BTY587 — Data Analysis And Simulations

Test 1 will assess students' conceptual understanding and practical application of the topics covered in Units I and II. It will include MCQ and application-based questions on data preprocessing, visualization, dimensionality reduction, hypothesis testing, chi-square test, t-test, ANOVA and interpretation of p-values.

Rubric
To enable students to preprocess, clean and visually explore datasets using appropriate plots and dimensionality-reduction techniques also to apply and interpret statistical tests, hypothesis testing

Students will select a relevant biological or healthcare dataset and complete data cleaning, visualization, statistical testing, association/correlation analysis and basic supervised or unsupervised machine-learning analysis. The project must be completed strictly within time line and submitted as a structured research report containing methodology, results, graphs, interpretation, conclusion and references.

Rubric
To enable students to independently apply data preprocessing, visualization, statistical analysis, pattern mining and basic machine-learning techniques to solve a research problem using a real dataset

Students will be provided with a dataset and required to write and execute code for data cleaning, visualization, statistical testing, correlation or association analysis, classification, regression, clustering and model evaluation. Assessment will be based on appropriate technique selection, code correctness, successful execution, output interpretation and completion within the prescribed time.

Rubric
To test students' subject knowledge and provide an opportunity to improve their CA performance in case of low marks or a missed CA. To assess students' ability to write, execute and interpret code