Best Software For Cleaning Business

Description

Introducing the “Best Software for Cleaning Business”, the ultimate solution to‍ streamline and optimize your cleaning ⁣company’s ⁣operations. With a user-friendly interface and cutting-edge features, this software is specifically designed to meet the unique needs of the cleaning industry.

One of the standout‌ features of the “Best⁤ Software for Cleaning Business” is its comprehensive scheduling system. Say goodbye to ​the hassle of managing multiple cleaning ​appointments manually.⁢ This software allows you to effortlessly create and customize schedules for your cleaning‌ staff, ‍ensuring ​that no⁢ client is ever overlooked or double-booked. You can easily track ⁢the status of each appointment, ⁤assign‌ cleaners, and even send automatic ⁢reminders to clients,⁣ resulting in improved efficiency​ and customer satisfaction.

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The “Best Software

Product Description For Best Software For Cleaning Business

Price: $43.99 - $18.49
(as of Sep 30, 2023 13:00:03 UTC – Details)

Think about your data intelligently and ask the right questions

Key Features

Master data cleaning techniques necessary to perform real-world data science and machine learning tasks
Spot common problems with dirty data and develop flexible solutions from first principles
Test and refine your newly acquired skills through detailed exercises at the end of each chapter

Book 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 learn

Ingest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structures
Understand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and Bash
Apply useful rules and heuristics for assessing data quality and detecting bias, like Benford’s law and the 68-95-99.7 rule
Identify and handle unreliable data and outliers, examining z-score and other statistical properties
Impute sensible values into missing data and use sampling to fix imbalances
Use dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your data
Work carefully with time series data, performing de-trending and interpolation

Who 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.

Table of Contents

Data Ingestion – Tabular Formats
Data Ingestion – Hierarchical Formats
Data Ingestion – Repurposing Data Sources
The Vicissitudes of Error – Anomaly Detection
The Vicissitudes of Error – Data Quality
Rectification and Creation – Value Imputation
Rectification and Creation – Feature Engineering
Ancillary Matters – Closure/Glossary

From the Publisher

Key Features:

The first book to focus on all aspects of data cleaning
Hands-on examples of working with all the main data formats used in data science
Tips and tricks for identifying and remediating data integrity and hygiene problems

How does Cleaning Data for Effective Data Science help readers master data cleansing?

The book follows the usual path, from data acquisition and analysis to remediation, tasks that every data scientist needs to perform. This book fleshes out essential content on data preparation that many existing books treat as a throwaway first chapter, and gives you skills in real-world data science.

Each chapter discusses tools and techniques to perform the relevant task.  Long-form real-world exercises are provided at the end of each chapter to reinforce your knowledge.

This book includes:

Minimal use of heavy mathematics or statistics, with some references to important concepts
Personal conclusions that I derived from my years of experience in data cleaning
Real-world exercises to help you gain confidence on the concepts learned

What makes this book special?

This book hopes to show you a good range of techniques you will need in preparing data for analysis and modeling. I addressed most of the common data formats that you will encounter in your daily work. The chapters of this book are arranged into something resembling the order of stages of the pipelines you will develop in your data science practice.

The focus of each chapter is on the conceptual actions needed, in a language-agnostic way, but concrete example code is provided everywhere. Throughout the book, Python is used most frequently, followed by R, with occasional use of other programming languages. But all exercises simply present one or more datasets and ask you to perform tasks using them. Accomplishing those tasks using the programming language of your choice is wonderful.

If you ask, “Why multiple languages?”, I’d say it broadens the appeal of the book to include folks both with a Python and an R background, and even to some with neither, who find the book’s agnosticism more approachable. The code that’s presented isn’t very complicated (the largest examples are Python) with clear explanations for how it’s used.

Table of Contents:

Data Ingestion – Tabular Formats
Data Ingestion – Hierarchical Formats
Data Ingestion – Repurposing Data Sources
The Vicissitudes of Error – Anomaly Detection
The Vicissitudes of Error – Data Quality
Rectification and Creation – Value Imputation
Rectification and Creation – Feature Engineering
Ancillary Matters – Closure/Glossary

This graph uses a statistical graphing library called Seaborn, which is built on top of Matplotlib

Publisher ‏ : ‎ Packt Publishing (March 31, 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 498 pages
ISBN-10 ‏ : ‎ 1801071292
ISBN-13 ‏ : ‎ 978-1801071291
Item Weight ‏ : ‎ 1.87 pounds
Dimensions ‏ : ‎ 7.5 x 1.13 x 9.25 inches