Advanced Diploma in Mathematical Sciences

Associated Assessment Criteria for Exit Level Outcome 1

• Knowledge and Understanding: Use integrated knowledge of advanced mathematics to solve problems accurately.
• Application of Mathematics Concepts: Apply mathematics concepts, theorems, algorithms, and procedures in various contexts.
• Programming Skills: Apply advanced programming using software like R, Matlab, and SAS to find solutions in financial and biomathematics.
• Statistical Analysis: Apply statistical models, probability theorems, and tests to analyze processes in Financial Mathematics and Biomathematics.
• Multivariate Statistics: Use tools like regression and Principal component analysis in analyzing financial or biosciences data sets.
• Financial Mathematics Principles: Apply principles, strategies, and techniques in various financial contexts.
• Sub-disciplines Knowledge: Apply knowledge from sub-disciplines like Biostatistics and Business Analytics in solving problems.
• Data Mining: Use standard data mining software in practical problem-solving.

Associated Assessment Criteria for Exit Level Outcome 2

• Formulate Models: Create conceptual and quantitative models for real-world problems in finance and biosciences.
• Core Knowledge Application: Competently model problems in Biosciences including disease modeling.
• Time Series Models: Use appropriate models and algorithms for forecasting in finance.
• Numerical Techniques: Select and implement correct numerical techniques for various problems.
• Computational Tools: Use tools like Mat Lab, R, and statistical packages for data manipulation and modeling.
• Data Mining Phases: Understand and apply phases of data mining competently.

Associated Assessment Criteria for Exit Level Outcome 3

• Structured Approach: Use a structured analytical approach in problem-solving.
• Mathematical Structures: Identify correct mathematical structures in given models.
• Problem Solving: Solve open-ended problems by formulating them accurately.
• Critical Analysis: Analyze mathematical models critically and propose necessary changes.

Associated Assessment Criteria for Exit Level Outcome 4

• Professional Responsibilities: Understand and abide by professional responsibilities and ethical norms.
• Validation of Solutions: Check answers and solutions for validity, reliability, and accuracy.
• Analytical Methods: Understand the limitations and power of analytical methods.
• Justification: Justify and validate conclusions and decisions accurately.

Associated Assessment Criteria for Exit Level Outcome 5

• Evaluation of Information: Access, evaluate, and synthesize scientific or financial information.
• Academic Integrity: Ensure academic integrity and social responsibility in compiling reports.
• Communication Skills: Demonstrate effective written, visual, and oral presentation skills.
• Mathematical Arguments: Construct mathematical arguments accurately.
• Language Usage: Use correct language in producing clear written documents.
• Collaboration: Communicate effectively online and work collaboratively.

Associated Assessment Criteria for Exit Level Outcome 6

• Knowledge Demonstration: Demonstrate knowledge of financial instruments, asset pricing, and portfolio selection.
• Data Analysis: Analyze financial or bio data from various sources.

Associated Assessment Criteria for Exit Level Outcome 7

• Learning Management: Reflect on and manage own learning needs with minimum guidance.
• Independent Investigation: Carry out independent investigations using available literature and databases.
• Learning Strategies: Develop effective learning strategies and skills.
• Feedback Utilization: Seek and utilize feedback for self-evaluation and improvement.
• Time Management: Manage time effectively to meet standards and deadlines.

Associated Assessment Criteria for Exit Level Outcome 8

• Model Adaptation: Critically challenge assumptions and modify models as needed.
• Data Analysis Principles: Implement data analysis principles with integrity and confidentiality.
• Problem-Solving Approaches: Evaluate different approaches to problem-solving.

Integrated Assessment

• Continuous Assessment: Forms part of continuous assessment with a mix of formative and summative methods.
• Assessment Policy: Promotes constructive alignment, learner-centered learning, and fair assessment practices.
• Formative Assessment: Enhances student learning through various tasks.
• Summative Assessment: Assesses learner's attainment against learning outcomes.
• Integrated Assessment: Holistic assessment across modules for learner development.
• Emphasis: Integration of knowledge, skills, and abilities in Mathematics, Statistics, and Computing.