March 2021
Beginner to intermediate
498 pages
12h 3m
English
Overview
Dive into the intricacies of data cleaning, a crucial aspect of any data science and machine learning pipeline, with 'Cleaning Data for Effective Data Science.' This comprehensive guide walks you through tools and methodologies like Python, R, and command-line utilities to prepare raw data for analysis. Learn practical strategies to manage, clean, and refine data encountered in the real world.
What this Book will help me do
- Understand and utilize various data formats such as JSON, SQL, and PDF for data ingestion and processing.
- Master key tools like pandas, SciPy, and Tidyverse to manipulate and analyze datasets efficiently.
- Develop heuristics and methodologies for assessing data quality, detecting bias, and identifying irregularities.
- Apply advanced techniques like feature engineering and statistical adjustments to enhance data usability.
- Gain confidence in handling time series data by employing methods for de-trending and interpolating missing values.
Author(s)
David Mertz has years of experience as a Python programmer and data scientist. Known for his engaging and accessible teaching style, David has authored numerous technical articles and books. He emphasizes not only the technicalities of data science tools but also the critical thinking that approaches solutions creatively and effectively.
Who is it for?
'Cleaning Data for Effective Data Science' is designed for data scientists, software developers, and educators dealing with data preparation. Whether you're an aspiring data enthusiast or an experienced professional looking to refine your skills, this book provides essential tools and frameworks. Prior programming knowledge, particularly in Python or R, coupled with an understanding of statistical fundamentals, will help you make the most of this resource.
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