/Cleaning-Data-for-Effective-Data-Science

Cleaning Data for Effective Data Science, published by Packt

Primary LanguageJupyter NotebookMIT LicenseMIT

Cleaning Data for Effective Data Science

This is the code repository for Cleaning Data for Effective Data Science, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

Binder

  • Paperback: 498 pages
  • ISBN-13: 9781801071291
  • Date Of Publication: 30 March 2021

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About the Book

It is something of a truism in data science, data analysis, or machine learning that most of the effort needed to achieve your actual purpose lies in cleaning your data. Written in David’s signature friendly and humorous style, this book discusses in detail the essential steps performed in every production data science or data analysis pipeline and prepares you for data visualization and modeling results.

The book dives into the practical application of tools and techniques needed for data ingestion, anomaly detection, value imputation, and feature engineering. It also offers long-form exercises at the end of each chapter to practice the skills acquired.

You will begin by looking at data ingestion of data formats such as JSON, CSV, SQL RDBMSes, HDF5, NoSQL databases, files in image formats, and binary serialized data structures. Further, the book provides numerous example data sets and data files, which are available for download and independent exploration.

Moving on from formats, you will impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features that are necessary for successful data analysis and visualization goals.

By the end of this book, you will have acquired a firm understanding of the data cleaning process necessary to perform real-world data science and machine learning tasks.

Instructions and Navigation

All of the code for each chapter is within Jupyter Notebooks.

Table of Contents

  1. Preface

    1. Doing the Other 80% of the Work
    2. Types of Grime
    3. Nomenclature
    4. Typography
    5. Taxonomy
    6. Included Code
    7. Running the Book
    8. Using this Book
    9. Data Hygiene
    10. Exercises

  2. Data Ingestion – Tabular Formats

    1. Tidying Up
    2. CSV
    3. Spreadsheets Considered Harmful
    4. SQL RDBMS
    5. Other formats
    6. Data Frames
    7. Exercises
    8. Denouement

  3. Data Ingestion – Hierarchical Formats

    1. JSON
    2. XML
    3. Configuration Files
    4. NoSQL Databases
    5. Denouement

  4. Data Ingestion – Repurposing Data Sources

    1. Web Scraping
    2. Portable Document Format
    3. Image Formats
    4. Binary Serialized Data Structures
    5. Custom Text Formats
    6. Exercises
    7. Denouement

  5. Anomaly Detection

    1. Missing data
    2. Miscoded Data
    3. Fixed Bounds
    4. Outliers
    5. Multivariate Outliers
    6. Exercises
    7. Denouement

  6. Data Quality

    1. Missing Data
    2. Biasing Trends
    3. Benford's Law
    4. Class Imbalance
    5. Normalization and Scaling
    6. Cyclicity and Autocorrelation
    7. Bespoke Validation
    8. Exercises
    9. Denouement

  7. Value Imputation

    1. Typical-Value Imputation
    2. Trend Imputation
    3. Sampling
    4. Exercises
    5. Denouement

  8. Feature Engineering

    1. Date/time fields
    2. String fields
    3. String Vectors
    4. Decompositions
    5. Quantization and Binarization
    6. One-Hot Encoding
    7. Polynomial Features
    8. Exercises
    9. Denouement

  9. Closure

    1. What You Know
    2. What You Don't Know (Yet)

  10. Glossary

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