Singapore University of Social Sciences

Data Technologies for Financial Modelling (FIN205)

Applications Open: 01 April 2020

Applications Close: 31 May 2020

Next Available Intake: July 2020

Course Types: Modular Undergraduate Course

Language: English

Duration: 6 months

Fees: $1378 View More Details on Fees

Area of Interest: Finance

Schemes: Lifelong Learning Credit (L2C)

Funding: To be confirmed


Synopsis

A business graduate needs to be conversant with data and spreadsheet technologies so that he/shewill be able to implement financial models efficiently for analysis, budgeting, forecasting, planningand simulations in their work in organisations pertaining to financial matters.FIN205 Data Technologies for Financial Modelling aims to equip business graduates with therequisite knowledge and skills relating to the kinds of technologies that are available and how touse the most popularly important ones (e.g. Excel) in calculations and the modelling of financialsituations.A broad range of topics, covering financial modelling methods, financial valuation, pricing and risk,simulations and event studies, strategies for decision making and financial data tools andtechnologies are covered. Such knowledge and skills would be highly useful for those consideringto work in the financial industries as well as the finance or analytics department of a company, thoseaspiring to start their own business or those who work in smaller firms that do not employ financialspecialists.

Level: 2
Credit Units: 5
Presentation Pattern: Every semester

Topics

  • Basic financial calculations
  • Calculating cost of capital
  • How financial models work
  • Financial statement modelling
  • Asset valuation
  • Option pricing
  • Portfolio risk
  • Option risk
  • Event studies and simulations
  • Portfolio insurance
  • Using Excel tools to power up modelling
  • Using Python to power up modelling

Learning Outcome

  • Demonstrate understanding on the basics of financial modelling
  • Illustrate considerations in financial statements with modelling
  • Analyse different financial pricing/valuation models
  • Appraise the notion of risk and measurements through modelling methods
  • Develop strategies in finance for decision making
  • Contrast the limitations and advantages of different data analytic tools
  • Use data technologies such as Excel and Python to model and understand financial problems effectively
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