Outbreak Prediction and Data Analysis with Python

Understanding how diseases spread is a critical skill in modern data science and public health. This course introduces you to the essential techniques used to process health data and predict future trends. You will move from raw data to actionable insights, learning how to clean complex datasets, visualize global trends, and apply machine learning algorithms to predict future cases. What you'll learn: - Understand fundamental epidemiological data structures and terminology - Perform exploratory data analysis to identify trends and anomalies in health records - Create clear data visualizations to track confirmed cases, recoveries, and mortality rates - Apply the Random Forest algorithm to build predictive models for case forecasting - Evaluate model performance using modern validation techniques - Practice data cleaning and preprocessing specifically for time-series health data The course begins with foundational concepts of data science and key terminology before moving into the practical application of Python for data manipulation and predictive modeling. This course is designed for absolute beginners interested in data science or health analytics, and no prior programming experience is required. Start building your data science skills by analyzing real-world health data today.