Bio-Statistics/Big data analysis (Compulsory – 8 ECTS)
This course focuses on data statistical analysis. The objective is for students to develop the knowledge, skills and perspectives necessary to analyze clinical, medical and biology data in order to answer specific research questions. The main focus are statistical principles and applied skills necessary to process biomedical data, including: data acquisition, data analysis, data interpretation and the presentation of results. This course was designed to facilitate the understanding, analysis, and interpretation of biomedical big data. During the labs, practical examples in biomedical signal analysis are conducted using SPSS, R and MATLAB, with focus on cardiology clinical data and bio-sequences analysis.
1. Statistical analysis
- a. Estimating unknown quantities from a sample. Sampling from populations. Estimating population means and standard deviations. Confidence intervals.
- b. Hypothesis testing. Research hypotheses versus statistical hypotheses. Type I and Type II errors. Sampling distributions for test statistics.
- c. Categorical data analysis. Chi-square goodness of fit test. Chi-square test of independence. Yate’s continuity correction. Effect size with Cramer’s V. Fisher exact test and McNemar’s test.
- d. Comparing two means. One sample z-test. One sample t-test. Student’s independent sample t-test. Welch’s independent samples t-test. Paired sample t-test. Wilcoxon tests for non-normal data.
- e. Comparing several means. Introduction to one-way ANOVA. Assumptions of one-way ANOVA. Relationship between ANOVA and t-tests.
- f. Regression. Introduction to regression. Hypothesis tests for regression models. Logistic Regression. Multinomial Regression.
2. Big Data
- a. Analytical Methods
- b. Descriptive analytics
- c. Predictive analytics
- d. Prescriptive analytics
- e. Clinical decision support systems
