Practical Data Science With Hadoop And Spark
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Practical Data Science With Hadoop And Spark
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Author : Ofer Mendelevitch
language : en
Publisher: Addison-Wesley Professional
Release Date : 2016-12-08
Practical Data Science With Hadoop And Spark written by Ofer Mendelevitch 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 2016-12-08 with Computers categories.
The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language
Practical Data Science With Python
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Author : Nathan George
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-09-30
Practical Data Science With Python written by Nathan George 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 2021-09-30 with Computers categories.
Learn to effectively manage data and execute data science projects from start to finish using Python Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source. What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
Practical Data Science With Sap
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Author : Greg Foss
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-09-18
Practical Data Science With Sap written by Greg Foss 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 2019-09-18 with Computers categories.
Learn how to fuse today's data science tools and techniques with your SAP enterprise resource planning (ERP) system. With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data. Data engineers and scientists will explore ways to add SAP data to their analysis processes, while SAP business analysts will learn practical methods for answering questions about the business. By focusing on grounded explanations of both SAP processes and data science tools, this book gives data scientists and business analysts powerful methods for discovering deep data truths. You'll explore: Examples of how data analysis can help you solve several SAP challenges Natural language processing for unlocking the secrets in text Data science techniques for data clustering and segmentation Methods for detecting anomalies in your SAP data Data visualization techniques for making your data come to life
Practical Data Analytics For Bfsi Leveraging Data Science For Driving Decisions In Banking Financial Services And Insurance Operations
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Author : Bharat Sikka
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2023-09-02
Practical Data Analytics For Bfsi Leveraging Data Science For Driving Decisions In Banking Financial Services And Insurance Operations written by Bharat Sikka and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-02 with Computers categories.
Revolutionizing BFSI with Data Analytics Key Features ● Real-world examples and exercises will ground you in the practical application of analytics techniques specific to BFSI. ● Master Python for essential coding, SQL for data manipulation, and industry-leading tools like IBM SPSS and Power BI for sophisticated analyses. ● Understand how data-driven strategies generate profits, mitigate risks, and redefine customer support dynamics within the BFSI sphere. Book Description Are you looking to unlock the transformative potential of data analytics in the dynamic world of Banking, Financial Services, and Insurance (BFSI)? This book is your essential guide to mastering the intricate interplay of data science and analytics that underpins the BFSI landscape. Designed for intermediate-level practitioners, as well as those aspiring to join the ranks of BFSI analytics professionals, this book is your compass in the data-driven realm of banking. Address the unique challenges and opportunities of the BFSI sector using Artificial Intelligence and Machine Learning models for a data driven analysis. What you will learn ● Delve into the world of Data Science, including Artificial Intelligence and Machine Learning, with a focus on their application within BFSI. ● Explore hands-on examples and step-by-step tutorials that provide practical solutions to real-world challenges faced by banking institutions. ● Develop skills in essential programming languages such as Python (fundamentals) and SQL (intermediate), crucial for effective data manipulation and analysis. ● Gain insights into how businesses adapt data-driven strategies to make informed decisions, leading to improved operational efficiency. Who is this book for? This book is tailored for professionals already engaged in or seeking roles within Data Analytics in the BFSI industry. Additionally, it serves as a strategic resource for business leaders and upper management, guiding them in shaping data platforms and products within their organizations. Table of Contents 1. Introduction to BFSI and Data Driven Banking 2. Introduction to Analytics and Data Science 3. Major Areas of Analytics Utilization 4. Understanding Infrastructures behind BFSI for Analytics 5. Data Governance and AI/ML Model Governance in BFSI 6. Domains of BFSI and team planning 7. Customer Demographic Analysis and Customer Segmentation 8. Text Mining and Social Media Analytics 9. Lead Generation Through Analytical Reasoning and Machine Learning 10. Cross Sell and Up Sell of Products through Machine Learning 11. Pricing Optimization 12. Data Envelopment Analysis 13. ATM Cash Forecasting 14. Unstructured Data Analytics 15. Fraud Modelling 16. Detection of Money Laundering and Analysis 17. Credit Risk and Stressed Assets 18. High Performance Architectures: On-Premises and Cloud 19. Growing Trends in the Data-Driven Future of BFSI Index
Practical Data Science
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Author : Andreas François Vermeulen
language : en
Publisher: Apress
Release Date : 2018-02-21
Practical Data Science written by Andreas François Vermeulen and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-21 with Computers categories.
Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling ofpolyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers
Intelligent Analytics With Advanced Multi Industry Applications
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Author : Sun, Zhaohao
language : en
Publisher: IGI Global
Release Date : 2021-01-08
Intelligent Analytics With Advanced Multi Industry Applications written by Sun, Zhaohao and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-08 with Computers categories.
Many fundamental technological and managerial issues surrounding the development and implementation of intelligent analytics within multi-industry applications remain unsolved. There are still questions surrounding the foundation of intelligent analytics, the elements, the big characteristics, and the effects on business, management, technology, and society. Research is devoted to answering these questions and understanding how intelligent analytics can improve healthcare, mobile commerce, web services, cloud services, blockchain, 5G development, digital transformation, and more. Intelligent Analytics With Advanced Multi-Industry Applications is a critical reference source that explores cutting-edge theories, technologies, and methodologies of intelligent analytics with multi-industry applications and emphasizes the integration of artificial intelligence, business intelligence, big data, and analytics from a perspective of computing, service, and management. This book also provides real-world applications of the proposed concept of intelligent analytics to e-SMACS (electronic, social, mobile, analytics, cloud, and service) commerce and services, healthcare, the internet of things, the sharing economy, cloud computing, blockchain, and Industry 4.0. This book is ideal for scientists, engineers, educators, university students, service and management professionals, policymakers, decision makers, practitioners, stakeholders, researchers, and others who have an interest in how intelligent analytics are being implemented and utilized in diverse industries.
Practical Big Data Analytics
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Author : Nataraj Dasgupta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-15
Practical Big Data Analytics written by Nataraj Dasgupta 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 2018-01-15 with Computers categories.
Get command of your organizational Big Data using the power of data science and analytics Key Features A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses Get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big Data Book Description Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization's data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using different tools and methods articulated in this book. What you will learn - Get a 360-degree view into the world of Big Data, data science and machine learning - Broad range of technical and business Big Data analytics topics that caters to the interests of the technical experts as well as corporate IT executives - Get hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, KDB+ and R - Create production-grade machine learning BI Dashboards using R and R Shiny with step-by-step instructions - Learn how to combine open-source Big Data, machine learning and BI Tools to create low-cost business analytics applications - Understand corporate strategies for successful Big Data and data science projects - Go beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologies Who this book is for The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.
Data Analytics Using Machine Learning Techniques On Cloud Platforms
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Author : Seema Rawat
language : en
Publisher: CRC Press
Release Date : 2025-09-23
Data Analytics Using Machine Learning Techniques On Cloud Platforms written by Seema Rawat and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-23 with Computers categories.
Data Analytics using Machine Learning Techniques on Cloud Platforms examines how machine learning (ML) and cloud computing combine to drive data-driven decision-making across industries. Covering ML techniques, loud-based analytics tools and security concerns, this book provides theoretical foundations and real-world applications in fields like healthcare, logistics and e-commerce. It also addresses security challenges, privacy concerns and compliance frameworks, ensuring a comprehensive understanding of cloud-based analytics. This book: Covers supervised and unsupervised learning, including regression, clustering, classification and neural networks Discusses Hadoop, Spark, Tableau, Power BI and Splunk for analytics and visualization Examines how cloud computing enhances scalability, efficiency and automation in data analytics Showcases ML-driven solutions in e-commerce, supply chain logistics, healthcare and education This book is an essential resource for students, researchers and professionals who seek to understand and apply ML-driven cloud analytics in real-world scenarios.
Encyclopedia Of Data Science And Machine Learning
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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2023-01-20
Encyclopedia Of Data Science And Machine Learning written by Wang, John and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Computers categories.
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
The Data Science Framework
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Author : Juan J. Cuadrado-Gallego
language : en
Publisher: Springer Nature
Release Date : 2020-10-01
The Data Science Framework written by Juan J. Cuadrado-Gallego and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-01 with Computers categories.
This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines.