Lau S., Gonzalez J., Nolan D. Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python

O’Reilly Media, 2023. - 594 p. - ISBN: 1098113004.As an aspiring data scientist, you appreciate why organizations rely on data for important decisions — whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas.Refine a question of interest to one that can be studied with data.
Pursue data collection that may involve text processing, web scraping, etc.
Glean valuable insights about data through data cleaning, exploration, and visualization.
Learn how to use modeling to describe the data.
Generalize findings beyond the data.Expected Background KnowledgeWe expect readers to be proficient in Python and understand how to use built-in data structures like lists, dictionaries, and sets; import and use functions and classes from other packages; and write functions from scratch. We also use the numpy Python package without introduction but don’t expect readers to have much prior experience using it. Readers will get more from this book if they also know a bit about probability, calculus, and linear algebra, but we aim to explain mathematical ideas intuitively.True PDF