This course path introduces the core programming and statistics skills used in data analysis with Python. It is designed for beginners who want to learn how to prepare data, explore patterns, create visualizations, and draw conclusions from data. You will work with common Python libraries for data analysis, including NumPy and pandas for data manipulation, Matplotlib and Seaborn for reporting and visualization, and SciPy for statistical analysis. Along the way, you will practice cleaning data, handling missing values, encoding variables, merging datasets, grouping records, and preparing data for analysis. The path also covers descriptive and inferential statistics, including hypothesis testing and interpretation of statistical results. By the end, you will have a practical foundation for analyzing datasets and communicating insights with clear reports and visualizations.
