# Java Data Science Made Easy

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## Java Data Science Made Easy

Author | : Richard M. Reese,Jennifer L. Reese,Alexey Grigorev |

Publsiher | : Packt Publishing Ltd |

Total Pages | : 715 |

Release | : 2017-07-07 |

ISBN | : 1788479181 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Java Data Science Made Easy Book Excerpt:**

Data collection, processing, analysis, and more About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples A highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks. Who This Book Is For This course is meant for Java developers who are comfortable developing applications in Java, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Java programming language will also find this book to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing Java stack, this book is for you! What You Will Learn Understand the key concepts of data science Explore the data science ecosystem available in Java Work with the Java APIs and techniques used to perform efficient data analysis Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images, and create your own search Learn how to build deep neural networks with DeepLearning4j Build data science applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. You will cover a wide range of topics – from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings. By the end of this course, you will be up and running with various facets of data science using Java, in no time at all. This course contains premium content from two of our recently published popular titles: Java for Data Science Mastering Java for Data Science Style and approach This course follows a tutorial approach, providing examples of each of the concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

## Java for Data Science

Author | : Richard Reese,Jennifer Reese |

Publsiher | : Unknown |

Total Pages | : 558 |

Release | : 2016-12-30 |

ISBN | : 9781785280115 |

Category | : Electronic Book |

Language | : EN, FR, DE, ES & NL |

**Java for Data Science Book Excerpt:**

Examine the techniques and Java tools that are supporting the growing field of data scienceAbout This Book* Your entry ticket to the world of data science with the stability and power of Java* Explore, analyze, and visualize your data effectively using easy-to-follow examples* Make your Java applications smarter using machine learningWho This Book Is ForThis book is for Java developers who are comfortable with developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful.What you will learn* Understand the nature and key concepts used in the field of data science* Grasp how data is collected, cleaned, and processed* Get to grips with key data analysis techniques* See specialized analysis techniques centered around machine learning* Master the effective visualization of your data* Work with the Java APIs and techniques used to perform data analysisIn DetailData science is concerned with extracting knowledge and insights from a wide variety of data sources to analyze patterns or predict future behavior. It draws from a wide array of disciplines including such fields as statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques so you understand their purpose and application.The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and then more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data.The book ends with a discussion on the resources that support the parallel implementation of many of these techniques and then a conclusion where more in-depth problem are illustrated. Due to the nature of the topic, simple examples of a technique are presented early followed by a more detailed treatment later in the book. This permits a more natural and smooth introduction to the techniques and flow in the book.

## Java for Data Science

Author | : Richard M. Reese,Jennifer L. Reese |

Publsiher | : Packt Publishing Ltd |

Total Pages | : 386 |

Release | : 2017-01-10 |

ISBN | : 1785281240 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Java for Data Science Book Excerpt:**

Examine the techniques and Java tools supporting the growing field of data science About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples Make your Java applications more capable using machine learning Who This Book Is For This book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful. What You Will Learn Understand the nature and key concepts used in the field of data science Grasp how data is collected, cleaned, and processed Become comfortable with key data analysis techniques See specialized analysis techniques centered on machine learning Master the effective visualization of your data Work with the Java APIs and techniques used to perform data analysis In Detail Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application. The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book. Style and approach This book follows a tutorial approach, providing examples of each of the major concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

## Scalability Patterns

Author | : Chander Dhall |

Publsiher | : Apress |

Total Pages | : 167 |

Release | : 2018-07-20 |

ISBN | : 1484210735 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Scalability Patterns Book Excerpt:**

In this book, the CEO of Cazton, Inc. and internationally-acclaimed speaker, Chander Dhall, demonstrates current website design scalability patterns and takes a pragmatic approach to explaining their pros and cons to show you how to select the appropriate pattern for your site. He then tests the patterns by deliberately forcing them to fail and exposing potential flaws before discussing how to design the optimal pattern to match your scale requirements. The author explains the use of polyglot programming and how to match the right patterns to your business needs. He also details several No-SQL patterns and explains the fundamentals of different paradigms of No-SQL by showing complementary strategies of using them along with relational databases to achieve the best results. He also teaches how to make the scalability pattern work with a real-world microservices pattern. With the proliferation of countless electronic devices and the ever growing number of Internet users, the scalability of websites has become an increasingly important challenge. Scalability, even though highly coveted, may not be so easy to achieve. Think that you can't attain responsiveness along with scalability? Chander Dhall will demonstrate that, in fact, they go hand in hand. What You'll Learn Architect and develop applications so that they are easy to scale. Learn different scaling and partitioning options and the combinations. Learn techniques to speed up responsiveness. Deep dive into caching, column-family databases, document databases, search engines and RDBMS. Learn scalability and responsiveness concepts that are usually ignored. Effectively balance scalability, performance, responsiveness, and availability while minimizing downtime. Who This Book Is For Executives (CXOs), software architects , developers, and IT Pros

## Technology Made Simple for the Technical Recruiter Second Edition

Author | : Obi Ogbanufe |

Publsiher | : iUniverse |

Total Pages | : 318 |

Release | : 2019-04-27 |

ISBN | : 1532064985 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Technology Made Simple for the Technical Recruiter Second Edition Book Excerpt:**

If you’re a technical recruiter who wants to keep your skills up to date in the competitive field of technical resource placement, you need a detailed guidebook to outpace competitors. This technical skills primer focuses on technology fundamentals—from basic programming terms to big data vocabulary, network lingo, operating system jargon, and other crucial skill sets. Topics covered include · sample questions to ask candidates, · types of networks and operating systems, · software development strategies, · cloud systems administration and DevOps, · data science and database job roles, and · information security job roles. Armed with indispensable information, the alphabet soup of technology acronyms will no longer be intimidating, and you will be able to analyze client and candidate requirements with confidence. Written in clear and concise prose, Technology Made Simple for the Technical Recruiter is an invaluable resource for any technical recruiter.

## Python Made Easy

Author | : Nilabh Nishchhal |

Publsiher | : Notion Press |

Total Pages | : 436 |

Release | : 2020-10-20 |

ISBN | : 1649837267 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Python Made Easy Book Excerpt:**

Python Made Easy: Beginners Guide to Programming and Data Analysis using Python Get comprehensive learning of Python Programming starting from the very basics and going up to utilizing python libraries for data analysis and Visualization. Based on the author’s journey to master Python, this book will help you to quickly start with writing programs and solving your problems using Python. It provides an ideal and elegant way to start learning Python, both for a newcomer to the programming world and a professional developer expert in other languages. This book comes loaded with illustrations and real-life examples. It gives you exercises which challenge you to refresh your conceptual clarity and write better codes. It is super easy to follow and will work as a self-paced tutorial to get you started with the latest and best in Python. All the advanced Python features to date are included. • Get to know the history, present, and future of Data Science • Get introduced to the basics of Computer Programming • Explore the exciting world of Python using Anaconda • Learn how to install and use Python on your computer • Create your Variables, Objects and learn Syntax of operations • Explore Python’s built-in object types like Lists, dictionaries, Tuples, Strings and sets • Learn to make your codes reusable by using functions • Organize your codes, functions and other objects into larger components with Modules • Explore Classes – the Object-Oriented Programming tool for elegant codes • Write complex codes and learn how to handle Errors and Exceptions • Learn about NumPy arrays and operations on them • Explore data analysis using pandas on a real-life data set • Dive into the exciting world of Visualization with 3 chapters on Visualization and Matplotlib • Experience the Power of What you learnt by 3 projects • Learn to make your own application complete with GUI by using API

## Computer Science Made Simple

Author | : V. Anton Spraul |

Publsiher | : Crown |

Total Pages | : 192 |

Release | : 2010-02-17 |

ISBN | : 9780307433015 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Computer Science Made Simple Book Excerpt:**

Be smarter than your computer If you don't understand computers, you can quickly be left behind in today's fast-paced, machine-dependent society. Computer Science Made Simple offers a straightforward resource for technology novices and advanced techies alike. It clarifies all you need to know, from the basic components of today’s computers to using advanced applications. The perfect primer, it explains how it all comes together to make computers work. Topics covered include: * hardware * software * programming * networks * the internet * computer graphics * advanced computer concepts * computers in society Look for these Made Simple titles: Accounting Made Simple Arithmetic Made Simple Astronomy Made Simple Biology Made Simple Bookkeeping Made Simple Business Letters Made Simple Chemistry Made Simple Earth Science Made Simple English Made Simple French Made Simple German Made Simple Inglés Hecho Fácil Investing Made Simple Italian Made Simple Keyboarding Made Simple Latin Made Simple Learning English Made Simple Mathematics Made Simple The Perfect Business Plan Made Simple Philosophy Made Simple Physics Made Simple Psychology Made Simple Sign Language Made Simple Spanish Made Simple Spelling Made Simple Statistics Made Simple Your Small Business Made Simple www.broadway.com

## ISC Computer Science XI

Author | : Dr. Dheeraj Mehrotra |

Publsiher | : S. Chand Publishing |

Total Pages | : 135 |

Release | : 2022 |

ISBN | : 9355013612 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**ISC Computer Science XI Book Excerpt:**

ISC Computer Science XI

## Proceedings of 3rd International Conference on Computing Informatics and Networks

Author | : Ajith Abraham,Oscar Castillo,Deepali Virmani |

Publsiher | : Springer Nature |

Total Pages | : 659 |

Release | : 2021-03-14 |

ISBN | : 981159712X |

Category | : Technology & Engineering |

Language | : EN, FR, DE, ES & NL |

**Proceedings of 3rd International Conference on Computing Informatics and Networks Book Excerpt:**

This book is a collection of high-quality peer-reviewed research papers presented in the Third International Conference on Computing Informatics and Networks (ICCIN 2020) organized by the Department of Computer Science and Engineering (CSE), Bhagwan Parshuram Institute of Technology (BPIT), Delhi, India, during 29–30 July 2020. The book discusses a wide variety of industrial, engineering and scientific applications of the emerging techniques. Researchers from academic and industry present their original work and exchange ideas, information, techniques and applications in the field of artificial intelligence, expert systems, software engineering, networking, machine learning, natural language processing and high-performance computing.

## Data Structures And Algorithms

Author | : Harry. H. Chaudhary. |

Publsiher | : Programmers Mind LLC New York. |

Total Pages | : 250 |

Release | : 2014-10-01 |

ISBN | : 1928374650XXX |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Data Structures And Algorithms Book Excerpt:**

Features of Book - Essential Data Structures Skills -- Made Easy! All Code/Algo written in C Programming. || Learn with Fun strategy. Anyone can comfortably follow this book to Learn DSA Step By Step. Unique strategy- Concepts, Problems, Analysis, Questions, Solutions. Why This Book - This book gives a good start and complete introduction for data structures and algorithms for Beginner’s. While reading this book it is fun and easy to read it. This book is best suitable for first time DSA readers, Covers all fast track topics of DSA for all Computer Science students and Professionals. Learn all Concept’s Clearly with World Famous Programmer Harry Chaudhary. Main Objective - Data structures is concerned with the storage, representation and manipulation of data in a computer. In this book, we discuss some of the more versatile and popular data structures used to solve a variety of useful problems. Among the topics are linked lists, stacks, queues, trees, graphs, sorting and hashing. What Special - Data Structures & Algorithms Using C or C++ takes a gentle approach to the data structures course in C Providing an early, text gives students a firm grasp of key concepts and allows those experienced in another language to adjust easily. Flexible by design,. Finally, a solid foundation in building and using abstract data types is alsoprovided. Using C, this book develops the concepts & theory of data structures and algorithm analysis in a gradual, step-by-step manner, proceeding from concrete examples to abstract principles. Standish covers a wide range of both traditional and contemporary software engineering topics. This is a handy guide of sorts for any computer science Students, This book is a solution bank for various problems related to data structures and algorithms. It can be used as a reference manual by Computer Science Engineering students. This Book also covers all aspects of CS, IT. Special Note: Digital Pdf Edition || Epub Edition is Available on Google Play & Books. less

## Practical Machine Learning with Python

Author | : Dipanjan Sarkar,Raghav Bali,Tushar Sharma |

Publsiher | : Apress |

Total Pages | : 530 |

Release | : 2017-12-20 |

ISBN | : 1484232070 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Practical Machine Learning with Python Book Excerpt:**

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students

## C Programming made easy

Author | : Shaikh Nooruddin |

Publsiher | : VIEH GROUP |

Total Pages | : 291 |

Release | : 2022 |

ISBN | : 1928374650XXX |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**C Programming made easy Book Excerpt:**

Have you never programmed a computer before, and think or have been told that C is a good programming language to get started with. It is! Maybe you have some experience with other programming languages, but want to learn C. It's a great language to add to your resume! Or perhaps you are stuck in a low paying programming job, and want to move up to a better, more senior position. Learning C can help you! The fact is, learning how to program in C is not only an excellent programming language to get started with, but it will also make you a better programming in other computer languages! Why learn C ? C is often considered to be the mother of all languages because so many other languages have been based on it. Though C is simple it is one of the most powerful languages ever created. Considering it was created over 40 years ago, it is still used heavily and is usually in the top 5 or 10 most popular and most widely programming languages in the world. Learning C can actually make you a better programming in other languages like C++, Java, or C# by equipping you with a mental model of what the computer is actually doing when you run your programs. By learning how things really work "under the hood", and understand memory space, CPU architecture and so on, you can create more efficient programs, and obtain a huge advantage over other programmers in the process. If you want to become a better developer, learning C is a great way to start! Why taking this book is the best decision you can make. By the end of this book, you will understand the fundamentals of the C Programming Language, and make yourself more marketable for entry level programming positions. You will understand variables and the different data types, be able to utilize functions and arrays, understand the concept of pointers, learn about control flow (decision statements and iteration). You will be in a position to apply for real-time programming positions, and truly understand the core language that most modern languages are based on! If you have previously used the C programming language, then this book will deepen your understanding of it. If you have never used it, no problem, you will see that it can help you become a more efficient C developer. The book will be constantly refined in the future based on student feedback! This book does not skip on the details. You will learn how to write high quality code and become an excellent problem solver. This book does not just present how to code in the C programming language, but, also includes all the details on "why" you are doing the things you are doing. After reading this book, you will fully understand the concepts of the C Programming language.

## Data Structures and Algorithms Made Easy in Java

Author | : Narasimha Karumanchi |

Publsiher | : Unknown |

Total Pages | : 438 |

Release | : 2011-12-16 |

ISBN | : 9788192107554 |

Category | : Electronic Book |

Language | : EN, FR, DE, ES & NL |

**Data Structures and Algorithms Made Easy in Java Book Excerpt:**

Video Link: youtube.com/watch?v=l_GRquIrVyg A handy guide of sorts for any computer science professional, "Data Structures And Algorithms Made Easy in Java: Data Structure And Algorithmic Puzzles" is a solution bank for various complex problems related to data structures and algorithms. It can be used as a reference manual by those readers in the computer science industry. The book has around 21 chapters and covers Recursion and Backtracking, Linked Lists, Stacks, Queues, Trees, Priority Queue and Heaps, Disjoint Sets ADT, Graph Algorithms, Sorting, Searching, Selection Algorithms [Medians], Symbol Tables, Hashing, String Algorithms, Algorithms Design Techniques, Greedy Algorithms, Divide and Conquer Algorithms, Dynamic Programming, Complexity Classes, and other Miscellaneous Concepts. Data Structures And Algorithms Made Easy in Java: Data Structure And Algorithmic Puzzles by Narasimha Karumanchi was published in 2011, and it is coded in Java language. This book serves as guide to prepare for interviews, exams, and campus work. It is also available in C/C++. In short, this book offers solutions to various complex data structures and algorithmic problems. Peeling Data Structures and Algorithms for (Java, Second Edition): Programming puzzles for interviewsCampus PreparationDegree/Masters Course PreparationInstructor'sBig job hunters: Microsoft, Google, Apple, Amazon, Yahoo, Flip Kart, Adobe, IBM Labs, Citrix, Mentor Graphics, NetApp, Oracle, Face book, McAfee and many moreReference Manual for working people What is unique? Our main objective isn't to propose theorems and proofs about DS and Algorithms. We took the direct route and solved problems of varying complexities. That is, each problem corresponds to multiple solutions with different complexities. In other words, we ennumerated possible solutions. With this approach, even when a new question arises, we offer a choice of different solution strategies based on your priorities. Topics Covered: IntroductionRecursion and BacktrackingLinked ListsStacksQueuesTreesPriority Queue and HeapsDisjoint Sets ADTGraph AlgorithmsSorting Searching Selection Algorithms [Medians] Symbol Tables Hashing String Algorithms Algorithms Design Techniques Greedy Algorithms Divide and Conquer Algorithms Dynamic Programming Complexity Classes Miscellaneous Concepts Target Audience? These books prepare readers for interviews, exams, and campus work. Language? All code was written in Java. If you are using C/C++, please search for "Data Structures and Algorithms Made Easy." Also, check out sample chapters and the blog at: CareerMonk.com

## Data Science on the Google Cloud Platform

Author | : Valliappa Lakshmanan |

Publsiher | : "O'Reilly Media, Inc." |

Total Pages | : 462 |

Release | : 2022-03-29 |

ISBN | : 109811891X |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Data Science on the Google Cloud Platform Book Excerpt:**

Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines

## The Data Science Handbook

Author | : Field Cady |

Publsiher | : John Wiley & Sons |

Total Pages | : 416 |

Release | : 2017-01-20 |

ISBN | : 1119092930 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**The Data Science Handbook Book Excerpt:**

A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using PythonTM along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.

## Data Science and Big Data Analytics

Author | : EMC Education Services |

Publsiher | : John Wiley & Sons |

Total Pages | : 432 |

Release | : 2015-01-05 |

ISBN | : 1118876059 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Data Science and Big Data Analytics Book Excerpt:**

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Corresponding data sets are available from the book’s page at Wiley which you can find on the Wiley site by searching for the ISBN 9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

## Data Science for Mathematicians

Author | : Nathan Carter |

Publsiher | : CRC Press |

Total Pages | : 498 |

Release | : 2020-09-16 |

ISBN | : 0429675674 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**Data Science for Mathematicians Book Excerpt:**

Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them.

## Essential Math for Data Science

Author | : Thomas Nield |

Publsiher | : "O'Reilly Media, Inc." |

Total Pages | : 346 |

Release | : 2022-05-26 |

ISBN | : 1098102886 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Essential Math for Data Science Book Excerpt:**

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

## Data Science Strategy For Dummies

Author | : Ulrika Jägare |

Publsiher | : John Wiley & Sons |

Total Pages | : 352 |

Release | : 2019-06-12 |

ISBN | : 1119566274 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Data Science Strategy For Dummies Book Excerpt:**

All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. Learn exactly what data science is and why it’s important Adopt a data-driven mindset as the foundation to success Understand the processes and common roadblocks behind data science Keep your data science program focused on generating business value Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.

## Environmental Software Systems Data Science in Action

Author | : Ioannis N. Athanasiadis,Steven P. Frysinger,Gerald Schimak,Willem Jan Knibbe |

Publsiher | : Springer Nature |

Total Pages | : 272 |

Release | : 2020-01-29 |

ISBN | : 3030398153 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Environmental Software Systems Data Science in Action Book Excerpt:**

This book constitutes the refereed proceedings of the 13th IFIP WG 5.11 International Symposium on Environmental Software Systems, ISESS 2020, held in Wageningen, The Netherlands, in February 2020. The 22 full papers and 3 short papers were carefully reviewed and selected from 29 submissions. The papers cover a wide range of topics on environmental informatics, including data mining, artificial intelligence, high performance and cloud computing, visualization and smart sensing for environmental, earth, agricultural and food applications.