Python For Data Mining Quick Syntax Reference
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Python For Data Mining Quick Syntax Reference
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Author : Valentina Porcu
language : en
Publisher: Apress
Release Date : 2018-12-19
Python For Data Mining Quick Syntax Reference written by Valentina Porcu and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-19 with Computers categories.
Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis. Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning. What You'll Learn Install Python and choose a development environment Understand the basic concepts of object-oriented programming Import, open, and edit files Review the differences between Python 2.x and 3.x Who This Book Is For Programmers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques.
The Python Quick Syntax Reference
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Author : Gregory Walters
language : en
Publisher: Apress
Release Date : 2013-12-17
The Python Quick Syntax Reference written by Gregory Walters and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-17 with Computers categories.
The Python Quick Syntax Reference is the "go to" book that contains an easy to read and use guide to Python programming and development. This condensed code and syntax reference presents the Python language in a well-organized format designed to be used time and again. You won't find jargon, bloated samples, case studies, or history of Hello World and computer theory in this handy reference. This Python syntax reference is packed with useful information and is a must-have for any Python developer. What you’ll learn Variables, strings, lists, dictionaries and conditional statements are and how to use them Some of the standard libraries and what they can do to help you How to write your own functions How to write your first Python program based on terminal Input and Output How to use the Python Interactive Shell How to use classes in your Python programs Who this book is for The Python Quick Syntax Reference is a great pocket reference guide for anyone wanting to program in Python, from the new user to the experienced programmer. Table of Contents Chapter 1: Hello Python Chapter 2: Variables Chapter 3: Operators Chapter 4: Strings Chapter 5: Conditional Statements and Loops Chapter 6: Data Structures Chapter 7: Keywords Chapter 8: Functions Chapter 9: Libraries Chapter 10: Classes
Mathematical Methods For Knowledge Discovery And Data Mining
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Author : Giovanni Felici
language : en
Publisher: IGI Global Snippet
Release Date : 2008
Mathematical Methods For Knowledge Discovery And Data Mining written by Giovanni Felici and has been published by IGI Global Snippet this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Business & Economics categories.
Annotation The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure. Mathematical Methods for Knowledge Discovery & Data Mining focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; and many others. This Premier Reference Source is an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance and insurance, manufacturing, marketing, performance measurement, and telecommunications.
Python Data Mining Quick Start Guide
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Author : Nathan Greeneltch
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-25
Python Data Mining Quick Start Guide written by Nathan Greeneltch 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 2019-04-25 with Computers categories.
Explore the different data mining techniques using the libraries and packages offered by Python Key FeaturesGrasp the basics of data loading, cleaning, analysis, and visualizationUse the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data miningYour one-stop guide to build efficient data mining pipelines without going into too much theoryBook Description Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle. What you will learnExplore the methods for summarizing datasets and visualizing/plotting dataCollect and format data for analytical workAssign data points into groups and visualize clustering patternsLearn how to predict continuous and categorical outputs for dataClean, filter noise from, and reduce the dimensions of dataSerialize a data processing model using scikit-learn’s pipeline featureDeploy the data processing model using Python’s pickle moduleWho this book is for Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.
Julia Quick Syntax Reference
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Author : Antonello Lobianco
language : en
Publisher: Apress
Release Date : 2019-11-11
Julia Quick Syntax Reference written by Antonello Lobianco and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-11 with Computers categories.
This quick Julia programming language guide is a condensed code and syntax reference to the Julia 1.x programming language, updated with the latest features of the Julia APIs, libraries, and packages. It presents the essential Julia syntax in a well-organized format that can be used as a handy reference. This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. You will learn how to use Julia packages for data analysis, numerical optimization and symbolic computation, and how to disseminate your results in dynamic documents or interactive web pages. In this book, the focus is on providing important information as quickly as possible. It is packed with useful information and is a must-have for any Julia programmer. What You Will Learn Set up the software needed to run Julia and your first Hello World example Work with types and the different containers that Julia makes available for rapid application development Use vectorized, classical loop-based code, logical operators, and blocks Explore Julia functions by looking at arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts Build custom structures in Julia Interface Julia with other languages such as C/C++, Python, and R Program a richer API, modifying the code before it is executed using expressions, symbols, macros, quote blocks, and more Maximize your code’s performance Who This Book Is For Experienced programmers new to Julia, as well as existing Julia coders new tothe now stable Julia version 1.0 release.
Data Mining With Python Quick Start Guide
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Author : Freeman Bhekisisa Dlamini
language : en
Publisher:
Release Date : 2021-04-07
Data Mining With Python Quick Start Guide written by Freeman Bhekisisa Dlamini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-07 with categories.
You will learn how to implement a variety of popular data mining algorithms in Python (a programming language - software development environment) to tackle business problems and opportunities.This is the first version of the python book series and 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 Freeman Dlamini, brings both experiences teaching business analytics courses using Python, and expertise in the application of machine learning methods.A new section on ethical issues in data miningMore than a dozen case studies demonstrating applications for the data mining techniques describedEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presentedData 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 book 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
Ultimate Python Libraries For Data Analysis And Visualization
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Author : Abhinaba Banerjee
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-04-04
Ultimate Python Libraries For Data Analysis And Visualization written by Abhinaba Banerjee and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-04 with Computers categories.
Test your Data Analysis skills to its fullest using Python and other no-code tools KEY FEATURES ● Comprehensive coverage of Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, Julius AI for data acquisition, preparation, analysis, and visualization ● Real-world projects and practical applications for hands-on learning ● In-depth exploration of low-code and no-code tools for enhanced productivity DESCRIPTION Ultimate Data Analysis and Visualization with Python is your comprehensive guide to mastering the intricacies of data analysis and visualization using Python. This book serves as your roadmap to unlocking the full potential of Python for extracting insights from data using Pandas, NumPy, Matplotlib, Seaborn, and Julius AI. Starting with the fundamentals of data acquisition, you'll learn essential techniques for gathering and preparing data for analysis. From there, you’ll dive into exploratory data analysis, uncovering patterns and relationships hidden within your datasets. Through step-by-step tutorials, you'll gain proficiency in statistical analysis, time series forecasting, and signal processing, equipping you with the tools to extract actionable insights from any dataset. What sets this book apart is its emphasis on real-world applications. With a series of hands-on projects, you’ll apply your newfound skills to analyze diverse datasets spanning industries such as finance, healthcare, e-commerce, and more. By the end of the book, you'll have the confidence and expertise to tackle any data analysis challenge with Python. To aid your journey, the book includes a handy Python cheat sheet in the appendix, serving as a quick reference guide for common functions and syntax. WHAT WILL YOU LEARN ● Acquire data from various sources using Python, including web scraping, APIs, and databases. ● Clean and prepare datasets for analysis, handling missing values, outliers, and inconsistencies. ● Conduct exploratory data analysis to uncover patterns, trends, and relationships within your data. ● Perform statistical analysis using Python libraries such as NumPy and Pandas, including hypothesis testing and regression analysis. ● Master time series analysis techniques for forecasting future trends and making data-driven decisions. ● Apply signal processing methods to analyze and interpret signals in data, such as audio, image, and sensor data. ● Engage in real-world projects across diverse industries, from finance to healthcare, to reinforce your skills and experience. ● Utilize Python for in-depth analysis of real-world datasets, gaining practical experience and insights. ● Refer to the Python cheat sheet in the appendix for quick access to common functions and syntax, aiding your learning and development. WHO IS THIS BOOK FOR? This book is ideal for beginners, professionals, or students aiming to enhance their careers through hands-on experience in data acquisition, preparation, analysis, time series, and signal processing. Prerequisite knowledge includes basic Python and introductory statistics. Whether starting fresh or seeking to refresh skills, this comprehensive guide helps readers upskill effectively. TABLE OF CONTENTS 1. Introduction to Data Analysis and Data Visualization using Python 2. Data Acquisition 3. Data Cleaning and Preparation 4. Exploratory Data Analysis 5. Statistical Analysis 6. Time Series Analysis and Forecasting 7. Signal Processing 8. Analyzing Real-World Data Sets using Python APPENDIX A Python Cheat Sheet Index
Learn By Examples A Quick Guide To Data Science With Python
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Author : Eric M. H. Goh
language : en
Publisher: SVBook Pte. Ltd.
Release Date :
Learn By Examples A Quick Guide To Data Science With Python written by Eric M. H. Goh and has been published by SVBook Pte. Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
This book aim to equip the reader with Python Programming and Data Science basics. There will be many examples and explanations that are straight to the point. You will be walked through data mining process from data preparation to data analysis (descriptive statistics) and data visualization to prediction modeling (machine learning) and deployment using Python. Content Covered: IntroductionGetting Started (Installing WinPython, IDE, ...)Language Essentials (variables, list, data types manipulations, ...)Language Essentials II (conditional statements, loops, ...)Object Essentials (Modules, Class and Objects, ...)Data Mining with Python (Pandas, ScikitLearn, ...) We will be using opensource tools and IDE, hence, you don't have to worry about buying any softwares. The book is designed for non-programmers only. It will gives you a head start into python programming, with a touch on data mining. This book has been taught at Udemy and EMHAcademy.com. Use the following Coupon to get the Udemy Course at $11.99: https://www.udemy.com/fundamentals-of-python-for-data-mining/?couponCode=EBOOKSPECIAL ISBN: 978-163535299-3
Forthcoming Books
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Author : Rose Arny
language : en
Publisher:
Release Date : 2002
Forthcoming Books written by Rose Arny and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with American literature categories.
Python Data Science
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Author : Christopher Wilkinson
language : en
Publisher:
Release Date : 2019-10-26
Python Data Science written by Christopher Wilkinson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-26 with categories.
An Ultimate Guide to Learn Fundamentals of Python Data Science is full of insights and strategies for data scientists, programming professionals, and students who want to equip themselves with the new trending libraries and functions of Python as a data management tool. This book has all the major techniques of data collection, interpretation and processing to achieve refined information. The reader will learn about the scientific research of data, syntax of Python programming language, and all the basic knowledge of imported libraries and methods.An effective approach of Python data science can save time, resources, and energy. You can learn to help any company with the running processes: accounts, HR modules, sales, services and more. Keeping in view the requirements of brand and competition, this guide for beginners covers all the data management strategies and tactics. The development of the well-structured function of Python is purely a systematic and knowledge-based technique. Building a scientific data research system has never been as easy as it is today. A lot of companies have shifted their data systems to the open-source, easy to learn, Python language. If you really want to learn Python Data Science, don't waste your time looking around - buy this extraordinary book now to get started. It is a detailed book with a comprehensive knowledge of data science, Python data structures, standard libraries, data science frameworks and predictive models in Python. Build your success story through learning the best practices of data science. Click the Buy button to get started.