This data analysis course is designed for students as well as individuals or professionals who want to gain a solid understanding of statistics and develop practical skills using R Studio software. As a PhD holder with a double competence in life sciences and statistics, I will guide you through the fundamental principles of biostatistics using concrete examples from the agronomic and forestry fields. During this program, we will cover the following topics: I. Introduction to the basic concepts of statistics: You will learn the fundamental notions such as measures of central tendency, dispersion, probability distributions, hypothesis testing and estimation. II. Data collection and preparation: You will learn best practices for collecting and preparing your data for statistical analysis. We will also discuss common data quality issues and techniques to resolve them. III. Data exploration and visualization: You will explore different methods to visualize your data and extract meaningful insights. We will use graphs and advanced visualization techniques to identify patterns and relationships between variables. IV. Statistical analysis techniques: You will learn to apply common statistical methods such as parametric and non-parametric tests, linear and logistic regression, analysis of variance (ANOVA) and correlation analysis. V. Introduction to machine learning with R: You will learn the basics of machine learning and explore popular techniques such as tree regression, random forests, and classification methods. We'll see how to use these techniques to predict outcomes and make data-driven decisions. This course will focus on practical applications and will use the R Studio software, a reference in the field of data analysis, which is also free to access. You will develop practical skills by manipulating real datasets and applying appropriate statistical techniques to draw meaningful conclusions. Whether you are a student, professional, or simply interested in data analysis, this course will provide you with the foundation to understand and apply statistics in a variety of contexts, with an emphasis on the fields of life sciences and agronomy. Do not hesitate to register and join this course to acquire a strong skill in data analysis and improve your evidence-based decision making.