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COR1305

Modeling & Data Analytics

1 CreditsBoth

Description

Data Science has become one of the main drivers in transforming businesses and organizations. With proper data collection, preparation, analysis and modelling, insights can be achieved leading to better decision making and actions that create positive impact. Data analytics is divided into different levels namely, descriptive analytics, predictive analytics, prescriptive analytics and automated (or embedded) analytics. In this course, students will acquire practical skills in modelling and analysis to resolve business problems using software tools including Excel and Tableau. Knowing how to effectively use these tools to build models and analyse data to solve problems will add tremendous value in our students’ future professional career. This course’s primary focus is on using Excel spreadsheet as a platform to build mathematical models from scratch to represent business problems for detailed analysis. The use of such models to drive understanding and consensus towards generating insights and actions enhanced the assurance of execution success. With the data collected, data manipulation and transformation will be needed to prepare the data into useful forms for analysis. In terms of data analytics, this course will cover descriptive analytics, predictive analytics and prescriptive analytics, using both Excel and Tableau. In-class exercises would be used to present business problem modeling and analysis. Students would interactively develop the skills and experience to deal with open-ended questions, unclear assumptions and incomplete information. In addition to the individual assessments (take-home assignments and in-class quizzes), a group project will allow students to apply the knowledge and skills acquired to solve a business problem of their choice end-to-end where they will define the business questions, collect the necessary data, build the models, perform the data analytics, to draw insights and conclusions.

Requisites

Prerequisites: None

Co-requisites: None

Anti-requisites: None

Attributes

Department: SCIS

Course Level: Undergraduate

Tracks: N/A

Areas: 2nd Major Only - Information Systems Business-Oriented Electives Capabilities - Managing Data Science and Analytics Electives Grad Req - Dig Tech/Data Ana (Intake 2024 onwards) Information Systems Core (Intake 2024 onwards) Tech for Business Core (Intake 2018 and earlier) Technology & Entrepreneurship Technology Studies Cluster

Learning Outcomes

1. Identify the decision variables, parameters, performance measures and consequence variables of a business problem. 2. Create an influence diagram and a black-box view of a business problem. 3. Identify the data requirement for modelling and use spreadsheet as a data repository and its management. 4. Perform statistical analysis and create charts and visualizations to answer descriptive analytics questions to obtain insights to support decision making. 5. Perform regression analysis, business forecasting, and classification to answer predictive analytics questions to understand future trends and predict future outcomes. 6. Create both process-based and time-based simulation models to answer prescriptive analytics questions to obtain the best solutions for problems with multiple possible solutions. 7. Use Excel Solver to create optimization models to answer prescriptive analytics questions to obtain the best solutions subject to system constraints. 8. Create and run macros to perform repetitive tasks to automate processes. 9. Perform what-ifs analysis using spreadsheet functions and add-on tools. 10. Perform trade-off analysis to reflect how much of one performance measure must be sacrificed to achieve a given improvement in another performance measure. 11. Perform sensitivity analysis to examines the effect of small changes in a given input variable on a performance measure. 12. Deliver a group project with presentation and a written report to convince the stakeholders of the value of the proposed modelling and analytics solution to the business problem. 13. Create visually appealing and user-friendly business dashboards to answer business questions. 1. Identify the decision variables, parameters, performance measures and consequence variables of a business problem 2. Create an influence diagram and a black-box view of a business problem 3. Perform data analysis, compute simple statistics and assess risks of decision choices 4. Design a spreadsheet model as a decision support tool and understand its use by end-users 5. Identify key drivers and establish backward relationships between variables when deriving solution to a business problem 6. Identify the data requirement for modelling, and use spreadsheet as a small scale data repository and its management 7. Identify the key benefits of simulation to verify analytical results and create Monte-Carlo simulation in the spreadsheet model 8. Create and run macros to perform repetitive tasks 9. Create modeling situations in real time via Time-based & Process-based simulations 10. Use Excel Solver to perform basic optimization by maximizing/minimizing an objective function and defining system constraints 11. Perform what-ifs analysis using spreadsheet functions and add-on tools 12. Perform trade-off analysis to reflect how much of one performance measure must be sacrificed to achieve a given improvement in another performance measure 13. Perform sensitivity analysis to examines the effect of small changes in a given input variable on a performance measure 14. Evaluate and test possible decision choices under uncertainty and incomplete information 15. Deliver a presentation and written-report to convince the stakeholders of the value of the proposed solution to the business problem

Graduate Learning Outcomes

Disciplinary Knowledge, Multidisciplinary Knowledge, Interdisciplinary Knowledge, Critical thinking & problem solving

Competencies

Data Analytics, Business Requirements Mapping, Process Improvement and Optimisation, Business Performance Management

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