Applied Data Science for Data Analysts

Data analysts often reach a ceiling where standard reporting is no longer enough to drive strategic business decisions. Moving into data science allows you to predict future trends and uncover hidden patterns rather than just explaining the past. This text-based course bridges the gap between traditional data analysis and modern data science. You will learn how to prepare complex datasets, build foundational machine learning models, and use advanced libraries to extract actionable intelligence. By the end of this program, you will be able to apply statistical rigor and predictive logic to any data-driven project with confidence. What you'll learn: - Understand the core differences between data analysis and data science workflows - Apply modern dataframe libraries for efficient data manipulation and cleaning - Build and evaluate regression and classification models to solve common business problems - Practice feature engineering techniques to improve model accuracy and relevance - Explore the basics of prompt engineering for automating data documentation and code generation - Implement best practices for data storytelling and communicating technical results to stakeholders The course begins with essential terminology and the data science lifecycle before moving into practical techniques for modeling and evaluation. You will progress through written explanations and code-based exercises designed to reinforce practical application in a professional environment. This course is designed for beginner-level data analysts and professionals looking to expand their technical toolkit; no prior data science experience is required. Start your journey into the world of predictive analytics today.