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Learning Data Science (Data Wrangling, Exploration, Visualization, and Modeling with Python)

List Price: $89.99
SKU:
9781098113001
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  • Product Details

    Author:
    Sam Lau, Joseph Gonzalez, Deborah Nolan
    Format:
    Paperback
    Pages:
    594
    Publisher:
    O'Reilly Media (October 24, 2023)
    Language:
    English
    ISBN-13:
    9781098113001
    ISBN-10:
    1098113004
    Dimensions:
    7" x 9.19"
    File:
    TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20260827163241-20260827.xml
    Folder:
    TWO RIVERS
    List Price:
    $89.99
    Case Pack:
    7
    As low as:
    $77.39
    Publisher Identifier:
    P-PER
    Discount Code:
    C
    Country of Origin:
    United States
    Pub Discount:
    60
    Weight:
    33.6oz
    Imprint:
    O'Reilly Media
  • Overview

    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