Python and R for the Modern Data Scientist
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language; Understand the strengths and weaknesses of each language; Identify use cases where one language is better suited than the other; Understand the modern open source ecosystem av ...
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language; Understand the strengths and weaknesses of each language; Identify use cases where one language is better suited than the other; Understand the modern open source ecosystem av ...
Tableau Desktop Pocket Reference
In a crowded field of data visualization and analytics tools, Tableau Desktop has emerged as the clear leader. This is partly due to its ease of use, but once you dive into Tableau's extensive feature set, you'll understand just how powerful and flexible this software can be for your business or organization. With this handy pocket reference, author Ryan Sleeper (Innovative Tableau) shows you how to translate the vast amounts of data into useful information. Tableau has done an amazing job of making valuable insights accessible to analysts and executives who would otherwise need to rely on IT. This book quickly guides you through Tableau Desktop's learning curve. You'll learn: How to shape data for use with Tableau Desktop; How to create the most effective chart types; Core concepts including discrete versus continuous; Must-know technical features including filters, parameters, and sets; Key syntax for creating the most useful analyses; How to bring it all together with dashboar ...
In a crowded field of data visualization and analytics tools, Tableau Desktop has emerged as the clear leader. This is partly due to its ease of use, but once you dive into Tableau's extensive feature set, you'll understand just how powerful and flexible this software can be for your business or organization. With this handy pocket reference, author Ryan Sleeper (Innovative Tableau) shows you how to translate the vast amounts of data into useful information. Tableau has done an amazing job of making valuable insights accessible to analysts and executives who would otherwise need to rely on IT. This book quickly guides you through Tableau Desktop's learning curve. You'll learn: How to shape data for use with Tableau Desktop; How to create the most effective chart types; Core concepts including discrete versus continuous; Must-know technical features including filters, parameters, and sets; Key syntax for creating the most useful analyses; How to bring it all together with dashboar ...
Python for Everybody
Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet. Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software. This book uses the Python 3 language. ...
Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet. Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software. This book uses the Python 3 language. ...
Parallel and High Performance Computing
Complex calculations, like training deep learning models or running large-scale simulations, can take an extremely long time. Efficient parallel programming can save hours - or even days - of computing time. Parallel and High Performance Computing shows you how to deliver faster run-times, greater scalability, and increased energy efficiency to your programs by mastering parallel techniques for multicore processor and GPU hardware. Write fast, powerful, energy efficient programs that scale to tackle huge volumes of data. Using parallel programming, your code spreads data processing tasks across multiple CPUs for radically better performance. With a little help, you can create software that maximizes both speed and efficiency. Parallel and High Performance Computing offers techniques guaranteed to boost your code's effectiveness. You'll learn to evaluate hardware architectures and work with industry standard tools such as OpenMP and MPI. You'll master the data structures and algor ...
Complex calculations, like training deep learning models or running large-scale simulations, can take an extremely long time. Efficient parallel programming can save hours - or even days - of computing time. Parallel and High Performance Computing shows you how to deliver faster run-times, greater scalability, and increased energy efficiency to your programs by mastering parallel techniques for multicore processor and GPU hardware. Write fast, powerful, energy efficient programs that scale to tackle huge volumes of data. Using parallel programming, your code spreads data processing tasks across multiple CPUs for radically better performance. With a little help, you can create software that maximizes both speed and efficiency. Parallel and High Performance Computing offers techniques guaranteed to boost your code's effectiveness. You'll learn to evaluate hardware architectures and work with industry standard tools such as OpenMP and MPI. You'll master the data structures and algor ...
Protecting SQL Server Data
In Protecting SQL Server Data, John Magnabosco demonstrates how sensitive data, stored in SQL Server, can be protected using an efficient and maintainable encryption-based data architecture. He explains how to assess and categorize data elements according to sensitivity, regulate access to the various categories of data using database roles, views and stored procedures, and then how to implement a secure data architecture using features such as cell-level encryption, transparent data encryption, one-way encryption, obfuscation, and more. The battle to secure personal and business data is a tough one, and the consequences of mishandling sensitive data can be severe. Even more damaging than the fines and lawsuits that can be result from non-compliance with regulations, is the loss of customer confidence that results when these breaches of security occur. Database Administrators must use every weapon and strategy at their disposal in the "war" to protect their sensitive data from ...
In Protecting SQL Server Data, John Magnabosco demonstrates how sensitive data, stored in SQL Server, can be protected using an efficient and maintainable encryption-based data architecture. He explains how to assess and categorize data elements according to sensitivity, regulate access to the various categories of data using database roles, views and stored procedures, and then how to implement a secure data architecture using features such as cell-level encryption, transparent data encryption, one-way encryption, obfuscation, and more. The battle to secure personal and business data is a tough one, and the consequences of mishandling sensitive data can be severe. Even more damaging than the fines and lawsuits that can be result from non-compliance with regulations, is the loss of customer confidence that results when these breaches of security occur. Database Administrators must use every weapon and strategy at their disposal in the "war" to protect their sensitive data from ...
SQL Server on Kubernetes
Build a modern data platform by deploying SQL Server in Kubernetes. Modern application deployment needs to be fast and consistent to keep up with business objectives and Kubernetes is quickly becoming the standard for deploying container-based applications, fast. This book introduces Kubernetes and its core concepts. Then it shows you how to build and interact with a Kubernetes cluster. Next, it goes deep into deploying and operationalizing SQL Server in Kubernetes, both on premises and in cloud environments such as the Azure Cloud. You will begin with container-based application fundamentals and then go into an architectural overview of a Kubernetes container and how it manages application state. Then you will learn the hands-on skill of building a production-ready cluster. With your cluster up and running, you will learn how to interact with your cluster and perform common administrative tasks. Once you can admin the cluster, you will learn how to deploy applications and SQL Serve ...
Build a modern data platform by deploying SQL Server in Kubernetes. Modern application deployment needs to be fast and consistent to keep up with business objectives and Kubernetes is quickly becoming the standard for deploying container-based applications, fast. This book introduces Kubernetes and its core concepts. Then it shows you how to build and interact with a Kubernetes cluster. Next, it goes deep into deploying and operationalizing SQL Server in Kubernetes, both on premises and in cloud environments such as the Azure Cloud. You will begin with container-based application fundamentals and then go into an architectural overview of a Kubernetes container and how it manages application state. Then you will learn the hands-on skill of building a production-ready cluster. With your cluster up and running, you will learn how to interact with your cluster and perform common administrative tasks. Once you can admin the cluster, you will learn how to deploy applications and SQL Serve ...
AI for Healthcare with Keras and Tensorflow 2.0
Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries. This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you c ...
Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries. This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you c ...
Tableau Strategies
If you want to increase Tableau's value to your organization, this practical book has your back. Authors Ann Jackson and Luke Stanke guide data analysts through strategies for solving real-world analytics problems using Tableau. Starting with the basics and building toward advanced topics such as multidimensional analysis and user experience, you'll explore pragmatic and creative examples that you can apply to your own data. Staying competitive today requires the ability to quickly analyze and visualize data and make data-driven decisions. With this guide, data practitioners and leaders alike will learn strategies for building compelling and purposeful visualizations, dashboards, and data products. Every chapter contains the why behind the solution and the technical knowledge you need to make it work. Use this book as a high-value on-the-job reference guide to Tableau; Visualize different data types and tackle specific data challenges; Create compelling data visualizations, dashb ...
If you want to increase Tableau's value to your organization, this practical book has your back. Authors Ann Jackson and Luke Stanke guide data analysts through strategies for solving real-world analytics problems using Tableau. Starting with the basics and building toward advanced topics such as multidimensional analysis and user experience, you'll explore pragmatic and creative examples that you can apply to your own data. Staying competitive today requires the ability to quickly analyze and visualize data and make data-driven decisions. With this guide, data practitioners and leaders alike will learn strategies for building compelling and purposeful visualizations, dashboards, and data products. Every chapter contains the why behind the solution and the technical knowledge you need to make it work. Use this book as a high-value on-the-job reference guide to Tableau; Visualize different data types and tackle specific data challenges; Create compelling data visualizations, dashb ...
Just Enough R
If your job involves working with data in any manner, you cannot afford to ignore the R revolution! If your domain is called data analysis, analytics, informatics, data science, reporting, business intelligence, data management, big data, or visualization, you just have to learn R as this programming language is a game-changing sledgehammer. However, if you have looked at a standard text on R or read some of the online discussions, you might feel that there is a steep learning curve of six months or more to grok the language. I will debunk this myth through my book by focusing on practical essentials instead of theory. If you have programmed in some language in the past (whether that language be SAS, SPSS, C, C++, C#, Java, Python, Perl, Visual Basic, Ruby, Scala, shell scripts, or plain old SQL), even if you are rusty, this book will get you up and running with R in a single day, writing programs for data analysis and visualization. ...
If your job involves working with data in any manner, you cannot afford to ignore the R revolution! If your domain is called data analysis, analytics, informatics, data science, reporting, business intelligence, data management, big data, or visualization, you just have to learn R as this programming language is a game-changing sledgehammer. However, if you have looked at a standard text on R or read some of the online discussions, you might feel that there is a steep learning curve of six months or more to grok the language. I will debunk this myth through my book by focusing on practical essentials instead of theory. If you have programmed in some language in the past (whether that language be SAS, SPSS, C, C++, C#, Java, Python, Perl, Visual Basic, Ruby, Scala, shell scripts, or plain old SQL), even if you are rusty, this book will get you up and running with R in a single day, writing programs for data analysis and visualization. ...
Human-in-the-Loop Machine Learning
Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process. Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You'll find best pra ...
Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process. Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You'll find best pra ...