables and ways to incorporate these variables in a statistical model
Audience: Undergraduate

3. Implement and interpret output from the most common statistical methodologies
Audience: Undergraduate

4. Recognize the structure of relational databases
Audience: Undergraduate

5. Construct basic optimization and simulation models
Audience: Undergraduate

6. Understand the collection, management, reasoning, and communication with data in corporate and other environments
Audience: Undergraduate

Learning Outcomes: 1. Build and share dashboards using data visualization tools.
Audience: Undergraduate

2. Utilize SQL to query cloud databases.
Audience: Undergraduate

3. Build and deploy a predictive model in the cloud.
Audience: Undergraduate

4. Analyze big data in a cloud environment.
Audience: Undergraduate

Learning Outcomes: 1. Demonstrate the application of fundamental business terminology and concepts in the disciplines of economics, business law, finance, and accounting to effectively communicate with professionals across a given business organization.
Audience: Undergraduate

2. Explain the various reasoning businesses use to make decisions and the impact those decisions have on the firm and society as a whole and examine assumptions and predispositions regarding the role of business in society.
Audience: Undergraduate

3. Apply the common business processes and tools to develop professional, academic, and personal futures.
Audience: Undergraduate

Learning Outcomes: 1. Summarize the business environment including core functions, participants, and the external forces that influence business activities such as globalization, diversity, business ethics, and corporate social responsibility
Audience: Undergraduate

2. Evaluate different types of business formations and create a business plan
Audience: Undergraduate

3. Describe and apply the role of marketing in business
Audience: Undergraduate

4. Analyze business strategies and management decisions in order to evaluate their impact on business performance
Audience: Undergraduate

Learning Outcomes: 1. Apply discrete and continuous probability distributions to data encountered in business applications and evaluate the quality of parameter estimates.
Audience: Undergraduate

2. Explain the basics of modeling using both regression and time series approaches.
Audience: Undergraduate

3. Recognize the assumptions and limitations of the methods utilized in this course to aid businesses in making decisions under uncertainty.
Audience: Undergraduate

4. Interpret statistical information for someone with a non/less technical background.
Audience: Undergraduate

5. Analyze business data and construct models using appropriate statistical software.
Audience: Undergraduate

6. Communicate and present the results of an analysis.
Audience: Undergraduate

Learning Outcomes: 1. Effectively implement a systematic approach to analyzing case problems.
Audience: Undergraduate

2. Identify types of case problems and match them with frameworks and analytical tools designed to inform those problems and structure recommended solutions.
Audience: Undergraduate

3. Apply relevant analytical tools to identify missing information needed to understand problems.
Audience: Undergraduate

4. Formulate probing questions to bring out critical facts and assumptions about problem contexts.
Audience: Undergraduate

5. Integrate available facts and data into the relevant analytical tools and frameworks to diagnose problems holistically.
Audience: Undergraduate

6. Concisely communicate the analysis of case problems along with actionable recommendations to address the situations.
Audience: Undergraduate

Learning Outcomes: 1. Make effective business decisions that integrate data driven analysis and input from diverse team members from cross functional disciplines.
Audience: Undergraduate

2. Develop and apply inclusive leadership skills in a team setting to facilitate a sense of belonging among team members.
Audience: Undergraduate

3. Perform an external analysis of a firm’s environment (industry, rivals, & broader environment), and an internal analysis of a firm’s resources (capabilities, tangible, and intangible assets, etc.) to identify productive courses of action.
Audience: Undergraduate

4. Perform an analysis of alternative strategic positions in an industry, distinguishing between cost and differentiation strategies, with implications for building competitive advantage.
Audience: Undergraduate

5. Effectively communicate solutions to complex problems through application of the frameworks, methods and tools, learned in WSB core classes.
Audience: Undergraduate

Learning Outcomes: 1. Build machine learning models for the purpose of prediction in business contexts, choosing from a variety of statistical learning techniques.
Audience: Undergraduate

2. Adjust models and tune parameters of machine learning models in business settings, taking into account the fundamental bias-variance tradeoff.
Audience: Undergraduate

3. Evaluate prediction accuracy of models for regression and classification problems for business applications.
Audience: Undergraduate

4. Communicate the advantages and disadvantages of different prediction approaches, and justify and explain their choices in the context of business problems.
Audience: Undergraduate

5. Interpret the prediction outcomes of a given machine learning model in a business context, and communicate the results to non-expert audiences.
Audience: Undergraduate

6. Review advanced literature on machine learning, and further develop machine learning skills in self-study.
Audience: Undergraduate

Learning Outcomes: 1. Read, clean, create new datasets
Audience: Graduate

2. Describe the resulting data by computing and interpreting summary statistics, and via visualization tools
Audience: Graduate

3. Describe uncertainty and risk using the language of probability theory
Audience: Graduate

4. Perform hypothesis tests common in business applications
Audience: Graduate

5. Perform Monte Carlo simulations
Audience: Graduate

6. Estimate cross-sectional and time-series models for forecasting purposes and select among competing models via cross-validation
Audience: Graduate

7. Apply machine learning methods in the context of simple examples
Audience: Graduate

Learning Outcomes: 1. Derive measures related to the characteristics of probability functions such as moments, mean, variance, median,mode, and percentiles.
Audience: Graduate

2. Carry out an ordinary least-squares (OLS) regression and interpret the parameter estimates as well as predictions.
Audience: Graduate

3. Prepare, compile, and run basic computer programs in a variety of programming languages including C/C++,Java, Python, R, Stata, and VBA.
Audience: Graduate

4. Implement algorithms for business analytics problems in a structured fashion in Python and R.
Audience: Graduate

5. Pick suitable numerical functions from available packages, including quadrature (numerical integration), numerical solution of non-linear equations, optimization, and random-number generation, and use the functionality for business analyses.
Audience: Graduate

6. Run a Monte Carlo simulation using R and Python.
Audience: Graduate

Learning Outcomes: 1. Develop a deeper understanding of ethical dilemmas at work and your personal ethical beliefs.
Audience: Graduate

2. Use a systematic ethics decision-making framework to arrive at moral conclusions.
Audience: Graduate

3. Explain why firms should be ethical.
Audience: Graduate

4. Describe best practices for enhancing an organization’s ethical performance.
Audience: Graduate

5. Benchmark and assess your organization’s ethical performance.
Audience: Graduate

Learning Outcomes: 1. Explain the roles management, shareholders and directors have in impacting the Corporate Governance environment.
Audience: Graduate

2. Articulate the responsibilities of a Board of Directors and its Committees as well as what makes for effective members.
Audience: Graduate

3. Evaluate the root cause of Corporate Governance breakdowns and apply this learning to new situations and environments.
Audience: Graduate

4. Compare public vs. private company Corporate Governance environments including the major influences on international Corporate Governance environments.
Audience: Graduate

5. Reflect on the current and future practices in Corporate Governance including those that address global environmental and social issues.
Audience: Graduate

Learning Outcomes: 1. Identify the information contained in data sets and make simple data corrections/transformations to prepare data for visualization tools
Audience: Graduate

2. Explore data sets visually to recognize patterns and relationships that provide insights and recommendations into business problems
Audience: Graduate

3. Identify design principles and best practices that make good visualizations effective and recognize challenges present in making data understandable across a range of audiences
Audience: Graduate

4. Construct effective static and interactive data visualizations for business decision-making and critique/improve existing ones
Audience: Graduate

5. Efficiently and persuasively communicate data insights through presentations, dashboards, and reports
Audience: Graduate

6. Explain history and current landscape of data visualization tools and their relative strengths and weaknesses
Audience: Graduate

7. Proficiently use Tableau visualization software
Audience: Graduate

8. Improve data presentation and visualizations in Excel and PowerPoint
Audience: Graduate

9. Make basic visualizations in R
Audience: Graduate

Learning Outcomes: 1. Analyze a specific real-world organizational challenge and apply strategic thinking and decision-making skills to develop an effective and integrative business solution to that challenge.
Audience: Graduate

2. Apply creative and critical thinking to address ambiguity and uncertainty in the project.
Audience: Graduate

3. Communicate effectively with the client.
Audience: Graduate

4. Lead and function effectively in teams.
Audience: Graduate

5. Reflect on personal and professional development as business leaders for a global business environment.
Audience: Graduate

Learning Outcomes: 1. Identify appropriate modeling, analysis, and optimization methodologies when faced with a concrete problem.
Audience: Graduate

2. Leverage spreadsheet software (e.g., Excel) and programming languages (e.g., Python) for modeling and optimization.
Audience: Graduate

3. Classify optimization problems (e.g., linear, integer, nonlinear) and explain the challenges of solving different problem types.
Audience: Graduate

4. Recognize classic optimization problems (e.g., knapsack, network, traveling salesman) and recall how to formulate them.
Audience: Graduate

5. Discuss fundamental optimization theoretical concepts and their practical implications.
Audience: Graduate

Learning Outcomes: 1. Identify statements and analytical reports that use correlational evidence to infer causal relationships.
Audience: Graduate

2. Articulate why correlation may not equate to causation due to factors such as selection bias.
Audience: Graduate

3. Explain the value of randomization for establishing causal effects through experiments.
Audience: Graduate

4. Demonstrate skills to develop a randomized experiment and analyze the results.
Audience: Graduate

5. Identify potential “natural experiments” that allow for causal analysis from existing data in business environments and be able to implement analytic approaches to analyzing data in these situations.
Audience: Graduate

Learning Outcomes: 1. Communicate the fundamentals and common use cases for robotic process automation (RPA).
Audience: Graduate

2. Automate various business processes using RPA tools and best practices.
Audience: Graduate

3. Complete an RPA project that involves identifying automation opportunities and implementing solutions.
Audience: Graduate

Learning Outcomes: 1. Construct advanced SQL statements – including joins, common table expressions, window functions, etc.
Audience: Graduate

2. Build cloud data warehouses in various software vendor platforms.
Audience: Graduate

3. Automate steps in a data pipeline within a cloud environment.
Audience: Graduate

4. Present self-designed technical solutions to address business problems.
Audience: Graduate

Learning Outcomes: 1. Extract data from both structured or unstructured databases
Audience: Graduate

2. Transform and combine data with other relevant information and load into targeted systems such as data warehouses, data marts or analytical applications
Audience: Graduate

3. Use Python to execute simple web scraping, mine data from social networks, and use the libraries for advanced data analysis beyond simple descriptive analytics
Audience: Graduate

4. Implement Online Analytical Processing (OLAP) and create multi-dimension data cubes
Audience: Graduate

5. Execute social mining techniques and create valuable information from text-mining for decision-making
Audience: Graduate

6. Discuss current landscape in data warehousing, big data, and other emerging topics
Audience: Graduate

Learning Outcomes: 1. Justify the use of probability for coherent uncertainty quantification
Audience: Graduate

2. Explain how Bayesian updating occurs in conjugate models and hierarchical models
Audience: Graduate

3. Compare and contrast the conceptual and practical benefits and challenges of different posterior approximation strategies like MCMC and variational inference
Audience: Graduate

4. Implement posterior approximation algorithms in modern statistical and probabilistic programming languages such as R or Stan
Audience: Graduate

5. Specify, fit, criticize, and revise Bayesian models in practice
Audience: Graduate

Learning Outcomes: 1. Apply the SCQ framework to a business problem in a case format
Audience: Graduate

2. Develop a presentation using the answer-first principle and storyboarding techniques
Audience: Graduate

3. Prepare and deliver a persuasive presentation
Audience: Graduate

4. Reflect on your own progress and learning during the module and practice sharing constructive feedback
Audience: Graduate

Learning Outcomes: 1. Explain, at a foundational level, the cloud landscape for business analytics – i.e. vendors, services, and costs.
Audience: Graduate

2. Demonstrate how to load and connect to data in the cloud for a variety of analytics.
Audience: Graduate

3. Setup and manage a cloud data warehouse.
Audience: Graduate

4. Build and deploy machine learning models in the cloud.
Audience: Graduate

5. Develop and test a data pipeline in the cloud.
Audience: Graduate

6. Analyze big data in a cloud environment.
Audience: Graduate

Learning Outcomes: 1. Identify and explain the business challenge being examined.
Audience: Graduate

2. Successfully engage in a team and “client” project.
Audience: Graduate

3. Apply analytic methods to discover insights to answer the business challenge.
Audience: Graduate

4. Create recommendations based on the analysis findings.
Audience: Graduate

5. Successfully present those findings and recommendations to an audience of business professionals.
Audience: Graduate

Learning Outcomes: 1. Articulate the business challenge being examined and establish a work plan for addressing that business challenge.
Audience: Graduate

2. Conduct, compile and synthesize research relevant to the business challenge.
Audience: Graduate

3. Work with a team to complete tasks and reflect on your own progress and learning as part of that team.
Audience: Graduate

Learning Outcomes: 1. Determine recommendations for action that will positively impact the sponsor organization.
Audience: Graduate

2. Practice sharing and receiving feedback to improve outcomes.
Audience: Graduate

3. Present findings and recommendations to the sponsor organization.
Audience: Graduate

4. Work with a team to complete tasks and reflect on your own progress and learning as part of that team.
Audience: Graduate

Learning Outcomes: 1. Explore how organizations utilize analytics and artificial intelligence through interactions with industry professionals
Audience: Graduate

2. Articulate insights from interactions with industry professionals
Audience: Graduate

3. Articulate business recommendations based on in-class activities and analysis
Audience: Graduate

4. Demonstrate professional written and verbal communication skills
Audience: Graduate

5. Articulate current career aspirations
Audience: Graduate

Learning Outcomes: 1. Calculate descriptive statistics and generate basic visualizations using Python.
Audience: Graduate

2. Explain and apply principles and rules of probability.
Audience: Graduate

3. Utilize inferential statistics and communicate the uncertainty in statistical estimates.
Audience: Graduate

4. Perform regression analysis, and distinguish between correlation and causation.
Audience: Graduate

Learning Outcomes: 1. Construct a variety of SQL statements.
Audience: Graduate

2. Query and prepare data in response to business questions.
Audience: Graduate

3. Design a database to meet a business need and technical requirements.
Audience: Graduate

Learning Outcomes: 1. Create compelling data visualizations and dashboards.
Audience: Graduate

2. Explain the cloud landscape for business analytics - i.e. vendors, services, and costs.
Audience: Graduate

3. Setup and manage a cloud data warehouse.
Audience: Graduate

4. Build and deploy a machine learning model in the cloud.
Audience: Graduate

5. Analyze big data in a cloud environment.
Audience: Graduate

Learning Outcomes: 1. Develop and communicate analytics solutions to business challenges and opportunities.
Audience: Graduate

2. Build a predictive model using the Python programming language.
Audience: Graduate

3. Create an executive dashboard to support business decision making.
Audience: Graduate

4. Use SQL to query a database in response to business questions.
Audience: Graduate

Learning Outcomes: 1. Demonstrate the fundamentals of the Python programming language.
Audience: Graduate

2. Apply programming best practices; such as readability, simplicity, and reusability.
Audience: Graduate

3. Utilize Python for data manipulation and analysis
Audience: Graduate

Learning Outcomes: 1. Build (generalized) linear regression models for the purpose of prediction.
Audience: Graduate

2. Select predictive algorithms and model features, taking into account the fundamental bias-variance tradeoff.
Audience: Graduate

3. Evaluate prediction accuracy of models for regression and classification problems.
Audience: Graduate

4. Interpret the output of regression models, and communicate the results to non-expert audiences.
Audience: Graduate

Learning Outcomes: 1. Build machine learning models for the purpose of prediction, choosing from a variety of approaches.
Audience: Graduate

2. Tune models to optimize predictive performance.
Audience: Graduate

3. Evaluate and compare prediction accuracy of regression and classification models.
Audience: Graduate

4. Identify more advanced literature on machine learning and be able to develop skills in self-study.
Audience: Graduate

Learning Outcomes: 1. Retrieve, assemble, and clean text data for use in analytics applications.
Audience: Graduate

2. Use text representation approaches, including bag of words, for visualizing text data.
Audience: Graduate

3. Build classification and regression models using features created from text data.
Audience: Graduate

4. Explain and experiment with text generation using artificial intelligence (AI).
Audience: Graduate

Learning Outcomes: 1. Apply a Situation-Complication-Question (SCQ) framework to a business problem
Audience: Graduate

2. Use answer-first principles and storyboarding techniques
Audience: Graduate

3. Scope a project and understand customer needs
Audience: Graduate

4. Manage time and resources in a project
Audience: Graduate

5. Differentiate between traditional and agile project management approaches
Audience: Graduate

Learning Outcomes: 1. Recognize potential warning signs of predictive models failing or overpromising.
Audience: Graduate

2. Engage with ethical issues concerning analytics applications regarding autonomy, beneficience, and fairness.
Audience: Graduate

3. Audit algorithms/predictions for potential biases.
Audience: Graduate

4. Make strategic choices among analytics projects, including effective management of human resources.
Audience: Graduate

Learning Outcomes: 1. Develop and communicate an analytics solution to a business challenge or opportunity.
Audience: Graduate

2. Apply principles of project management to a business analytics project.
Audience: Graduate

3. Reflect on a team-based analytics project and identify lessons learned.
Audience: Graduate