Data Analysis and Predictive Modeling

The main goal of the Analysis and Predictive Modeling of Data course is to illustrate the fundamental concepts of basic statistics applied to biomedical sciences. The course integrates descriptive and inferential statistics to equip students with essential skills for data collection, analysis, interpretation, and presentation, with a particular focus on data collected during their Master's Thesis. Specifically, the course aims to:
1. Structure of a Scientific Paper and the Role of Data Analysis and Data Collection:
· Familiarize students with the structure of a scientific paper, section by section, emphasizing the specific role of data analysis.
· Teach students the differences between various types of data (qualitative vs. quantitative, discrete vs. continuous).
· Introduce methods for data collection and sampling from a population for conducting a sample study.

2. Descriptive Statistics:
· Highlight the importance of effective data summarization and presentation using tables and graphs (dot plots, histograms, bar charts, and boxplots).
· Teach students how to calculate and interpret key numerical indicators, including:
◦ Central tendency (mode, mean, and median)
◦ Position (minimum, maximum, percentiles)
◦ Dispersion (range, variance, standard deviation)
◦ Shape (skewness, kurtosis)

3. Inferential Statistics:
· Provide students with tools for making predictions and inferences about a population based on sample data.
· Explain and explore the implications of the Central Limit Theorem, which underpins inferential statistics, enabling confidence interval calculations and hypothesis testing.
· Introduce key statistical tests (e.g., Z-test, Student's t-test for paired and unpaired data, ANOVA, chi-square test). A complete list of statistical tests is detailed in the course syllabus.

4. Predictive Modeling:
· Introduce predictive modeling, which involves building models to make forecasts. This branch of statistics is increasingly important due to the rapid development of artificial intelligence, particularly machine learning.
· Explain how one or more variables (independent variables or predictors) are used to predict another variable (dependent or predicted variable).
· Cover covariance and correlation between variable pairs and predictive models based on:
◦ Simple and multiple linear regression
◦ Polynomial regression
◦ Nonlinear regression, focusing on four types of functions commonly used in biomedical sciences: sinusoidal/cosinusoidal function, exponential function, sigmoidal function, Michaelis-Menten saturation function.

By the end of the course, students will:
1. Enhance Analytical Thinking - Gain familiarity with statistical data analysis and learn to identify patterns, trends, and outliers.
2. Develop Competence in Statistical Techniques - Acquire practical skills in descriptive and inferential statistics, enabling them to analyze datasets, conduct statistical tests, and draw rational conclusions.
3. Make Data-Driven Decisions - Learn how to make informed decisions based on statistical analysis, a critical skill in today's data-driven world.
4. Improve Problem-Solving Skills - Apply statistical methods to tackle complex problems and derive meaningful conclusions from data.
5. Prepare for Academic Careers - Build a solid foundation for students interested in pursuing a Ph.D.
6. Develop Data Presentation Skills - Learn how to present data effectively using graphs, tables, and reports that convey complex information clearly and efficiently.