Associated Assessment Criteria for Exit Level Outcome 1:

• Generate visual and numerical data summaries using Excel.
• Perform basic data cleaning for analysis.
• Translate data into actionable insights.
• Manage files and data using Linux.
• Assess and compare various models.
• Interpret machine learning results.
• Draw graphs of multivariable functions.
• Perform exploratory and descriptive data analysis.
• Compute expected values and variances.
• Understand continuous statistical distributions.

Associated Assessment Criteria for Exit Level Outcome 2:

• Fit a linear model in Excel.
• Identify clear business objectives for Big Data analysis.
• Transform text into data for Natural Language Processing.

Associated Assessment Criteria for Exit Level Outcome 3:

• Define fundamental data science concepts.
• Describe the data cycle and CRISP data mining process.
• Recognize ethical and legal issues in data science.
• Collaborate effectively as a Data Scientist.
• Explain the importance of Big Data analytics.
• Model patterns in complex datasets.
• Apply statistical learning techniques.
• Discuss approaches to the "curse of dimensionality."
• Explain traditional statistical procedures in data mining.
• Answer theoretical questions on machine learning techniques.
• Define and apply distribution theory.
• Apply limit distributions to practical problems.
• Explain different approaches to inference.
• Discuss Bayesian inference and its application.
• Explain vector calculus theorems.
• Use integration techniques in multiple integrals.
• Interpret linear algebra algorithms using matrices.

Associated Assessment Criteria for Exit Level Outcome 4:

• Demonstrate effective written communication of data analysis.
• Analyze and summarize data visually.
• Communicate data solutions effectively.
• Present research findings and methodology.
• Write structured scientific documents.
• Use appropriate referencing in scientific writing.
• Present oral reports on projects.
• Present machine learning results effectively.

Associated Assessment Criteria for Exit Level Outcome 5:

• Evaluate model performance on Big Data and text data.
• Program in Python with Numphy and Matplotlib.
• Implement machine learning techniques in Python.
• Work alone or in a team on software development.

Associated Assessment Criteria for Exit Level Outcome 6:

• Design algorithms for data-related problems.
• Aggregate data from various sources.
• Adapt to new models and technologies.

Associated Assessment Criteria for Exit Level Outcome 7:

• Apply Big Data tools for business decision-making.
• Transform random variables for probability distributions.
• Use central limit theorem and sampling distributions for estimation.

Associated Assessment Criteria for Exit Level Outcome 8:

• Understand negotiation, leadership, and communication in software development.

Integrated Assessment:

• Flexible assessment involves various assessment methods.
• Examination system includes tests and exams for final marks.
• Formative assessments in practicals and tutorials.
• Summative assessments in tests and exams.
• Learners are responsible for their learning and engagement.