This course introduces students to predictive analytics using techniques from machine learning and statistics. Techniques learned will include linear and logistic regression, and artificial neural networks. Students will learn to apply these tools to a range of common business problems including forecasting, classification and recommendation.
This course develops presentation and communication skills to explain business insights obtained from data. Students will become advanced users of spreadsheet based tools such as Excel and Tableau. This course covers a variety of visualization skills used to illustrate and explore different data types such as traditional tabular data, network data, text/unstructured data and geographic data.
This course provides students with a sound understanding and practice in developing and using databases for the purpose of capturing, organizing and analysing organizational data. Relational databases will be covered extensively. Topics will include understanding requirements, database modelling, normalization, design and structured query language (SQL). Principles and tools for NoSQL databases and MapReduce will also be introduced.
This course will introduce students to causal analysis for supporting business decisions. Students will learn to use retrospective data through regression analysis as well as to design experiments to investigate and support causal hypotheses. Advanced techniques such as difference-in-difference and instrumental variable approaches will be introduced.
This course will cover recent developments in artificial intelligence and big data and their application and implementation in core business processes. This course will examine how to implement advanced artificial intelligence and big data analytics projects within the firm. This will include building and housing an analytics team; identifying, proposing and evaluating analytics projects; and actual implementation of the project.
This course provides a foundation for thinking about the ethical and legal implication of leveraging data science in business decision making. Topics covered include ethical theories as well as legal principles and values concerning privacy, informed consent, data ownership, capture, aggregation, dissemination and protection.
The first part of this course will introduce students to optimization, constrained optimization and mathematical programming. The second part will introduce basic concepts of simulation including input analysis, flowcharting, model building, model validation, verification and output analysis.
This project course focuses on formulating and proposing a business analytics strategy to solve an unstructured business problem. Students focus on understanding the underlying managerial issues in the company or the organization they are working with, and prepare a proposal for the analytics project they are aiming to tackle.
This project course focuses on the implementation and delivery of a business analytics project. Students work on gathering the required data, structure their analytical solutions, design implementation steps and present their findings.
This course will provide students with the opportunity to apply data analytics tools, such as OLS, logistic and probit regressions, simulations, and optimization analysis, to accounting information in order to address a variety of problems. These problems span the various functional areas of accounting, including financial and managerial accounting, auditing and taxation. The types of problems that students may be exposed to include, but are not limited to, using data to detect earnings management in financial information, assess the financial performance, position and/or credit risk of an entity, estimate cost functions, identify cost overruns, perform budgeting simulations, optimize short run production, search for patterns, outliers and anomalies for the purpose of assessing audit risk and/or performing audit procedures, and to assist in tax planning and compliance.
This course intends to provide an overview of Business Analytics tools applied to finance.
We explore analytical tools such as time series analysis applied to financial markets. In particular, we broadly focus on financial decisions involving portfolio selection, risk measurement, risk management, hedging, and other financial modelling decisions involving equity, fixed income and derivative markets. Topics include CAPM and Fama-French 5-factor asset pricing models, portfolio theory, liquidity risks and measurement, options and futures, credit analytics, VaR (Value at Risk), and Monte Carlo Simulation. We focus on R programming to download and process financial and economic data from various sources, such as WRDS, Bloomberg and also publicly available sources such as Yahoo! Finance, Google Finance, FRED (Federal Reserve Bank’s Economic Data Library), and SEC.
This course will help prepare you for the role of HR data scientist or individual wanting to become more sophisticated in terms of their people analytics. You will learn which analyses are appropriate for various kinds of HR-related data-driven questions. The types of analyses you may be exposed to include, but are not limited to: a brief introduction to psychometric theory; how to build predictive models of employee turnover and job performance; statistical methods for validation of HR assessment measures; estimation of adverse impact; utility analysis; meta-analytic methods specific to HR; how to analyze and interpret employee engagement data; multi-level analysis of organizational data; methods to investigate pay equity; methods to effectively visualize/communicate complex organizational data to decision-makers.
The course deals with how marketers can extract useful information and intelligence from marketing data for designing marketing strategies. The emphasis is on advanced data analysis techniques relevant to marketing decisions. Students will learn to master strong analytic skills with the application to customer relationship management, brand marketing, customer segmentation, sales promotion, social media marketing and other marketing topics.
Students in this course will study the application of data driven analytics to core problems in operations and supply chain management. This course will use techniques including regression, optimization and simulation to model and analyze problems in inventory management, site selection, revenue management, transportation and logistics. The course will emphasize the use of large, realistic supply chain data sets to develop management insights.
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