Название: Learning Data Science: Programming and Statistics Fundamentals Using Python (7th Early Release)Автор: Sam Lau, Deborah Nolan, Joseph Gonzalez
Издательство: O’Reilly Media, Inc.
Год: 2023-05-19
Страниц: 689
Язык: английский
Формат: epub
Размер: 11.6 MB
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.
Data scientists work with data stored in tables. The Chapter 3 introduces dataframes, one of the most widely used ways to represent data tables. We’ll also introduce Pandas, the standard Python package for working with dataframes. Data types in a programming sense refers to how a computer stores data internally. For instance, the size column has a string data type in Python. But from a statistical point of view, the size column stores ordered categorical data (ordinal data). We talk more about this specific distinction in the next chapter. In this chapter, we’ll show you how to do common dataframe operations using pandas. Data scientists use the Pandas library when working with dataframes in Python. First, we’ll explain the main objects that pandas provides: the DataFrame and Series classes. Then, we’ll show you how to use pandas to perform common data manipulation tasks, like slicing, filtering, sorting, grouping, and joining.
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
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