Bachelor Dental Science: Biomedical Data Analysis Module (5 ECTS)

Bachelor Dental Science 180 ECTS / EQF 6 ; unit title: Biomedical Data Analysis 5 ECTST. this unit can include some bioinformatics , as well as a basic statistical theory (ex. Sampling techniques , hypothesis testing, confidence intervals etc.. could you develop a module with reading list .. please when you consider learning outcome differentiate from knowledge and skills

Certainly! Below is a detailed module outline for the unit titled "Biomedical Data Analysis" worth 5 ECTS, which includes elements of bioinformatics and basic statistical theory.

Module Title: Biomedical Data Analysis

ECTS: 5

EQF Level: 6

Module Description:

This module provides an introduction to the principles and practices of biomedical data analysis, incorporating elements of bioinformatics and basic statistical theory. Students will learn to handle, analyze, and interpret biomedical data using various statistical techniques and bioinformatics tools.

Learning Outcomes:

Knowledge:

1. Understand the fundamental concepts of bioinformatics and its applications in biomedical research.
2. Comprehend basic statistical theories including sampling techniques, hypothesis testing, and confidence intervals.
3. Recognize the importance of data integrity and ethical considerations in biomedical data analysis.

Skills:

1. Apply statistical methods to analyze biomedical data.
2. Utilize bioinformatics tools to interpret biological data.
3. Perform hypothesis testing and construct confidence intervals for biomedical datasets.
4. Communicate findings effectively through written reports and presentations.

Module Content:

1. Introduction to Biomedical Data Analysis

   • Overview of biomedical data types
   • Importance of data analysis in biomedical research

2. Bioinformatics Basics

   • Introduction to bioinformatics
   • Key bioinformatics databases and tools (e.g., NCBI, BLAST, GenBank)
   • Sequence alignment and analysis

3. Statistical Theory

   • Descriptive statistics
   • Probability distributions
   • Sampling techniques
   • Hypothesis testing
   • Confidence intervals

4. Data Handling and Preprocessing

   • Data cleaning and preparation
   • Handling missing data
   • Data normalization and transformation

5. Statistical Analysis Techniques

   • t-tests, chi-square tests, ANOVA
   • Regression analysis
   • Non-parametric tests

6. Ethical Considerations in Data Analysis

   • Data privacy and security
   • Ethical issues in biomedical research

7. Practical Applications and Case Studies

   • Real-world examples of biomedical data analysis
   • Hands-on exercises using statistical software (e.g., R, Python)

Teaching Methods:

• Lectures
• Practical lab sessions
• Group discussions
• Case study analysis
• Independent study

Assessment Methods:

• Written exams
• Practical assignments
• Group projects
• Oral presentations

Reading List:

Core Textbooks:

1. "Bioinformatics: Sequence and Genome Analysis" by David W. Mount

   • A comprehensive guide to bioinformatics, covering sequence alignment, database searching, and more.

2. "Introduction to the Practice of Statistics" by David S. Moore, George P. McCabe, and Bruce A. Craig

   • A foundational text on statistical methods and their applications.

3. "The Essentials of Biostatistics for Physicians, Nurses, and Clinicians" by Michael R. Chernick and Robert H. Friis

   • A practical guide to biostatistics in the medical field.

Supplementary Readings:

1. "Practical Statistics for Medical Research" by Douglas G. Altman

   • A detailed exploration of statistical techniques used in medical research.

2. "Bioinformatics for Dummies" by Jean-Michel Claverie and Cedric Notredame

   • An accessible introduction to bioinformatics for beginners.

3. "R for Data Science" by Hadley Wickham and Garrett Grolemund

   • A hands-on guide to data analysis using R.

4. "Python for Data Analysis" by Wes McKinney

   • A comprehensive resource for data analysis using Python.

Journal Articles and Online Resources:

1. "The NCBI Handbook" - Available online at the NCBI website.
2. "Bioinformatics: An Introduction" - PLOS Computational Biology.
3. "Statistical Methods in Medical Research" - Journal articles from the journal "Statistical Methods in Medical Research."

Software and Tools:

• R and RStudio
• Python (with libraries such as Pandas, NumPy, SciPy)
• Bioinformatics tools (e.g., BLAST, Clustal Omega)

This module aims to equip students with both the theoretical knowledge and practical skills necessary for effective biomedical data analysis, preparing them for further study or careers in the