The software listed below are shells which provide a user interface for access to an operating system's services. There are a number of shells available dependent on your operating system. Macs come with Terminal (a terminal emulator); however, users may choose to use a shell available for macOS.
Beginning Data Analysis with Python and Jupyter [Book] by
Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. What you will learn Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests Plan a machine learning classification strategy and train classification, models Use validation curves and dimensionality reduction to tune and enhance your models Discover how you can use web scraping to gather and parse your own bespoke datasets Scrape tabular data from web pages and transform them into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.
Data Science Projects with Python by
Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features Learn techniques to use data to identify the exact problem to be solved Visualize data using different graphs Identify how to select an appropriate algorithm for data extraction Book Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You'll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you'll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. By the end of this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learn Install the required packages to set up a data science coding environment Load data into a Jupyter Notebook running Python Use Matplotlib to create data visualizations Fit a model using scikit-learn Use lasso and ridge regression to reduce overfitting Fit and tune a random forest model and compare performance with logistic regression Create visuals using the output of the Jupyter Notebook Who this book is for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.
Learn Python Programming by
Learn the fundamentals of Python (3.7) and how to apply it to data science, programming, and web development. Fully updated to include hands-on tutorials and projects. Key Features Learn the fundamentals of Python programming with interactive projects Apply Python to data science with tools such as IPython and Jupyter Utilize Python for web development and build a real-world app using Django Book Description Learn Python Programming is a quick, thorough, and practical introduction to Python - an extremely flexible and powerful programming language that can be applied to many disciplines. Unlike other books, it doesn't bore you with elaborate explanations of the basics but gets you up-and-running, using the language. You will begin by learning the fundamentals of Python so that you have a rock-solid foundation to build upon. You will explore the foundations of Python programming and learn how Python can be manipulated to achieve results. Explore different programming paradigms and find the best approach to a situation; understand how to carry out performance optimization and effective debugging; control the flow of a program; and utilize an interchange format to exchange data. You'll also walk through cryptographic services in Python and understand secure tokens. Learn Python Programming will give you a thorough understanding of the Python language. You'll learn how to write programs, build websites, and work with data by harnessing Python's renowned data science libraries. Filled with real-world examples and projects, the book covers various types of applications, and concludes by building real-world projects based on the concepts you have learned. What you will learn Get Python up and running on Windows, Mac, and Linux Explore fundamental concepts of coding using data structures and control flow Write elegant, reusable, and efficient code in any situation Understand when to use the functional or OOP approach Cover the basics of security and concurrent/asynchronous programming Create bulletproof, reliable software by writing tests Build a simple website in Django Fetch, clean, and manipulate data Who this book is for Learn Python Programming is for individuals with relatively little experience in coding or Python. It's also ideal for aspiring programmers who need to write scripts or programs to accomplish tasks. The book shows you how to create a full-fledged application.
The Essential R Reference by
An essential library of basic commands you can copy and paste into R The powerful and open-source statistical programming language R is rapidly growing in popularity, but it requires that you type in commands at the keyboard rather than use a mouse, so you have to learn the language of R. But there is a shortcut, and that's where this unique book comes in. A companion book to Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, this practical reference is a library of basic R commands that you can copy and paste into R to perform many types of statistical analyses. Whether you're in technology, science, medicine, business, or engineering, you can quickly turn to your topic in this handy book and find the commands you need. Comprehensive command reference for the R programming language and a companion book to Visualize This: The FlowingData Guide to Design, Visualization, and Statistics Combines elements of a dictionary, glossary, and thesaurus for the R language Provides easy accessibility to the commands you need, by topic, which you can cut and paste into R as needed Covers getting, saving, examining, and manipulating data; statistical test and math; and all the things you can do with graphs Also includes a collection of utilities that you'll find useful Simplify the complex statistical R programming language with The Essential R Reference. .
Design Patterns and Best Practices in Java by
Create various design patterns to master the art of solving problems using Java Key Features This book demonstrates the shift from OOP to functional programming and covers reactive and functional patterns in a clear and step-by-step manner All the design patterns come with a practical use case as part of the explanation, which will improve your productivity Tackle all kinds of performance-related issues and streamline your development Book Description Having a knowledge of design patterns enables you, as a developer, to improve your code base, promote code reuse, and make the architecture more robust. As languages evolve, new features take time to fully understand before they are adopted en masse. The mission of this book is to ease the adoption of the latest trends and provide good practices for programmers. We focus on showing you the practical aspects of smarter coding in Java. We'll start off by going over object-oriented (OOP) and functional programming (FP) paradigms, moving on to describe the most frequently used design patterns in their classical format and explain how Java's functional programming features are changing them. You will learn to enhance implementations by mixing OOP and FP, and finally get to know about the reactive programming model, where FP and OOP are used in conjunction with a view to writing better code. Gradually, the book will show you the latest trends in architecture, moving from MVC to microservices and serverless architecture. We will finish off by highlighting the new Java features and best practices. By the end of the book, you will be able to efficiently address common problems faced while developing applications and be comfortable working on scalable and maintainable projects of any size. What you will learn Understand the OOP and FP paradigms Explore the traditional Java design patterns Get to know the new functional features of Java See how design patterns are changed and affected by the new features Discover what reactive programming is and why is it the natural augmentation of FP Work with reactive design patterns and find the best ways to solve common problems using them See the latest trends in architecture and the shift from MVC to serverless applications Use best practices when working with the new features Who this book is for This book is for those who are familiar with Java development and want to be in the driver's seat when it comes to modern development techniques. Basic OOP Java programming experience and elementary familiarity with Java is expected.
Fundamentals of Java Programming by
Making extensive use of examples, this textbook on Java programming teaches the fundamental skills for getting started in a command-line environment. Meant to be used for a one-semester course to build solid foundations in Java, Fundamentals of Java Programming eschews second-semester content to concentrate on over 180 code examples and 250 exercises. Key object classes (String, Scanner, PrintStream, Arrays, and File) are included to get started in Java programming. The programs are explained with almost line-by-line descriptions, also with chapter-by-chapter coding exercises. Teaching resources include solutions to the exercises, as well as digital lecture slides.
SAS Essentials by
A step-by-step introduction to using SAS® statistical software as a foundational approach to data analysis and interpretation Presenting a straightforward introduction from the ground up, SAS® Essentials: Mastering SAS for Data Analytics, Second Edition illustrates SAS using hands-on learning techniques and numerous real-world examples. Keeping different experience levels in mind, the highly-qualified author team has developed the book over 20 years of teaching introductory SAS courses. Divided into two sections, the first part of the book provides an introduction to data manipulation, statistical techniques, and the SAS programming language. The second section is designed to introduce users to statistical analysis using SAS Procedures. Featuring self-contained chapters to enhance the learning process, the Second Edition also includes: Programming approaches for the most up-to-date version of the SAS platform including information on how to use the SAS University Edition Discussions to illustrate the concepts and highlight key fundamental computational skills that are utilized by business, government, and organizations alike New chapters on reporting results in tables and factor analysis Additional information on the DATA step for data management with an emphasis on importing data from other sources, combining data sets, and data cleaning Updated ANOVA and regression examples as well as other data analysis techniques A companion website with the discussed data sets, additional code, and related PowerPoint® slides SAS Essentials: Mastering SAS for Data Analytics, Second Edition is an ideal textbook for upper-undergraduate and graduate-level courses in statistics, data analytics, applied SAS programming, and statistical computer applications as well as an excellent supplement for statistical methodology courses. The book is an appropriate reference for researchers and academicians who require a basic introduction to SAS for statistical analysis and for preparation for the Basic SAS Certification Exam.
Exploratory Data Analysis with MATLAB by
Praise for the Second Edition: "The authors present an intuitive and easy-to-read book. ... accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB." --Adolfo Alvarez Pinto, International Statistical Review "Practitioners of EDA who use MATLAB will want a copy of this book. ... The authors have done a great service by bringing together so many EDA routines, but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. --David A Huckaby, MAA Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity, EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book's website. New to the Third Edition Random projections and estimating local intrinsic dimensionality Deep learning autoencoders and stochastic neighbor embedding Minimum spanning tree and additional cluster validity indices Kernel density estimation Plots for visualizing data distributions, such as beanplots and violin plots A chapter on visualizing categorical data
Introduction to Modeling and Simulation with MATLAB® and Python by
Introduction to Modeling and Simulation with MATLAB and Python is intended for students and professionals in science, social science, and engineering that wish to learn the principles of computer modeling, as well as basic programming skills. The book content focuses on meeting a set of basic modeling and simulation competencies that were developed as part of several National Science Foundation grants. Even though computer science students are much more expert programmers, they are not often given the opportunity to see how those skills are being applied to solve complex science and engineering problems and may also not be aware of the libraries used by scientists to create those models. The book interleaves chapters on modeling concepts and related exercises with programming concepts and exercises. The authors start with an introduction to modeling and its importance to current practices in the sciences and engineering. They introduce each of the programming environments and the syntax used to represent variables and compute mathematical equations and functions. As students gain more programming expertise, the authors return to modeling concepts, providing starting code for a variety of exercises where students add additional code to solve the problem and provide an analysis of the outcomes. In this way, the book builds both modeling and programming expertise with a "just-in-time" approach so that by the end of the book, students can take on relatively simple modeling example on their own. Each chapter is supplemented with references to additional reading, tutorials, and exercises that guide students to additional help and allows them to practice both their programming and analytical modeling skills. In addition, each of the programming related chapters is divided into two parts - one for MATLAB and one for Python. In these chapters, the authors also refer to additional online tutorials that students can use if they are having difficulty with any of the topics. The book culminates with a set of final project exercise suggestions that incorporate both the modeling and programming skills provided in the rest of the volume. Those projects could be undertaken by individuals or small groups of students. The companion website at http://www.intromodeling.com provides updates to instructions when there are substantial changes in software versions, as well as electronic copies of exercises and the related code. The website also offers a space where people can suggest additional projects they are willing to share as well as comments on the existing projects and exercises throughout the book. Solutions and lecture notes will also be available for qualifying instructors.
Matlab by
MATLAB: A Practical Introduction to Programming and Problem Solving, winner of TAA's 2017 Textbook Excellence Award ("Texty"), guides the reader through both programming and built-in functions to easily exploit MATLAB's extensive capabilities for tackling engineering and scientific problems. Assuming no knowledge of programming, this book starts with programming concepts, such as variables, assignments, and selection statements, moves on to loops, and then solves problems using both the programming concept and the power of MATLAB. The fifth edition has been updated to reflect the functionality of the current version of MATLAB (R2018a), including the addition of local functions in scripts, the new string type, coverage of recently introduced functions to import data from web sites, and updates to the Live Editor and App Designer.
Advanced Analytics with R and Tableau by
Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of RAbout This Book* A comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and R* Combine Tableau analytics and visualization with the power of R using this step-by-step guide* Wondering how R can be used with Tableau? This book is your one-stop solution.Who This Book Is ForThis book will appeal to Tableau users who want to go beyond the Tableau interface and deploy the full potential of Tableau, by using R to perform advanced analytics with Tableau.A basic familiarity with R is useful but not compulsory, as the book will start off with concrete examples of R and will move quickly into more advanced spheres of analytics using online data sources to support hands-on learning. Those R developers who want to integrate R in Tableau will also benefit from this book.What You Will Learn* Integrate Tableau's analytics with the industry-standard, statistical prowess of R.* Make R function calls in Tableau, and visualize R functions with Tableau using RServe.* Use the CRISP-DM methodology to create a roadmap for analytics investigations.* Implement various supervised and unsupervised learning algorithms in R to return values to Tableau.* Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau/R calculated field functions.In DetailTableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation.Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics.By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.Style and approachTableau (uniquely) offers excellent visualization combined with advanced analytics; R is at the pinnacle of statistical computational languages. When you want to move from one view of data to another, backed up by complex computations, the combination of R and Tableau makes the perfect solution. This example-rich guide will teach you how to combine these two to perform advanced analytics by integrating Tableau with R and create beautiful data visualizations.
Visual Analytics with Tableau by
A four-color journey through a complete Tableau visualization Tableau is a popular data visualization tool that's easy for individual desktop use as well as enterprise. Used by financial analysts, marketers, statisticians, business and sales leadership, and many other job roles to present data visually for easy understanding, it's no surprise that Tableau is an essential tool in our data-driven economy. Visual Analytics with Tableau is a complete journey in Tableau visualization for a non-technical business user. You can start from zero, connect your first data, and get right into creating and publishing awesome visualizations and insightful dashboards. * Learn the different types of charts you can create * Use aggregation, calculated fields, and parameters * Create insightful maps * Share interactive dashboards Geared toward beginners looking to get their feet wet with Tableau, this book makes it easy and approachable to get started right away.
Beginning Data Analysis with Python and Jupyter [Book] by
Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. What you will learn Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests Plan a machine learning classification strategy and train classification, models Use validation curves and dimensionality reduction to tune and enhance your models Discover how you can use web scraping to gather and parse your own bespoke datasets Scrape tabular data from web pages and transform them into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.
Jupyter Cookbook by
Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle Key Features Create and share interactive documents with live code, text and visualizations Integrate popular programming languages such as Python, R, Julia, Scala with Jupyter Develop your widgets and interactive dashboards with these innovative recipes Book Description Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it. What you will learn Install Jupyter and configure engines for Python, R, Scala and more Access and retrieve data on Jupyter Notebooks Create interactive visualizations and dashboards for different scenarios Convert and share your dynamic codes using HTML, JavaScript, Docker, and more Create custom user data interactions using various Jupyter widgets Manage user authentication and file permissions Interact with Big Data to perform numerical computing and statistical modeling Get familiar with Jupyter's next-gen user interface - JupyterLab Who this book is for This cookbook is for data science professionals, developers, technical data analysts, and programmers who want to execute technical coding, visualize output, and do scientific computing in one tool. Prior understanding of data science concepts will be helpful, but not mandatory, to use this book.
Using R and RStudio for Data Management, Statistical Analysis, and Graphics by
Improve Your Analytical Skills Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information. New to the Second Edition The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping New chapter on simulation that includes examples of data generated from complex models and distributions A detailed discussion of the philosophy and use of the knitr and markdown packages for R New packages that extend the functionality of R and facilitate sophisticated analyses Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots Easily Find Your Desired Task Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.
Bash Cookbook by
Create simple to advanced shell scripts and enhance your system functionality with effective recipes Key Features Automate tedious and repetitive tasks Create several novel applications ranging from a simple IRC logger to a Web Scraper Manage your system efficiently by becoming a seasoned Bash user Book Description In Linux, one of the most commonly used and most powerful tools is the Bash shell. With its collection of engaging recipes, Bash Cookbook takes you through a series of exercises designed to teach you how to effectively use the Bash shell in order to create and execute your own scripts. The book starts by introducing you to the basics of using the Bash shell, also teaching you the fundamentals of generating any input from a command. With the help of a number of exercises, you will get to grips with the automation of daily tasks for sysadmins and power users. Once you have a hands-on understanding of the subject, you will move on to exploring more advanced projects that can solve real-world problems comprehensively on a Linux system. In addition to this, you will discover projects such as creating an application with a menu, beginning scripts on startup, parsing and displaying human-readable information, and executing remote commands with authentication using self-generated Secure Shell (SSH) keys. By the end of this book, you will have gained significant experience of solving real-world problems, from automating routine tasks to managing your systems and creating your own scripts. What you will learn Understand the basics of Bash shell scripting on a Linux system Gain working knowledge of how redirections and pipes interact Retrieve and parse input or output of any command Automate tasks such as data collection and creating and applying a patch Create a script that acts like a program with different features Customize your Bash shell and discover neat tricks to extend your programs Compile and install shell and log commands on your system's console using Syslog Who this book is for The Bash Cookbook is for you if you are a power user or system administrator involved in writing Bash scripts in order to automate tasks. This book is also ideal if you are interested in learning how to automate complex daily tasks.
Beginning Unix by
Covering all aspects of the Unix operating system and assuming no prior knowledge of Unix, this book begins with the fundamentals and works from the ground up to some of the more advanced programming techniques The authors provide a wealth of real-world experience with the Unix operating system, delivering actual examples while showing some of the common misconceptions and errors that new users make Special emphasis is placed on the Apple Mac OS X environment as well as Linux, Solaris, and migrating from Windows to Unix A unique conversion section of the book details specific advice and instructions for transitioning Mac OS X, Windows, and Linux users
Shell Scripting by
A compendium of shell scripting recipes that can immediately be used, adjusted, and applied The shell is the primary way of communicating with the Unix and Linux systems, providing a direct way to program by automating simple-to-intermediate tasks. With this book, Linux expert Steve Parker shares a collection of shell scripting recipes that can be used as is or easily modified for a variety of environments or situations. The book covers shell programming, with a focus on Linux and the Bash shell; it provides credible, real-world relevance, as well as providing the flexible tools to get started immediately.Shares a collection of helpful shell scripting recipes that can immediately be used for various of real-world challenges Features recipes for system tools, shell features, and systems administration Provides a host of plug and play recipes for to immediately apply and easily modify so the wheel doesn't have to be reinvented with each challenge faced Come out of your shell and dive into this collection of tried and tested shell scripting recipes that you can start using right away
Wicked Cool Shell Scripts by
Working with the shell is an efficient way to interact with your machine. With just a few lines of shell code, your machine can do exactly what you want it to do. What many people don t realize is you can use shell scripts for many essential (and not so essential) tasks, in addition to managing your files and system operations. This cookbook of useful, customizable, and fun shell scripts gives you tools to solve common problems and personalize your computing environment. Each section contains ready-to-use scripts and an explanation of how each script works, why you d want to use it, and suggestions for changing and expanding it. Save time and effort when working with files, maintaining your system, and managing your website with scripts like: An interactive spell checker that fixes spelling and typing errors in any document A disk backup utility that keeps your files safe in the event of a system crash Web logfile analysis tools that measure the traffic to your site, identify the m
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