Computing for Data Analysis: Foundations of Programming and Data Wrangling

Data is only as valuable as your ability to process and analyze it. To succeed in modern data science, you need a strong foundation in the programming principles that power data analysis, mining, and machine learning. This course guides you from absolute beginner to a confident data programmer. You will learn how to write clean, efficient code to ingest, clean, and manipulate datasets, moving beyond manual spreadsheet work to automated, scalable data workflows. What you'll learn: 1. Understand foundational programming concepts, including variables, data structures, and control flow tailored for data tasks. 2. Clean and manipulate structured data using modern dataframe libraries and vectorization techniques. 3. Apply data mining principles to extract meaningful patterns from raw, unstructured datasets. 4. Implement essential mathematical and statistical computations programmatically. 5. Write maintainable code using modern programming best practices, type hints, and basic testing. 6. Prepare and pipeline clean datasets for downstream machine learning models. You will start with core computational concepts and basic syntax, then progress to text-based explanations and written exercises where you analyze code to filter, merge, and transform real-world datasets. This course is designed for beginners who want to build a solid technical foundation in data computing, with no prior programming experience required. Start reading today to unlock the power of programmatic data analysis.