Data Analytics with Google Cloud Platform

Data Analytics with Google Cloud Platform

Author: Murari Ramuka

Publisher: BPB Publications

Published: 2019-12-16

Total Pages: 282

ISBN-13: 9389423643

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Step-by-step guide to different data movement and processing techniques, using Google Cloud Platform Services Key Featuresa- Learn the basic concept of Cloud Computing along with different Cloud service provides with their supported Models (IaaS/PaaS/SaaS)a- Learn the basics of Compute Engine, App Engine, Container Engine, Project and Billing setup in the Google Cloud Platforma- Learn how and when to use Cloud DataFlow, Cloud DataProc and Cloud DataPrep a- Build real-time data pipeline to support real-time analytics using Pub/Sub messaging servicea- Setting up a fully managed GCP Big Data Cluster using Cloud DataProc for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient mannera- Learn how to use Cloud Data Studio for visualizing the data on top of Big Querya- Implement and understand real-world business scenarios for Machine Learning, Data Pipeline EngineeringDescriptionModern businesses are awash with data, making data driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with enough knowledge of Cloud Computing in conjunction with Google Cloud Data platform to succeed in the role of a Cloud data expert.Current market is trending towards the latest cloud technologies, which is the need of the hour. Google being the pioneer, is dominating this space with the right set of cloud services being offered as part of GCP (Google Cloud Platform). At this juncture, this book will be very vital and will be cover all the services that are being offered by GCP, putting emphasis on Data services.What will you learnBy the end of the book, you will have come across different data services and platforms offered by Google Cloud, and how those services/features can be enabled to serve business needs. You will also see a few case studies to put your knowledge to practice and solve business problems such as building a real-time streaming pipeline engine, Scalable Datawarehouse on Cloud, fully managed Hadoop cluster on Cloud and enabling TensorFlow/Machine Learning API's to support real-life business problems. Remember to practice additional examples to master these techniques. Who this book is forThis book is for professionals as well as graduates who want to build a career in Google Cloud data analytics technologies. One stop shop for those who wish to get an initial to advance understanding of the GCP data platform. The target audience will be data engineers/professionals who are new, as well as those who are acquainted with the tools and techniques related to cloud and data space. a- Individuals who have basic data understanding (i.e. Data and cloud) and have done some work in the field of data analytics, can refer/use this book to master their knowledge/understanding.a- The highlight of this book is that it will start with the basic cloud computing fundamentals and will move on to cover the advance concepts on GCP cloud data analytics and hence can be referred across multiple different levels of audiences. Table of Contents1. GCP Overview and Architecture2. Data Storage in GCP 3. Data Processing in GCP with Pub/Sub and Dataflow 4. Data Processing in GCP with DataPrep and Dataflow5. Big Query and Data Studio6. Machine Learning with GCP7. Sample Use cases and ExamplesAbout the Author Murari Ramuka is a seasoned Data Analytics professional with 12+ years of experience in enabling data analytics platforms using traditional DW/BI and Cloud Technologies (Azure, Google Cloud Platform) to uncover hidden insights and maximize revenue, profitability and ensure efficient operations management. He has worked with several multinational IT giants like Capgemini, Cognizant, Syntel and Icertis.His LinkedIn Profile: https://www.linkedin.com/in/murari-ramuka-98a440a/


Hands On With Google Data Studio

Hands On With Google Data Studio

Author: Lee Hurst

Publisher: John Wiley & Sons

Published: 2020-01-09

Total Pages: 436

ISBN-13: 1119616182

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Learn how to easily transform your data into engaging, interactive visual reports! Data is no longer the sole domain of tech professionals and scientists. Whether in our personal, business, or community lives, data is rapidly increasing in both importance and sheer volume. The ability to visualize all kinds of data is now within reach for anyone with a computer and an internet connection. Google Data Studio, quickly becoming the most popular free tool in data visualization, offers users a flexible, powerful way to transform private and public data into interactive knowledge that can be easily shared and understood. Hands On With Google Data Studio teaches you how to visualize your data today and produce professional quality results quickly and easily. No previous experience is required to get started right away—all you need is this guide, a Gmail account, and a little curiosity to access and visualize data just like large businesses and organizations. Clear, step-by-step instructions help you identify business trends, turn budget data into a report, assess how your websites or business listings are performing, analyze public data, and much more. Practical examples and expert tips are found throughout the text to help you fully understand and apply your new knowledge to a wide array of real-world scenarios. This engaging, reader-friendly guide will enable you to: Use Google Data Studio to access various types of data, from your own personal data to public sources Build your first data set, navigate the Data Studio interface, customize reports, and share your work Learn the fundamentals of data visualization, personal data accessibility, and open data API's Harness the power of publicly accessible data services including Google’s recently released Data Set Search Add banners, logos, custom graphics, and color palettes Hands On With Google Data Studio: A Data Citizens Survival Guide is a must-have resource for anyone starting their data visualization journey, from individuals, consultants, and small business owners to large business and organization managers and leaders.


Social Data Analytics

Social Data Analytics

Author: Amin Beheshti

Publisher: CRC Press

Published: 2022-08-01

Total Pages: 251

ISBN-13: 100064460X

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This book is an introduction to social data analytics along with its challenges and opportunities in the age of Big Data and Artificial Intelligence. It focuses primarily on concepts, techniques and methods for organizing, curating, processing, analyzing, and visualizing big social data: from text to image and video analytics. It provides novel techniques in storytelling with social data to facilitate the knowledge and fact discovery. The book covers a large body of knowledge that will help practitioners and researchers in understanding the underlying concepts, problems, methods, tools and techniques involved in modern social data analytics. It also provides real-world applications of social data analytics, including: Sales and Marketing, Influence Maximization, Situational Awareness, customer success and Segmentation, and performance analysis of the industry. It provides a deep knowledge in social data analytics by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in data science to graduate courses in data analytics.


Data Science Quick Reference Manual Analysis and Visualization

Data Science Quick Reference Manual Analysis and Visualization

Author: Mario A. B. Capurso

Publisher: Mario A.B. Capurso

Published:

Total Pages: 221

ISBN-13:

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This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Second of a series of books, it covers methodological aspects, analysis and visualization. It describes the CRISP DM methodology, the working phases, the success criteria, the languages and the environments that can be used, the application libraries. Since this book uses Orange for the application aspects, its installation and widgets are described. In visualization, historical notes are made, and next the book describes the characteristics of an effective visualization, the types of messages that can be conveyed, the Grammar of Graphics, the use of a graph and a dashboard, the software and libraries that can be used, the role and use of color. 55 types of graphs are then analyzed, reporting meaning, use, examples and visual dimensions also with a vocabulary of graphs and summary tables. Examples are given in Orange and the possible use of Python with Orange is explained. Visualization-based inference is discussed, exploratory and confirmatory analysis is defined and techniques are reported. The book is accompanied by supporting material and it is possible to download the project samples in Orange and sample data.


Big Data Analytics: From Data to Discovery

Big Data Analytics: From Data to Discovery

Author: Dr. Sudhakar.K

Publisher: Leilani Katie Publication

Published: 2024-06-12

Total Pages: 207

ISBN-13: 9363486583

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Dr. Sudhakar.K, Associate Professor, Department of Artificial Intelligence & Data Science, NITTE Meenakshi Institute of Technology, Bangalore, Karnataka, India. Mrs.Noor Sumaiya, Assistant Professor, Department of Computer Science Engineering, The Oxford College of Engineering, Bangalore, Karnataka, India. Mrs.Niveditha.S, Assistant Professor, Department of Information Science & Engineering, Don Bosco Institute of Technology, Bangalore, Karnataka, India. Mr.Debarshi Mazumder, Assistant Professor, Department of Artificial Intelligence & Data Science, NITTE Meenakshi Institute of Technology, Bangalore, Karnataka, India.


Author:

Publisher: Smocot Ionut Mihai

Published:

Total Pages: 81

ISBN-13:

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Decoding Data: A Comprehensive Guide to Data Analysis

Decoding Data: A Comprehensive Guide to Data Analysis

Author: Md Fardaous Ahmed

Publisher: Ocleno

Published: 2024-06-06

Total Pages: 19

ISBN-13:

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Unlock the power of data with "Decoding Data," the ultimate guide for anyone looking to master the art and science of data analysis. Whether you're a seasoned analyst, a business professional, or a curious newcomer, this book will equip you with the skills and knowledge needed to navigate the vast landscape of data in today's digital age. Explore the essentials of data collection and management, dive deep into data cleaning and preprocessing techniques, and discover the transformative potential of exploratory data analysis. From the fundamentals of statistical analysis and hypothesis testing to the cutting-edge applications of machine learning, "Decoding Data" covers it all. Learn advanced data analysis techniques, including time series analysis, text mining, and big data analytics. Understand how to interpret and communicate your findings effectively, ensuring that your data-driven insights lead to impactful decision-making. Each chapter is packed with practical examples, real-world case studies, and hands-on tools to guide you every step of the way. "Decoding Data" is more than just a textbook—it's a comprehensive resource designed to make data analysis accessible, engaging, and above all, actionable. Transform your approach to data and unlock new opportunities for innovation and growth with this indispensable guide. Join the ranks of data-savvy professionals and let "Decoding Data" be your companion on the journey to mastering data analysis. Your future in data starts here.


From Data to Discovery: The Essential Guide to Big Data Analytics

From Data to Discovery: The Essential Guide to Big Data Analytics

Author: Dr.J.Premalatha

Publisher: SK Research Group of Companies

Published: 2024-02-27

Total Pages: 261

ISBN-13: 8119980808

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Dr.J.Premalatha, Vice Principal, Dhanalakshmi Srinivasan Arts and Science(Co-Ed) College, Mamallapuram, Chennai, Tamil Nadu, India. Dr.K.Kalaiselvi, Professor, Department of Data Analytics, Saveetha College of Liberal Arts and Sciences, SIMATS, Chennai, Tamil Nadu, India. Dr.A.Senthilkumar, Assistant Professor, Department of Computer Science with Data Analytics, Sri Ramakrishna College of Arts & Science, Coimbatore, Tamil Nadu, India.


Data-Driven Modelling and Predictive Analytics in Business and Finance

Data-Driven Modelling and Predictive Analytics in Business and Finance

Author: Alex Khang

Publisher: CRC Press

Published: 2024-07-24

Total Pages: 443

ISBN-13: 1040088465

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Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent. Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visualization tools AI-aided applications Cybersecurity techniques Cloud computing IoT-enabled systems for developing smart financial systems This book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.


Applications of Synthetic High Dimensional Data

Applications of Synthetic High Dimensional Data

Author: Sobczak-Michalowska, Marzena

Publisher: IGI Global

Published: 2024-03-25

Total Pages: 315

ISBN-13:

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The need for tailored data for machine learning models is often unsatisfied, as it is considered too much of a risk in the real-world context. Synthetic data, an algorithmically birthed counterpart to operational data, is the linchpin for overcoming constraints associated with sensitive or regulated information. In high-dimensional data, where the dimensions of features and variables often surpass the number of available observations, the emergence of synthetic data heralds a transformation. Applications of Synthetic High Dimensional Data delves into the algorithms and applications underpinning the creation of synthetic data, which surpass the capabilities of authentic datasets in many cases. Beyond mere mimicry, synthetic data takes center stage in prioritizing the mathematical domain, becoming the crucible for training robust machine learning models. It serves not only as a simulation but also as a theoretical entity, permitting the consideration of unforeseen variables and facilitating fundamental problem-solving. This book navigates the multifaceted advantages of synthetic data, illuminating its role in protecting the privacy and confidentiality of authentic data. It also underscores the controlled generation of synthetic data as a mechanism to safeguard private information while maintaining a controlled resemblance to real-world datasets. This controlled generation ensures the preservation of privacy and facilitates learning across datasets, which is crucial when dealing with incomplete, scarce, or biased data. Ideal for researchers, professors, practitioners, faculty members, students, and online readers, this book transcends theoretical discourse.