Python and Statistics for Biotechnology

In the field of biotechnology, research and data analysis increasingly rely on computational power. This text-based course bridges the gap, introducing you to Python programming and statistical methods specifically applied to biological and scientific datasets. You will transition from having no coding experience to confidently writing Python scripts, manipulating data, and performing essential statistical tests to draw meaningful conclusions from scientific observations. What you'll learn: - Learn core Python programming concepts including variables, loops, functions, and modern type hints. - Understand foundational statistical principles like descriptive statistics, probability distributions, and hypothesis testing. - Apply Python data structures and libraries to clean, filter, and organize complex scientific datasets. - Practice writing clean, readable code using modern Python conventions. - Perform statistical tests on experimental data to validate research hypotheses. - Analyze biotechnology scenarios, from gene expression levels to experimental trial data. Starting with fundamental programming syntax, scientific terminology, and basic statistical definitions, you will gradually progress to combining these skills to solve practical problems. Through structured written explanations and step-by-step code walkthroughs, you will build a solid foundation in computational science. This course is designed for students, researchers, and beginners in biotechnology and life sciences who want to learn Python and statistics from scratch, with no prior programming background required. Start your journey toward computational research today.