This book introduces mathematical and machine learning foundations for modern data analysis. Many students or data analysts may want to learn these subjects in a simpler way in many different areas, such as the environment, biology, social sciences, and engineering, but know a little bit about mathematics, statistics, computing, or programming. This textbook covers, in particular, the basics of regression, classification, clustering, data fitting, optimization, time series, and visualization in use today. It intends to provide a new, more friendly course not only for computing and mathematical students, but also for various other disciplines. You are not assumed to have had any previous programming experience, and the book provides a practical guide to installing Jupyter and Python for scientific computing by yourself. It uses practical examples to help you gain an intuitive understanding of the concepts, principles, and tools for data analysis, provides practical guidance on applying mathematics to data science, and understand the abilities and limitations to avoid their misuse. Practical code examples and exercises are provided throughout the book to help you practise what you’ve learned. Data files and Python codes are available on GitHub. It is ideal for beginners to self-study online or on campus, full- or part-time.
Child life specialists need research skills, but few resources exist. Combining clinical examples with advice from seasoned researchers, this text guides you from identifying a clinical question to reporting results and improving patient outcomes.
