Python Data Wrangling For Business Analytics
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Python Data Wrangling For Business Analytics
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Author : George Snypes
language : en
Publisher:
Release Date : 2024-11-18
Python Data Wrangling For Business Analytics written by George Snypes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-18 with Business & Economics categories.
Master the essential skills of modern data analysis with this comprehensive guide to Python data wrangling, data cleaning, and business analytics. Whether you're a business analyst moving from Excel to Python, a data scientist optimizing workflows, or an analytics professional handling large datasets, this practical guide bridges the gap between basic Python programming and real-world data challenges. What Makes This Book Different: Unlike theoretical guides, this hands-on manual tackles actual business scenarios you'll encounter daily. Learn through practical exercises using real-world datasets from various industries. Master professional-grade data cleaning techniques used by leading companies for customer analysis, sales reporting, financial data processing, and marketing analytics. Essential Skills You'll Master: Data cleaning and preprocessing with pandas and numpy form the foundation of your learning journey. You'll advance to automated data validation and quality checks, ensuring your analyses are built on reliable data. Through hands-on practice, you'll develop expertise in advanced data transformation techniques and complex dataset merging. Time series data handling becomes second nature as you work through real examples. The book covers text data processing, standardization techniques, ETL pipeline development, and crucial performance optimization methods for large datasets. Real-World Applications: Your journey through data wrangling will focus on practical business scenarios. You'll learn to handle data challenges in customer analytics, transforming raw customer data into actionable segments. Sales performance tracking becomes straightforward as you master data integration techniques. Financial reporting transforms from a manual process into an automated workflow. Marketing campaign analysis, supply chain analytics, and operations management datasets become opportunities rather than obstacles. You'll work with multiple data sources, from Excel files and databases to APIs and cloud services. Technical Coverage: The comprehensive guide to pandas for data manipulation starts with fundamentals and progresses to advanced techniques. You'll master step-by-step data cleaning workflows that can be applied immediately in your daily work. Missing data handling strategies ensure no valuable information is lost. Data validation frameworks protect the integrity of your analysis. Automated reporting techniques save hours of manual work. Best practices for reproducible analysis ensure your work meets professional standards. Code optimization methods keep your solutions scalable and efficient.
The Data Wrangling Workshop
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Author : Brian Lipp
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-29
The Data Wrangling Workshop written by Brian Lipp 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 2020-07-29 with Computers categories.
A beginner's guide to simplifying Extract, Transform, Load (ETL) processes with the help of hands-on tips, tricks, and best practices, in a fun and interactive way Key FeaturesExplore data wrangling with the help of real-world examples and business use casesStudy various ways to extract the most value from your data in minimal timeBoost your knowledge with bonus topics, such as random data generation and data integrity checksBook Description While a huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined. If you're a beginner, then The Data Wrangling Workshop will help to break down the process for you. You'll start with the basics and build your knowledge, progressing from the core aspects behind data wrangling, to using the most popular tools and techniques. This book starts by showing you how to work with data structures using Python. Through examples and activities, you'll understand why you should stay away from traditional methods of data cleaning used in other languages and take advantage of the specialized pre-built routines in Python. Later, you'll learn how to use the same Python backend to extract and transform data from an array of sources, including the internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, the book teaches you how to handle missing or incorrect data, and reformat it based on the requirements from your downstream analytics tool. By the end of this book, you will have developed a solid understanding of how to perform data wrangling with Python, and learned several techniques and best practices to extract, clean, transform, and format your data efficiently, from a diverse array of sources. What you will learnGet to grips with the fundamentals of data wranglingUnderstand how to model data with random data generation and data integrity checksDiscover how to examine data with descriptive statistics and plotting techniquesExplore how to search and retrieve information with regular expressionsDelve into commonly-used Python data science librariesBecome well-versed with how to handle and compensate for missing dataWho this book is for The Data Wrangling Workshop is designed for developers, data analysts, and business analysts who are looking to pursue a career as a full-fledged data scientist or analytics expert. Although this book is for beginners who want to start data wrangling, prior working knowledge of the Python programming language is necessary to easily grasp the concepts covered here. It will also help to have a rudimentary knowledge of relational databases and SQL.
Data Wrangling With Python
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Author : Dr. Tirthajyoti Sarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-02-28
Data Wrangling With Python written by Dr. Tirthajyoti Sarkar 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 2019-02-28 with Computers categories.
Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices. Key FeaturesFocus on the basics of data wranglingStudy various ways to extract the most out of your data in less timeBoost your learning curve with bonus topics like random data generation and data integrity checksBook Description For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You’ll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you’ll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets. By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. What you will learnUse and manipulate complex and simple data structuresHarness the full potential of DataFrames and numpy.array at run timePerform web scraping with BeautifulSoup4 and html5libExecute advanced string search and manipulation with RegEXHandle outliers and perform data imputation with PandasUse descriptive statistics and plotting techniquesPractice data wrangling and modeling using data generation techniquesWho this book is for Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have rudimentary knowledge of relational database and SQL.
Python For Business Analytics
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Author : Mahadi Hasan Miraz
language : en
Publisher: Springer Nature
Release Date : 2025-08-14
Python For Business Analytics written by Mahadi Hasan Miraz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-14 with Computers categories.
This book provides a thorough introduction to Python, specifically designed for those in business analytics. It starts with the fundamentals of Python and gradually covers more advanced topics, including data manipulation, visualization, and analytics techniques. The content is structured to help readers build a strong foundation in Python, essential for success in data science and business analytics. The book also features real-world case studies and practical examples, demonstrating how Python can be applied in business decision-making. These insights make it a valuable resource for students and professionals who want to use Python to solve real business problems. Python's importance in today’s data-driven industries cannot be overstated. Proficiency in this programming language enhances the ability to tackle complex challenges and supports strategic decision-making. For organizations, Python enables the setting of data-driven goals, improved performance, and the fostering of continuous learning. Its open-source nature and wide range of online resources make it accessible to everyone, ensuring that users are equipped with the skills needed in a rapidly evolving workplace. This book serves as a comprehensive guide for those aiming to excel in the field of business analytics through the effective use of Python.
Python Data Analysis
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Author : Avinash Navlani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-05
Python Data Analysis written by Avinash Navlani 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 2021-02-05 with Computers categories.
Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide Key FeaturesPrepare and clean your data to use it for exploratory analysis, data manipulation, and data wranglingDiscover supervised, unsupervised, probabilistic, and Bayesian machine learning methodsGet to grips with graph processing and sentiment analysisBook Description Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data. What you will learnExplore data science and its various process modelsPerform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing valuesCreate interactive visualizations using Matplotlib, Seaborn, and BokehRetrieve, process, and store data in a wide range of formatsUnderstand data preprocessing and feature engineering using pandas and scikit-learnPerform time series analysis and signal processing using sunspot cycle dataAnalyze textual data and image data to perform advanced analysisGet up to speed with parallel computing using DaskWho this book is for This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.
Data Mining For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2019-10-14
Data Mining For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Mathematics categories.
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R
Python For Data Analysis
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Author : Wes McKinney
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-09-25
Python For Data Analysis written by Wes McKinney and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-25 with Computers categories.
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
Data Wrangling 101
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Author : Amara Hawthorn
language : en
Publisher: Independently Published
Release Date : 2025-09-08
Data Wrangling 101 written by Amara Hawthorn and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-08 with Computers categories.
Are you tired of drowning in spreadsheets full of duplicates, typos, and confusing formats? Do you want to turn messy, unusable data into clean, reliable information-without being a coding expert? Data Wrangling 101 is your beginner-friendly guide to mastering one of the most essential skills in today's data-driven world: cleaning and transforming raw data. Written in plain English with no fluff, this book shows you step-by-step how to use Python and free, open-source tools to take control of your data-even if you've never programmed before. Inside, you'll discover how to: Identify and fix common data problems (duplicates, missing values, formatting errors). Standardize and reshape data for analysis. Automate tedious cleanup tasks that would take hours in Excel. Work with real-world datasets from spreadsheets, CSVs, and APIs. Use powerful Python libraries like Pandas and NumPy to handle data with ease. Packed with practical examples, exercises, and mini-projects, this book doesn't just teach you what to do-it shows you how to think like a data wrangler. By the end, you'll have the confidence to take messy, chaotic data and turn it into insights you can actually use. Whether you're a student, researcher, business analyst, or just curious about data, this is the perfect starting point for your journey into data science.
Practical Python Data Wrangling And Data Quality
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Author : Susan E. McGregor
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-12-03
Practical Python Data Wrangling And Data Quality written by Susan E. McGregor and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-03 with Computers categories.
The world around us is full of data that holds unique insights and valuable stories, and this book will help you uncover them. Whether you already work with data or want to learn more about its possibilities, the examples and techniques in this practical book will help you more easily clean, evaluate, and analyze data so that you can generate meaningful insights and compelling visualizations. Complementing foundational concepts with expert advice, author Susan E. McGregor provides the resources you need to extract, evaluate, and analyze a wide variety of data sources and formats, along with the tools to communicate your findings effectively. This book delivers a methodical, jargon-free way for data practitioners at any level, from true novices to seasoned professionals, to harness the power of data. Use Python 3.8+ to read, write, and transform data from a variety of sources Understand and use programming basics in Python to wrangle data at scale Organize, document, and structure your code using best practices Collect data from structured data files, web pages, and APIs Perform basic statistical analyses to make meaning from datasets Visualize and present data in clear and compelling ways
Business Analytics With Python
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Author : Bowei Chen
language : en
Publisher: Kogan Page Publishers
Release Date : 2025-03-03
Business Analytics With Python written by Bowei Chen and has been published by Kogan Page Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-03 with Computers categories.
Data-driven decision-making is a fundamental component of business success. Use this textbook to help you learn and understand the core knowledge and techniques needed for analysing business data with Python programming. Business Analytics with Python is ideal for students taking upper level undergraduate and postgraduate modules on analytics as part of their business, management or finance degrees. It assumes no prior knowledge or experience in computer science, instead presenting the technical aspects of the subject in an accessible, introductory way for students. This book takes a holistic approach to business analytics, covering not only Python as well as mathematical and statistical concepts, essential machine learning methods and their applications. Features include: - Chapters covering preliminaries, as well as supervised and unsupervised machine learning techniques - A running case study to help students apply their knowledge in practice. - Real-life examples demonstrating the use of business analytics for tasks such as customer churn prediction, credit card fraud detection, and sales forecasting. - Practical exercises and activities, learning objectives, and chapter summaries to support learning.