Curriculum
Course: Qualifi Level 6 Diploma in Business Mana...
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Text lesson

Unit code: T/618/7055 : Finance for Strategic Decision Makers

Learning Outcomes, Assessment Criteria and Indicative content 

Learning Outcomes 

When awarded credit for this  unit, a student will: 

Assessment Criteria 

Assessment of this learning outcome  will require a student to  demonstrate that they can:

Indicative Content 
1. Be able to evaluate financial information and other data necessary for making informed management decisions. 

1.1 Analyse financial information techniques used to inform decision-making.

1.2 Evaluate the reliability and validity of these techniques.

1.3 Analyse the difference between data and information.

1.4 Analyse additional information / data that could be used to inform decision making, both qualitative and quantitative. 

Decision-making techniques

  • Macroeconomics and financial forecasting considerations.
  • Probability
  • Decision trees.
  • Evaluation of business investment cases. 
2 Be able to apply and evaluate a range of accounting and data analysis techniques, as well as understand the workings of the financial markets. 

2.1 Apply accounting and data analysis techniques.

2.2 Identify financial markets and their operation.

2.3 Assess the role of key market players.

2.4 Analyse the role of foreign exchange and its impact on decision-making. 

Financial markets, institutions, and instruments

  • Markets and how they operate.
  • Key market players and what instruments are traded within them.
  • Shares, bonds, and derivatives.
  • Foreign exchange. 
3 Be able to apply financial spreadsheets and recognise good spreadsheet practice in a business context. 

3.1 Apply data to a financial spreadsheet and show how the tool can be used to provide information.

3.2 Analyse the role of data provision and presentation in providing managers with the opportunity to interpret data

3.3 Evaluate the rationale for data manipulation. 

Introduction to strategic decisionmaking and statistical modelling

  • Introduction to data analysis 
  • Manipulation and interpretation of data