Cleaning Data For Effective Data Science

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Cleaning Data For Effective Data Science

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Author : David Mertz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-03-31

Cleaning Data For Effective Data Science written by David Mertz and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-31 with Mathematics categories.

Think about your data intelligently and ask the right questions Key FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses. What you will learnIngest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structuresUnderstand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and BashApply useful rules and heuristics for assessing data quality and detecting bias, like Benford’s law and the 68-95-99.7 ruleIdentify and handle unreliable data and outliers, examining z-score and other statistical propertiesImpute sensible values into missing data and use sampling to fix imbalancesUse dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your dataWork carefully with time series data, performing de-trending and interpolationWho this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.

Data Cleaning For Effective Data Science

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Author : David Mertz
language : en
Publisher: Addison-Wesley Professional
Release Date : 2021-02

Data Cleaning For Effective Data Science written by David Mertz and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02 with categories.

The Foundation Of Insights Mastering Data Cleaning For Effective Data Science

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Author : S Williams
language : en
Publisher: NFT Publishing
Release Date : 2025-04-15

The Foundation Of Insights Mastering Data Cleaning For Effective Data Science written by S Williams and has been published by NFT Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.

In today’s data-driven world, the ability to transform raw, unrefined information into actionable insights is more critical than ever. The Foundation of Insights: Mastering Data Cleaning for Effective Data Science equips readers with the essential tools and strategies needed to tackle data quality issues , address missing data solutions , and overcome barriers like duplicate entries and inconsistent formats . This comprehensive guide dives deep into the importance of clean data , exploring its role in powering accurate analytics, robust machine learning models, and informed decision-making across industries such as healthcare , finance , and retail . From understanding the science behind data cleaning to leveraging cutting-edge innovations like AI-driven anomaly detection , automated ETL pipelines , and cloud-based data platforms , this book provides a roadmap for mastering modern data practices. It also addresses pressing challenges such as automation resistance , lack of expertise, and time constraints , offering practical steps to ensure high-quality datasets that drive innovation without compromising ethical principles. Readers will explore the ethical implications of data cleaning , including debates on data privacy concerns , bias mitigation , and the societal impact of incomplete or inaccurate datasets. With discussions on existing regulatory frameworks and consumer protection laws , alongside an application of Kantian ethics to foster fairness, inclusivity, and accountability, this book bridges the gap between technical proficiency and moral responsibility. Whether you're looking to integrate data cleaning techniques seamlessly into broader workflows or seeking long-term benefits for your organization, this resource delivers actionable strategies tailored to real-world scenarios. By blending statistical methods , database management strategies , and universal ideals, it paints a vision for a future where clean data empowers innovation while upholding trust and transparency. Packed with industry-specific examples, emerging trends, and hands-on guidance, this book is an indispensable companion for anyone committed to achieving excellence in data governance , enhancing data accuracy , and building ethical data frameworks that stand the test of time.

Recent Developments In Data Science And Business Analytics

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Author : Madjid Tavana
language : en
Publisher: Springer
Release Date : 2018-03-27

Recent Developments In Data Science And Business Analytics written by Madjid Tavana and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-27 with Business & Economics categories.

This edited volume is brought out from the contributions of the research papers presented in the International Conference on Data Science and Business Analytics (ICDSBA- 2017), which was held during September 23-25 2017 in ChangSha, China. As we all know, the field of data science and business analytics is emerging at the intersection of the fields of mathematics, statistics, operations research, information systems, computer science and engineering. Data science and business analytics is an interdisciplinary field about processes and systems to extract knowledge or insights from data. Data science and business analytics employ techniques and theories drawn from many fields including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition, visualization, descriptive analytics, predictive analytics, prescriptive analytics, uncertainty modeling, big data, data warehousing, data compression, computer programming, business intelligence, computational intelligence, and high performance computing among others. The volume contains 55 contributions from diverse areas of Data Science and Business Analytics, which has been categorized into five sections, namely: i) Marketing and Supply Chain Analytics; ii) Logistics and Operations Analytics; iii) Financial Analytics. iv) Predictive Modeling and Data Analytics; v) Communications and Information Systems Analytics. The readers shall not only receive the theoretical knowledge about this upcoming area but also cutting edge applications of this domains.

Data Science For Decision Makers

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Author : Erik Herman
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-12-31

Data Science For Decision Makers written by Erik Herman and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-31 with Computers categories.

Data Science for Decision Makers is an essential guide for executives, managers, entrepreneurs, and anyone seeking to harness the power of data to drive business success. In today's fast-paced and increasingly digital world, the ability to make informed decisions based on data-driven insights is vital. This book serves as a bridge between the complex world of data science and the strategic decision-making process, providing readers with the knowledge and tools they need to leverage data effectively. With a clear focus on practical application, this book demystifies key concepts in data science, from data collection and analysis to predictive modeling and visualization. Via real-world examples, case studies, and actionable insights, readers will learn how to extract insights from data and translate them into actionable strategies that drive organizational growth. Written in a reader-friendly manner, this book caters to both novice and experienced professionals alike. Whether you're a seasoned executive looking to sharpen your strategic acumen or a manager seeking to enhance your team's data literacy, this essential reference provides the necessary foundation to navigate the complex landscape of data science with confidence.

Clean Data

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Author : Tomasz Lelek
language : en
Publisher:
Release Date : 2018

Clean Data written by Tomasz Lelek and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.

"Effective data cleaning is one of the most important aspects of good Data Science and involves acquiring raw data and preparing it for analysis, which, if not done effectively, will not give you the accuracy or results that you're looking to achieve, no matter how good your algorithm is. Effective data cleaning is one of the most important aspects of good Data Science and involves acquiring raw data and preparing it for analysis, which, if not done effectively, will not give you the accuracy or results that you're looking to achieve, no matter how good your algorithm is. Data Cleaning is the hardest part of big data and ML. To address this matter, this course will equip you with all the skills you need to clean your data in Python, using tried and tested techniques. You'll find a plethora of tips and tricks that will help you get the job done, in a smart, easy, and efficient way."--Resource description page.

Data Cleaning

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Author : Ihab F. Ilyas
language : en
Publisher: Morgan & Claypool
Release Date : 2019-06-18

Data Cleaning written by Ihab F. Ilyas and has been published by Morgan & Claypool this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-18 with Computers categories.

This is an overview of the end-to-end data cleaning process. Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and incorrect business decisions. Poor data across businesses and the U.S. government are reported to cost trillions of dollars a year. Multiple surveys show that dirty data is the most common barrier faced by data scientists. Not surprisingly, developing effective and efficient data cleaning solutions is challenging and is rife with deep theoretical and engineering problems. This book is about data cleaning, which is used to refer to all kinds of tasks and activities to detect and repair errors in the data. Rather than focus on a particular data cleaning task, this book describes various error detection and repair methods, and attempts to anchor these proposals with multiple taxonomies and views. Specifically, it covers four of the most common and important data cleaning tasks, namely, outlier detection, data transformation, error repair (including imputing missing values), and data deduplication. Furthermore, due to the increasing popularity and applicability of machine learning techniques, it includes a chapter that specifically explores how machine learning techniques are used for data cleaning, and how data cleaning is used to improve machine learning models. This book is intended to serve as a useful reference for researchers and practitioners who are interested in the area of data quality and data cleaning. It can also be used as a textbook for a graduate course. Although we aim at covering state-of-the-art algorithms and techniques, we recognize that data cleaning is still an active field of research and therefore provide future directions of research whenever appropriate.

Data Cleaning The Ultimate Practical Guide

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Author : Lee Baker
language : en
Publisher: Lee Baker
Release Date : 2022-11-07

Data Cleaning The Ultimate Practical Guide written by Lee Baker and has been published by Lee Baker this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-07 with Business & Economics categories.

Transform your data woes into wins with "Data Cleaning: The Ultimate Practical Guide - From Dirty Data to Clean Data." No more staring blankly at error messages or struggling to make sense of messy datasets. This friendly and approachable guide is your passport to mastering the art of data cleaning. Ever wondered what makes data 'dirty' or 'clean'? This book dives deep into demystifying these concepts, equipping you with the knowledge to identify and eliminate errors efficiently. Learn how to prevent common data pitfalls from sneaking into your analyses, ensuring your data is not just clean but also primed for impactful insights. Forget dense technical jargon—this guide speaks your language. Perfect for beginners and seasoned professionals alike, it breaks down complex processes into simple, actionable steps. From understanding the phases of data cleaning to mastering essential pre-processing techniques, each chapter is crafted to empower you with practical skills. Discover: - The 4 crucial phases of data cleaning - 6 common types of dirty data and how to address them - Insights into 5 data collection methods and a streamlined 5-step cleaning process - Effective data pre-processing using straightforward summary statistics Whether you're a researcher, analyst, or simply curious about optimizing your data practices, this book is your go-to resource. By the time you finish reading, you'll possess a comprehensive understanding of data preparation—empowering you to unleash the true potential of your analyses. Ready to elevate your data skills? Don't wait—order "Data Cleaning: The Ultimate Practical Guide" today and take the first step towards cleaner, more impactful data analysis!

Journal Of Information Science

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Author :
language : en
Publisher:
Release Date : 2004

Journal Of Information Science written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Information science categories.

Library Information Science Abstracts

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Author :
language : en
Publisher:
Release Date : 2002

Library Information Science Abstracts written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Information science categories.