Data Science for IoT Engineers. Master Data Science Techniques and Machine Learning Applications for Innovative IoT Solutions

Mercury Learning and Information, P. G. Madhavan

This book introduces data science to professionals in engineering, physics, mathematics, and related fields. It serves as a workbook with MATLAB code, linking subject knowledge to data science, machine learning, and analytics, with applications in IoT. Part One integrates machine learning, systems theory, linear algebra, digital signal processing, and probability theory. Part Two develops a nonlinear, time-varying machine learning solution for modeling real-life business problems.Understanding data science is crucial for modern applications, particularly in IoT. This book presents a dynamic machine learning solution to handle these complexities. Topics include machine learning, systems theory, linear algebra, digital signal processing, probability theory, state-space formulation, Bayesian estimation, Kalman filter, causality, and digital twins.The journey begins with data science and machine learning, covering systems theory and linear algebra. Advanced concepts like the Kalman filter and Bayesian estimation lead to developing a dynamic machine learning model. The book ends with practical applications using digital twins.

Data Science Fundamentals Pocket Primer. An Essential Guide to Data Science Concepts and Techniques

Mercury Learning and Information, Oswald Campesato

This book, part of the Pocket Primer series, introduces the basic concepts of data science using Python 3 and other applications. It offers a fast-paced introduction to data analytics, statistics, data visualization, linear algebra, and regular expressions. The book features numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available.Understanding data science is crucial in today's data-driven world. This book provides a comprehensive introduction, covering key areas such as Python 3, data visualization, and statistical concepts. The practical code samples and hands-on approach make it ideal for beginners and those looking to enhance their skills.The journey begins with working with data, followed by an introduction to probability, statistics, and linear algebra. It then delves into Python, NumPy, Pandas, R, regular expressions, and SQL/NoSQL, concluding with data visualization techniques. This structured approach ensures a solid foundation in data science.

Data Science Tools. Comprehensive Guide to Mastering Fundamental Data Science and Statistics Techniques

Mercury Learning and Information, Christopher Greco

This book introduces popular data science tools and guides readers on how to use them effectively. It covers data analysis using Microsoft Excel, KNIME, R, and OpenOffice, applying statistical concepts such as confidence intervals, normal distribution, T-Tests, linear regression, histograms, and geographic analysis with real data from Federal Government sources.The course begins with the basics, including importing data and conducting various statistical tests. It progresses to specific methods for each tool, ensuring a comprehensive understanding of data analysis. Capstone exercises provide hands-on experience, reinforcing the concepts learned throughout the book.Understanding these tools and concepts is crucial for effective data analysis. This book takes readers from the basics to advanced statistical methods, combining theoretical insights with practical applications. Companion files with source code and data sets enhance the learning experience, making this book an essential resource for mastering data analysis with popular software applications.

Data Structures and Program Design Using C++. A Self-Teaching Introduction to Data Structures and C++

Mercury Learning and Information, D. Malhotra, N. Malhotra

This book introduces the fundamentals of data structures using C++ in a self-teaching format. It covers managing large amounts of information, SEO, and creating Internet/Web indexing services. Practical analogies with real-world applications help explain technical concepts. The book includes end-of-chapter exercises such as programming tasks, theoretical questions, and multiple-choice quizzes.The course starts with an introduction to data structures and the C++ language, progressing through arrays, linked lists, queues, searching and sorting, stacks, trees, multi-way search trees, hashing, files, and graphs. Each chapter builds on the previous one, ensuring a comprehensive understanding of data structures.Understanding these concepts is crucial for managing large databases and optimizing web services. This book guides readers from basic to advanced data structure techniques, blending theoretical knowledge with practical skills. Companion files with source code and data sets enhance the learning experience, making this book an essential resource for mastering data structures with C++.

Data Structures and Program Design Using Java. A Self-Teaching Introduction to Data Structures and Java

Mercury Learning and Information, D. Malhotra, N. Malhotra

This book introduces the fundamentals of data structures using Java in a self-teaching format. It covers managing large databases, effective SEO, and creating web indexing services. Real-world analogies help explain technical concepts. Each chapter includes programming tasks, theoretical questions, and multiple-choice quizzes.The course begins with an introduction to data structures and Java, moving through arrays, linked lists, queues, searching and sorting, stacks, trees, multi-way search trees, hashing, files, and graphs. Each chapter builds on the previous one, ensuring a thorough understanding of data structures.Understanding these concepts is crucial for managing information and optimizing web services. This book guides readers from basic to advanced techniques, blending theory with practical skills. It is an essential resource for mastering data structures with Java, enhanced by end-of-chapter exercises and real-world examples.

Data Structures and Program Design Using Python. A Self-Teaching Introduction to Data Structures and Python

Mercury Learning and Information, D. Malhotra, N. Malhotra

This book, part of the Pocket Primer series, introduces the basic concepts of data science using Python 3 and other applications. It offers a fast-paced introduction to data analytics, statistics, data visualization, linear algebra, and regular expressions. The book features numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available.Understanding data science is crucial in today's data-driven world. This book provides a comprehensive introduction, covering key areas such as Python 3, data visualization, and statistical concepts. The practical code samples and hands-on approach make it ideal for beginners and those looking to enhance their skills.The journey begins with working with data, followed by an introduction to probability, statistics, and linear algebra. It then delves into Python, NumPy, Pandas, R, regular expressions, and SQL/NoSQL, concluding with data visualization techniques. This structured approach ensures a solid foundation in data science.

Data Visualization for Business Decisions. Transforming Data into Actionable Insights

Mercury Learning and Information, Andres Fortino

This workbook is for business analysts aiming to enhance their skills in creating data visuals, presentations, and report illustrations to support business decisions. It focuses on developing visualization and analytical skills through qualitative labs. Readers will analyze and describe chart improvements instead of directly modifying them. The course covers eighteen elements across six dimensions: Story, Signs, Purpose, Perception, Method, and Charts.The journey starts with labs and a case study, introducing the analysis tool. It then delves into each dimension, guiding readers through exercises to enhance their understanding and skills. A comprehensive RAIKS survey assesses progress before and after using the text. The workbook concludes with a capstone exercise to review and analyze the final results of the two studied charts.These skills are crucial for effective data communication in business. This workbook transitions readers from basic to advanced visualization techniques, blending theoretical insights with practical skills. Companion files with videos, sample files, and slides enhance learning, making this workbook an essential resource for mastering business data visualization.

Data Wrangling Using Pandas, SQL, and Java. A Comprehensive Guide to Data Cleaning and Transformation

Mercury Learning and Information, Oswald Campesato

This book is designed for aspiring data scientists and those involved in data cleaning. It covers features of NumPy and Pandas, along with creating databases and tables in MySQL. It also addresses various data wrangling tasks using Python scripts and awk-based shell scripts. Companion files with code are available from the publisher.Understanding data cleaning and manipulation is vital for data scientists. This book provides a comprehensive introduction to essential tools and techniques. From Python basics to advanced data wrangling, it equips readers with the skills needed to manage and clean data effectively.The journey begins with an introduction to Python and progresses through working with data, Pandas, and SQL. It also covers Java, JSON, XML, and specific data cleaning tasks. The book culminates with detailed data wrangling techniques, ensuring readers gain practical, hands-on experience in data management.