Buyers and technological know-how gurus have named huge knowledge one of the most important trends to come together in a long time. Significant Details Bootcamp explains what huge knowledge is and how you can use it in your corporation to become one of tomorrow’s sector leaders. Alongside the way, it explains the really hottest technologies, firms, and developments.
Significant knowledge holds the keys to providing superior consumer company, providing…
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Author : David Feinleib
Genre : Business & Economics
Summary : 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.
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Author : Jasmine Harper
Genre : Computers
Summary : 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
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Author : Holden Karau
Genre : Computers
Summary : This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.--
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Author : Mowafa Househ
Genre : Medical
Summary : 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.
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Author : Simplilearn
Genre : Computers
Summary : Data analytics is increasingly becoming a key element in shaping a company’s business strategy. Today, data influences every decision made by an organization, and this is driving the wide-scale adoption of data analytics, including machine learning technologies and artificial intelligence solutions. The heightened focus is propelling a surge in data analytics spending, reflected in various studies conducted by leading market research firms. The field of data analytics offers some amazing salaries and is not only the hottest IT job, but it is also one of the best-paying jobs in the world. This guide aims at providing the readers with everything they need to know about the data analytics field, basic terminologies, key concepts, real-life use cases, skills you must master in order to scale up your career, and training and certifications you might need to reach your dream job.
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Author : Nathan George
Genre : Computers
Summary : 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.
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Author : Management Association, Information Resources
Genre : Computers
Summary : 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.