Bash for Data Scientists. A Comprehensive Guide to Shell Scripting for Data Science Tasks

Mercury Learning and Information, Oswald Campesato

This book introduces powerful command line utilities for creating efficient shell scripts to process datasets. Using the bash shell, the examples and scripts focus on small datasets to help readers understand the features of grep, sed, and awk. Companion files with code are available for download from the publisher.The course starts with an introduction to the basics, covering files and directories, and useful commands. It then progresses to conditional logic and loops, providing a solid foundation for processing datasets. Detailed chapters on using grep, sed, and awk illustrate their capabilities in handling and cleaning various types of datasets effectively.Advanced topics include processing datasets with Pandas, exploring NoSQL, SQLite, and Python. The book equips data scientists, analysts, and anyone seeking shell-based solutions with practical skills. By the end, users will be adept at creating robust scripts for dataset processing, combining command line utilities for optimal results.

Dealing With Data Pocket Primer. A Comprehensive Guide to Data Handling Techniques

Mercury Learning and Information, Oswald Campesato

This book introduces the basic concepts of managing data using various computer languages and applications. It is designed as a fast-paced introduction to key features of data management, including statistical concepts, data-related techniques, Pandas, RDBMS, SQL, NLP topics, Matplotlib, and data visualization. The companion files with source code and color figures enhance the learning experience.Understanding these concepts is crucial for anyone looking to manage data effectively. The book covers the fundamentals of probability and statistics, working with data using Pandas, managing databases with SQL and MySQL, and cleaning data using NLP techniques. It also delves into data visualization, providing practical insights and numerous code samples.The journey begins with an introduction to probability and statistics, moving on to working with data and Pandas. It then covers RDBMS and SQL, focusing on practical SQL and MySQL usage. The book concludes with NLP, data cleaning, and visualization techniques, equipping readers with a comprehensive understanding of data management.

Augmented Reality Development with Unity. Build and optimize AR applications with Unity, ARKit, and ARCore

Mercury Learning and Information, Indika Wijesooriya

This book provides a comprehensive guide to developing augmented reality applications. Starting with an introduction to AR concepts, it explores key tools and platforms, emphasizing Unity as a core development tool. Readers will gain hands-on experience creating marker-based and marker-less AR apps using Vuforia, ARKit, and ARCore.The book takes a practical approach, covering the creation of custom components, C# programming for Unity, and building mobile AR apps. Advanced topics include world-scale AR with Niantic Lightship and optimization strategies for AR app performance. Each chapter builds skills progressively, ensuring a thorough understanding of the subject.By the end, readers will have the confidence and skills to design and develop innovative AR applications, understanding the best practices and challenges of AR app development.

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 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.

Pandas Basics. Mastering Data Analysis with Pandas

Mercury Learning and Information, Oswald Campesato

This book is essential for aspiring data scientists and anyone needing to perform data cleaning using Pandas and NumPy. It offers numerous code samples and comprehensive coverage of NumPy and Pandas features, including writing regular expressions. Chapter 3 introduces fundamental statistical concepts, while Chapter 7 delves into data visualization using Matplotlib and Seaborn. Companion files with code are available for download from the publisher.Starting with an introduction to Python, the course progresses through working with data, and then moves into Pandas, covering its functionalities in three detailed chapters. The statistical concepts provided are crucial for analyzing data effectively, while the visualization techniques enhance the ability to present data insights clearly.By the end of this course, users will have a solid foundation in data manipulation and cleaning, statistical analysis, and data visualization, enabling them to tackle real-world data science tasks confidently and efficiently.

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.

Bash Command Line and Shell Scripts Pocket Primer. Mastering Bash Commands and Scripting Techniques

Mercury Learning and Information, Oswald Campesato

As part of the best-selling Pocket Primer series, this book introduces readers to useful command-line utilities for creating powerful shell scripts. It focuses on the “bash” command set, though many concepts apply to other command shells like sh, ksh, zsh, and csh. The book covers piping data between commands and using versatile sed and awk commands. Aimed at beginners, it also serves as a good reference for those with some experience in shell scripting.The journey starts with an introduction to bash, covering files and directories, useful commands, and conditional logic with loops. Readers then learn to filter data with grep, transform data with sed, and work with awk. The book introduces shell scripts, showcasing their use with grep and awk for data manipulation. Various scripts are provided for data scientists and analysts needing shell-based solutions for text file cleaning.Understanding these concepts is crucial for simplifying routine tasks and creating efficient shell scripts. This book transitions readers from novices to proficient scriptwriters, combining theoretical knowledge and practical skills. Companion files with source code examples enhance learning. By the end, readers will be equipped to implement shell scripts in real-world scenarios.