Big Data Bootcamp Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Big Data Bootcamp book. This book definitely worth reading, it is an incredibly well-written.
Investors and technology gurus have called big data one of the most important trends to come along in decades. Big Data Bootcamp explains what big data is and how you can use it in your company to become one of tomorrow’s market leaders. Along the way, it explains the very latest technologies, companies, and advancements. Big data holds the keys to delivering better customer service, offering more attractive products, and unlocking innovation. That’s why, to remain competitive, every organization should become a big data company. It’s also why every manager and technology professional should become knowledgeable about big data and how it is transforming not just their own industries but the global economy. And that knowledge is just what this book delivers. It explains components of big data like Hadoop and NoSQL databases; how big data is compiled, queried, and analyzed; how to create a big data application; and the business sectors ripe for big data-inspired products and services like retail, healthcare, finance, and education. Best of all, your guide is David Feinleib, renowned entrepreneur, venture capitalist, and author of Why Startups Fail. Feinleib’s Big Data Landscape, a market map featured and explained in the book, is an industry benchmark that has been viewed more than 150,000 times and is used as a reference by VMWare, Dell, Intel, the U.S. Government Accountability Office, and many other organizations. Feinleib also explains: • Why every businessperson needs to understand the fundamentals of big data or get run over by those who do • How big data differs from traditional database management systems • How to create and run a big data project • The technical details powering the big data revolution Whether you’re a Fortune 500 executive or the proprietor of a restaurant or web design studio, Big Data Bootcamp will explain how you can take full advantage of new technologies to transform your company and your career.
Investors and technology gurus have called big data one of the most important trends to come along in decades. Big Data Bootcamp explains what big data is and how you can use it in your company to become one of tomorrow’s market leaders. Along the way, it explains the very latest technologies, companies, and advancements. Big data holds the keys to delivering better customer service, offering more attractive products, and unlocking innovation. That’s why, to remain competitive, every organization should become a big data company. It’s also why every manager and technology professional should become knowledgeable about big data and how it is transforming not just their own industries but the global economy. And that knowledge is just what this book delivers. It explains components of big data like Hadoop and NoSQL databases; how big data is compiled, queried, and analyzed; how to create a big data application; and the business sectors ripe for big data-inspired products and services like retail, healthcare, finance, and education. Best of all, your guide is David Feinleib, renowned entrepreneur, venture capitalist, and author of Why Startups Fail. Feinleib’s Big Data Landscape, a market map featured and explained in the book, is an industry benchmark that has been viewed more than 150,000 times and is used as a reference by VMWare, Dell, Intel, the U.S. Government Accountability Office, and many other organizations. Feinleib also explains: • Why every businessperson needs to understand the fundamentals of big data or get run over by those who do • How big data differs from traditional database management systems • How to create and run a big data project • The technical details powering the big data revolution Whether you’re a Fortune 500 executive or the proprietor of a restaurant or web design studio, Big Data Bootcamp will explain how you can take full advantage of new technologies to transform your company and your career.
Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution
Big Data, Big Challenges: A Healthcare Perspective by Mowafa Househ,Andre W. Kushniruk,Elizabeth M. Borycki Pdf
This is the first book to offer a comprehensive yet concise overview of the challenges and opportunities presented by the use of big data in healthcare. The respective chapters address a range of aspects: from health management to patient safety; from the human factor perspective to ethical and economic considerations, and many more. By providing a historical background on the use of big data, and critically analyzing current approaches together with issues and challenges related to their applications, the book not only sheds light on the problems entailed by big data, but also paves the way for possible solutions and future research directions. Accordingly, it offers an insightful reference guide for health information technology professionals, healthcare managers, healthcare practitioners, and patients alike, aiding them in their decision-making processes; and for students and researchers whose work involves data science-related research issues in healthcare.
Research Anthology on Big Data Analytics, Architectures, and Applications by Management Association, Information Resources Pdf
Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians.
Embark on Your Data Science Journey! "Data Science Bootcamp: From Zero to Hero in Data Science" offers a comprehensive pathway for those aspiring to become expert data scientists. This meticulously crafted book serves as a rigorous bootcamp, providing learners of all levels the capacities to dive deep into the vast ocean of data science. Whether you are a beginner with a curiosity in data or an intermediate practitioner aiming to solidify your expertise, this book caters to your ambition with precision and depth. The book unfolds the mysteries of data science across 12 chapters, encompassing crucial topics from introductory concepts to advanced data manipulation and analysis techniques. Alongside theoretical insights, you'll engage with practical exercises, real-world case studies, and emerging trends in data science, equipping you with the holistic understanding needed to thrive in this dynamic field. By weaving together the fundamentals with cutting-edge methodologies, "Data Science Bootcamp" ensures your learning journey is both enlightening and actionable. It bridges the gap between academic concepts and their real-world applications, facilitating a smooth transition from learning to implementing. Discover the transformative power of data analysis, machine learning algorithms, and predictive modeling in shaping industries and driving innovation. Don't miss out on this unique opportunity to elevate your data science prowess. Embrace the challenge, harness the power of data, and embark on a rewarding career as a data scientist. With "Data Science Bootcamp," the road from beginner to hero in data science is engaging, accessible, and filled with invaluable insights. Make this pivotal leap today. Your journey through data science starts here! Table of Contents 1. Introduction to Data Science - The Essence of Data Science - Skills Needed for a Data Scientist - Understanding Data and Its Power 2. Data Wrangling and Cleaning - Fundamentals of Data Wrangling - Cleaning Data: Techniques and Importance - Practical Exercises in Data Cleaning 3. Exploratory Data Analysis - Introduction to EDA - Visualizing Data - Finding Patterns in Data 4. Statistical Foundations - Basic Statistical Concepts - Applying Statistics in Data Science - Statistical Tests and Their Importance 5. Machine Learning Basics - Understanding Machine Learning - Supervised vs. Unsupervised Learning - Building Your First Machine Learning Model 6. Advanced Machine Learning - Fine-Tuning ml Models - Dealing with Overfitting and Underfitting - Introduction to Deep Learning 7. Data Visualization - The Power of Data Visualization - Tools for Visualizing Data - Creating Engaging Visuals 8. Big Data and Its Applications - Understanding Big Data - Big Data Technologies - Applications of Big Data in Various Industries 9. Predictive Modeling - Introduction to Predictive Modeling - Building Predictive Models - Real-World Applications of Predictive Modeling 10. Natural Language Processing - Basics of NLP - Implementing NLP in data Science Projects - Advanced NLP Techniques 11. Ethical Considerations in Data Science - The Importance of Ethics - Data Privacy and Security - Fairness and Bias in Machine Learning 12. Career Path and Next Steps - Building a Portfolio - Preparing for Data Science Interviews - Continuous Learning and Growth in Data Science
Service Desk Manager Bootcamp by Rob Botwright Pdf
🚀 Welcome to the Service Desk Manager Bootcamp! 🚀 Are you ready to take your service desk management skills to the next level? Introducing our exclusive book bundle: "Service Desk Manager Bootcamp: ITIL 4 Standards, KPI & SLA Management." 📚 In this comprehensive bundle, you'll gain access to four essential books that cover everything you need to know to excel in service desk management: 📘 BOOK 1: Service Desk Essentials: A Beginner's Guide to ITIL 4 Standard Get started on the right foot with this beginner's guide to ITIL 4 Standard. Learn the core principles and practices of IT service management and align IT services with business objectives. 📘 BOOK 2: Mastering KPIs: Optimizing Service Desk Performance Unlock the secrets to optimizing service desk performance with this book. Discover how to identify, define, and track key performance indicators (KPIs) to enhance customer satisfaction and drive organizational success. 📘 BOOK 3: SLA Mastery: Advanced Strategies for Service Desk Managers Take your SLA management skills to the next level with advanced strategies and techniques. Learn how to negotiate, implement, and manage SLAs to ensure compliance and alignment with business objectives. 📘 BOOK 4: Beyond Basics: Expert Insights into Service Desk Management in the Digital Age Stay ahead of the curve with expert insights into service desk management in the digital age. Explore innovative approaches, emerging technologies, and best practices for delivering exceptional customer experiences. With over 3000 characters of expert guidance and practical strategies, this book bundle is your ticket to service desk excellence. Whether you're a beginner looking to build a strong foundation or an experienced manager seeking advanced techniques, the Service Desk Manager Bootcamp has something for everyone. Don't miss out on this opportunity to elevate your service desk management skills and drive organizational success. Get your hands on the Service Desk Manager Bootcamp book bundle today! 🌟
Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications by Tran Khanh Dang,Josef Küng,Makoto Takizawa,Tai M. Chung Pdf
This book constitutes the proceedings of the 7th International Conference on Future Data and Security Engineering, FDSE 2020, held in Quy Nhon, Vietnam, in November 2020.* The 29 full papers and 8 short were carefully reviewed and selected from 161 submissions. The selected papers are organized into the following topical headings: big data analytics and distributed systems; security and privacy engineering; industry 4.0 and smart city: data analytics and security; data analytics and healthcare systems; machine learning-based big data processing; emerging data management systems and applications; and short papers: security and data engineering. * The conference was held virtually due to the COVID-19 pandemic.
Embark on a transformative journey into the expansive world of "DATA SCIENCE" with our definitive MCQ guide, "DataWise." Tailored for data enthusiasts, students, and professionals delving into the dynamic field of data science, this resource is your key to unraveling the intricacies of statistical analysis, machine learning, and the transformative potential of data-driven insights. Dive into a knowledge-rich experience, progressing from foundational to advanced concepts through a series of thoughtfully curated multiple-choice questions. Key Features: MCQ Exploration: Navigate through a diverse array of questions covering fundamental principles, data science methodologies, and the unique characteristics of statistical analysis and machine learning, ensuring a comprehensive understanding of this transformative field. Detailed Explanations: Elevate your knowledge with comprehensive explanations accompanying each MCQ, unraveling the intricacies of data preprocessing, model selection, and the principles that define the power of data science. Real-World Applications: Bridge theory and practice, connecting data science concepts to real-world applications in predictive analytics, business intelligence, and solving complex problems across various industries. Progressive Difficulty Levels: Challenge yourself with questions ranging from foundational to advanced, providing a structured learning experience suitable for learners at all levels. Visual Learning Tools: Reinforce your understanding with visual aids such as data science diagrams, machine learning workflows, and statistical analysis illustrations, enhancing your grasp of data science concepts. Embark on a quest for data mastery with "DataWise: DATA SCIENCE." Download your copy now to master the essential skills needed for understanding the transformative potential of data science. Whether you're a student, data enthusiast, or a professional in the field, this guide is your key to unlocking the capabilities of data-driven insights with precision and expertise.
National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Division on Engineering and Physical Sciences,Board on Science Education,Computer Science and Telecommunications Board,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics
Author : National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Division on Engineering and Physical Sciences,Board on Science Education,Computer Science and Telecommunications Board,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics Publisher : National Academies Press Page : 223 pages File Size : 55,9 Mb Release : 2020-09-02 Category : Education ISBN : 9780309677738
Roundtable on Data Science Postsecondary Education by National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Division on Engineering and Physical Sciences,Board on Science Education,Computer Science and Telecommunications Board,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics Pdf
Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.
Data Science and Predictive Analytics by Ivo D. Dinov Pdf
Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder’s law > Moore’s law). Second, traditional discipline-bounds inhibit expeditious progress. Third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. There are very few rigorous instructional resources, interactive learning materials, and dynamic training environments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pillars for the urgently needed bridge to close that supply and demand predictive analytic skills gap. Exposing the enormous opportunities presented by the tsunami of Big data, this textbook aims to identify specific knowledge gaps, educational barriers, and workforce readiness deficiencies. Specifically, it focuses on the development of a transdisciplinary curriculum integrating modern computational methods, advanced data science techniques, innovative biomedical applications, and impactful health analytics. The content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. Big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies. The two examples below demonstrate the powerful need for scientific knowledge, computational abilities, interdisciplinary expertise, and modern technologies necessary to achieve desired outcomes (improving human health and optimizing future return on investment). This can only be achieved by appropriately trained teams of researchers who can develop robust decision support systems using modern techniques and effective end-to-end protocols, like the ones described in this textbook. • A geriatric neurologist is examining a patient complaining of gait imbalance and posture instability. To determine if the patient may suffer from Parkinson’s disease, the physician acquires clinical, cognitive, phenotypic, imaging, and genetics data (Big Data). Most clinics and healthcare centers are not equipped with skilled data analytic teams that can wrangle, harmonize and interpret such complex datasets. A learner that completes a course of study using this textbook will have the competency and ability to manage the data, generate a protocol for deriving biomarkers, and provide an actionable decision support system. The results of this protocol will help the physician understand the entire patient dataset and assist in making a holistic evidence-based, data-driven, clinical diagnosis. • To improve the return on investment for their shareholders, a healthcare manufacturer needs to forecast the demand for their product subject to environmental, demographic, economic, and bio-social sentiment data (Big Data). The organization’s data-analytics team is tasked with developing a protocol that identifies, aggregates, harmonizes, models and analyzes these heterogeneous data elements to generate a trend forecast. This system needs to provide an automated, adaptive, scalable, and reliable prediction of the optimal investment, e.g., R&D allocation, that maximizes the company’s bottom line. A reader that complete a course of study using this textbook will be able to ingest the observed structured and unstructured data, mathematically represent the data as a computable object, apply appropriate model-based and model-free prediction techniques. The results of these techniques may be used to forecast the expected relation between the company’s investment, product supply, general demand of healthcare (providers and patients), and estimate the return on initial investments.
Connie W. Delaney,Charlotte A. Weaver,Judith J. Warren,Thomas R. Clancy,Roy L. Simpson
Author : Connie W. Delaney,Charlotte A. Weaver,Judith J. Warren,Thomas R. Clancy,Roy L. Simpson Publisher : Springer Page : 488 pages File Size : 40,5 Mb Release : 2017-11-02 Category : Medical ISBN : 9783319533001
Big Data-Enabled Nursing by Connie W. Delaney,Charlotte A. Weaver,Judith J. Warren,Thomas R. Clancy,Roy L. Simpson Pdf
Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. Historically, large data methods were limited to traditional biostatical analyses. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Health systems electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. This text reflects how the learning health system infrastructure is maturing, and being advanced by health information exchanges (HIEs) with multiple organizations blending their data, or enabling distributed computing. It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery. New opportunities for nursing and call for new skills in research methodologies are being further enabled by new partnerships spanning all sectors.
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
How to Make Your PhD Work by Thomas R. Coughlin Pdf
How to Make Your PhD Work A modern guide for a challenging modern PhD market The job market for PhDs in science and engineering has become immensely more challenging in the last decade. As of 2022, less than 5% of PhDs attain permanent academic positions, yet books about navigating PhD programs continue to treat permanent academic employment as the assumed norm. Today’s PhDs need tools not only for completing their programs successfully, but for positioning themselves in a varied and competitive job market. How to Make Your PhD Work meets this need, with concrete, empowering advice that takes account of modern job market challenges and opportunities. It cuts through widespread misconceptions about STEM careers and funding, offers tips for navigating difficult degree programs, and supplies current or prospective PhDs with the tools to radically transform their post-degree career prospects. How to Make Your PhD Work readers will also find: Detailed discussion of topics including postdoctoral fellowships, nonacademic careers, success in industry, and more Twelve stories from PhD students who talk about their relationship with their advisor, their success with their project, and their transition into their careers Worksheets and case studies designed to help PhDs map out potential career paths An author with extensive experience of the nonacademic job market and a real understanding of the challenges STEM PhDs face How to Make Your PhD Work is ideal for any STEM PhD student, prospective student, or early career researcher looking to improve their positions in the job market.
Crest the data wave with a deep cultural shift Winning with Data explores the cultural changes big data brings to business, and shows you how to adapt your organization to leverage data to maximum effect. Authors Tomasz Tunguz and Frank Bien draw on extensive background in big data, business intelligence, and business strategy to provide a blueprint for companies looking to move head-on into the data wave. Instrumentation is discussed in detail, but the core of the change is in the culture—this book provides sound guidance on building the type of organizational culture that creates and leverages data daily, in every aspect of the business. Real-world examples illustrate these important concepts at work: you'll learn how data helped Warby-Parker disrupt a $13 billion monopolized market, how ThredUp uses data to process more than 20 thousand items of clothing every day, how Venmo leverages data to build better products, how HubSpot empowers their salespeople to be more productive, and more. From decision making and strategy to shipping and sales, this book shows you how data makes better business. Big data has taken on buzzword status, but there is little real guidance for companies seeking everyday business data solutions. This book takes a deeper look at big data in business, and shows you how to shift internal culture ahead of the curve. Understand the changes a data culture brings to companies Instrument your company for maximum benefit Utilize data to optimize every aspect of your business Improve decision making and transform business strategy Big data is becoming the number-one topic in business, yet no one is asking the right questions. Leveraging the full power of data requires more than good IT—organization-wide buy-in is essential for long-term success. Winning with Data is the expert guide to making data work for your business, and your needs.