Python For Big Data And Analytics

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Python For Big Data And Analytics

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Author : THOMPSON. CARTER
language : en
Publisher: Independently Published
Release Date : 2025-01-20

Python For Big Data And Analytics written by THOMPSON. CARTER and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-20 with Computers categories.

Python for Big Data and Analytics: Harness the Power of Python for Processing and Analyzing Large Data Sets Unlock the power of Python for big data with Python for Big Data and Analytics. Designed for data scientists, analysts, and IT professionals, this comprehensive guide takes you step-by-step through the tools, techniques, and best practices for processing and analyzing massive datasets. Whether you're working with structured data, unstructured data, or real-time streams, this book provides the knowledge and practical examples you need to master big data analytics with Python. From foundational concepts to advanced techniques, Python for Big Data and Analytics equips you with the skills to transform raw data into actionable insights. What You'll Learn: Getting Started with Python for Big Data: Understand why Python is the ideal language for big data analytics and set up your development environment. Data Wrangling and Cleaning: Use libraries like pandas and NumPy to clean, transform, and preprocess large datasets efficiently. Big Data Frameworks: Leverage tools like PySpark, Dask, and Ray to process and analyze massive datasets in parallel. Real-Time Data Processing: Work with streaming data using libraries like Apache Kafka and Python-based tools. Exploratory Data Analysis (EDA): Visualize patterns and trends in big datasets with Matplotlib, Seaborn, and Plotly. Machine Learning for Big Data: Build predictive models using scikit-learn, TensorFlow, and PyTorch to analyze and interpret large datasets. Database Integration: Connect Python to big data storage systems like Hadoop, MongoDB, and Amazon Redshift. Cloud Computing for Big Data: Deploy and scale Python-based analytics solutions on AWS, Google Cloud, and Azure. Handling Unstructured Data: Analyze text, images, and other unstructured data formats with libraries like NLTK, spaCy, and OpenCV. Time-Series Analysis: Perform advanced time-series analysis and forecasting on large-scale temporal data. Big Data Visualization: Create interactive dashboards and data visualizations using Bokeh and Dash. Optimizing Performance: Learn best practices for optimizing Python code and reducing memory consumption when working with large datasets. Real-World Applications: Solve real-world problems in industries like healthcare, finance, and e-commerce with practical big data projects. Security and Privacy in Big Data: Understand ethical considerations, data governance, and privacy issues in big data analytics. Who Is This Book For? This book is ideal for data professionals, Python programmers, and anyone looking to develop their expertise in big data analytics and processing. Why Choose This Book? With its focus on practical applications and hands-on projects, Python for Big Data and Analytics bridges the gap between theory and implementation, empowering you to tackle the challenges of big data analytics with confidence. Start harnessing the power of Python for big data today with Python for Big Data and Analytics: Harness the Power of Python for Processing and Analyzing Large Data Sets.

Python Data Analytics

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Author : Stephen Ward
language : en
Publisher:
Release Date : 2020-10-15

Python Data Analytics written by Stephen Ward and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-15 with categories.

Unlock the programming skills you need to prepare for a lucrative career in Data Science with this comprehensive introduction to Python programming for data analytics! Are you completely new to programming and want to learn how to code, but don't know where to begin? Are you looking to upgrade your data wrangling skills to future-proof your career and break into Data Science and Analytics? If you answered yes to any of the questions above, then keep reading... Data analysis has become a huge industry with tons of career potential and will remain relevant far into the foreseeable future. With the exponential growth and explosion of new data and the focus on using data to improve customer experiences and carry out research, data analysts will be needed to process and make sense of large amounts of information, with Python being the language of choice because of its versatility. In this guide, you're going to be shown everything you need to break into the world of Data Analysis with Python. Filled with tutorials for powerful libraries and practical, hands-on exercises, you're going to learn how to aggregate, munge, analyze and visualize data in Python. Here's a sample of what you're going to discover in Python Data Analytics Why Python is the perfect language to learn if you want to break into Big Data and data analytics Core statistical models and computation methods you need to know about as a budding data analyst How to master the CSV library for reading, writing and handling tabular data Using the Xlrd library to extract data from Microsoft Excel files How to convert text to speech using the powerful Win32.com library How to use the NumPy library to carry out fundamental and basic scientific and technical computing How to use the SciPy library to carry out advanced scientific and highly technical computing Surefire ways to manipulate the easy-to-use data structures of the Pandas framework for high-performance data analysis How to plot complex data, create figures and visualize data using the Python Matplotlib library ...and tons more! If you're completely new to programming and have never written a single line of code, but want to get started, this guide is perfect for as a crash guide to getting up to speed with programming in general. Whether you're a programmer looking to switch into an exciting new field with lots of potential for the future, or a regular data analyst looking to acquire the skills needed to remain relevant in a fast-changing world, this guide will teach you how to master powerful libraries used in the real-world by experienced data scientists.

Data Science And Analytics With Python

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Author : Jesus Rogel-Salazar
language : en
Publisher: CRC Press
Release Date : 2018-02-05

Data Science And Analytics With Python written by Jesus Rogel-Salazar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-05 with Computers categories.

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.

Data Science And Big Data Analytics

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Author : Andrew Park
language : en
Publisher: Andrew Park
Release Date : 2021-02-12

Data Science And Big Data Analytics written by Andrew Park and has been published by Andrew Park this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-12 with categories.

★ 55% OFF for Bookstores! NOW at $ 32,97 instead of $ 42.97! LAST DAYS! ★ Your Customers Never Stop to Use this Awesome book! Do you want to know everything about Data science? This guidebook is going to provide you with all of the information that you need to learn more about data science, what this process is all about, and how you can use the Python language to put it all to work for you! Even if you have no idea how to program or any idea of what to do with all of that data you have been collecting, this guidebook will give you all of the tools you need to be successful! There are a lot of different parts that come with data science and being able to put them all together can really help us to do better with helping our customers, finding new products to bring to market, and more. And with the help of this guidebook, we can hopefully find the best ways to beat out the competition and see the results that will work for us. It takes some time, and a good data analysis with the right algorithms from Python, but it can be one of the best ways to make some smart and sound decisions for your business. Working with data science is becoming even more prevalent as the years go on, and businesses all over the world, and in many different industries, are using this to help them see more success. Whether you want to make predictions, provide better customer service, or learn other valuable insights about your business, data science with the help of Python, can make this happen. When you are ready to see what Python data science can do for your business, make sure to check out this guidebook to get started. The process of Python data science is not an easy one and learning how to make this work for your needs, and to put all of the parts together can make a big difference in the way that you run your business, and how much success you will see when it comes to your business growing in the future. When you are ready to learn more about working with Python data science and how to make this work for your business, make sure to check out this guidebook to get started. There are so many parts that come with a data science project, and we are going to take some time to discuss them all in this guidebook. We are going to look at some of the basics that come with this data science project, and why it is so beneficial to so many companies to at least check it out and see what it has to offer them. At the same time, we are also going to explore how to set up your own environment to get started with data science, and some of the best libraries that are out there to help us succeed with the use of data science and Python put together. This book covers: What Is Data Science? How Can I Use Data Science? The Best Python Libraries for Data Science Setting Up Your Virtual Environments for Data Science The Importance of the NumPy Arrays Gathering and Collecting Your Data Loading and Preparing Your Dataset Data Mining Completing the Data Analysis How Machine Learning Can Help How to Work with Data Visualization Many businesses are able to benefit when they work with data analysis for some of their own needs. It will help them to learn more about their customers, their industry, and so much more. When you are ready to learn more about what data science can do for you and to figure out whether this is a process your business should spend some time on, make sure to check out this guidebook to help you get started. Buy it NOW and get addicted to this amazing book

Big Data Analysis With Python

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Author : Ivan Marin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-10

Big Data Analysis With Python written by Ivan Marin 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 2019-04-10 with Computers categories.

Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python. Key FeaturesGet a hands-on, fast-paced introduction to the Python data science stackExplore ways to create useful metrics and statistics from large datasetsCreate detailed analysis reports with real-world dataBook Description Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems. The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools. By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs. What you will learnUse Python to read and transform data into different formatsGenerate basic statistics and metrics using data on diskWork with computing tasks distributed over a clusterConvert data from various sources into storage or querying formatsPrepare data for statistical analysis, visualization, and machine learningPresent data in the form of effective visualsWho this book is for Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.

Python For Data Science

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Author : Jason Callaway
language : en
Publisher:
Release Date : 2020-11-25

Python For Data Science written by Jason Callaway and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Computers categories.

If you are a student or a professional looking for more technical skills, or if you are simply curious about the most up-to-date data analysis techniques and their powerful applications, then this is definitely the book for you. Learning all of the required skills to master data science and machine learning could certainly be challenging, but in this book, Jason Callaway has condensed all of the knowledge you need into a clear and beginner-friendly introduction, with practical examples, detailed explanations, and tips and tricks from his experience. Through his revolutionary and systematic approach, you can learn techniques to manipulate and process datasets, the principles of Python programming, and their real-world applications, regardless of your previous experience. Here's just a tiny fraction of what you will discover: What data science is, and why it has become fundamental in hundreds of business and technological applications The basics of Python programming Essential Python libraries such as NumPy, Pandas, and Matplotlib All of the most effective computational methods for data analysis Data visualization tools and techniques How to build statistical and machine learning models (even if you are brand new to programming) The future of Artificial Intelligence How to build neural networks with Python Step-by-step exercises, practical examples, and tips and tricks Are you ready to develop a successful career in the growing industry of data science?

Essentials Of Big Data Analytics

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Author : Pallavi Chavan
language : en
Publisher: Morgan Kaufmann
Release Date : 2026-01-23

Essentials Of Big Data Analytics written by Pallavi Chavan and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-23 with Computers categories.

Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concepts with hands-on practices using the Python and R programming languages and MapReduce framework. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of big data. Essentials of Big Data Analytics is designed to provide a comprehensive resource for readers looking to deepen their understanding of Big Data analytics, particularly within a computer science, engineering, and data science context. By bridging theoretical concepts with practical applications, the book emphasizes hands-on learning through exercises and tutorials, specifically utilizing R and Python. Given the growing role of Big Data in industry and scientific research, this book serves as a timely resource to equip professionals with the skills needed to thrive in data-driven environments. - Includes hands-on Tutorials and Case Studies: Structured exercises and real-world examples reinforce learning and skill-building - Focuses on Python and R for Big Data: Detailed lessons in Python and R programming cater to the increasing demand for data science expertise - Balanced Theory and Practice: Comprehensive coverage ensures a strong theoretical foundation paired with actionable insights for real-world application

Advanced Data Analytics Using Python

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Author : Sayan Mukhopadhyay
language : en
Publisher: Apress
Release Date : 2018-03-29

Advanced Data Analytics Using Python written by Sayan Mukhopadhyay and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-29 with Computers categories.

Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.

Big Data Analytics And Knowledge Discovery

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Author : Matteo Golfarelli
language : en
Publisher: Springer Nature
Release Date : 2021-09-04

Big Data Analytics And Knowledge Discovery written by Matteo Golfarelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-04 with Computers categories.

This volume LNCS 12925 constitutes the papers of the 23rd International Conference on Big Data Analytics and Knowledge Discovery, held in September 2021. Due to COVID-19 pandemic it was held virtually. The 12 full papers presented together with 15 short papers in this volume were carefully reviewed and selected from a total of 71 submissions. The papers reflect a wide range of topics in the field of data integration, data warehousing, data analytics, and recently big data analytics, in a broad sense. The main objectives of this event are to explore, disseminate, and exchange knowledge in these fields.

Large Scale Data Analytics With Python And Spark

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Author : Isaac Triguero
language : en
Publisher: Cambridge University Press
Release Date : 2023-11-23

Large Scale Data Analytics With Python And Spark written by Isaac Triguero and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-23 with Computers categories.

Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.