learning to love data science

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Learning To Love Data Science

Author : Mike Barlow
ISBN : 9781491936542
Genre : Computers
File Size : 90. 87 MB
Format : PDF, Mobi
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Until recently, many people thought big data was a passing fad. "Data science" was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you’ll appreciate how data science is fundamentally altering our world, for better and for worse. Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social good, you’ll find out how far data science reaches. With this anthology, you’ll learn how: Analysts can now get results from their data queries in near real time Indie manufacturers are blurring the lines between hardware and software Companies try to balance their desire for rapid innovation with the need to tighten data security Advanced analytics and low-cost sensors are transforming equipment maintenance from a cost center to a profit center CIOs have gradually evolved from order takers to business innovators New analytics tools let businesses go beyond data analysis and straight to decision-making Mike Barlow is an award-winning journalist, author, and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in a number of industries.

Machine Learning For Decision Makers

Author : Patanjali Kashyap
ISBN : 9781484229880
Genre : Computers
File Size : 82. 73 MB
Format : PDF, Mobi
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Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.

Smart Cities Smart Future

Author : Mike Barlow
ISBN : 9781119516187
Genre : Political Science
File Size : 37. 84 MB
Format : PDF, Kindle
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Are you curious about smart cities? You should be! By mid-century, two-thirds of us will live in cities. The world of tomorrow will be a world of cities. But will they be smart cities? Smart cities are complex blends of technologies, systems and services designed and orchestrated to help people lead productive, fulfilling, safe and happy lives. This remarkable book is a window into our shared future. In crisp language and sharp detail, Mike Barlow and Cornelia Lévy-Bencheton explain how smart cities are powerful forces for positive change. With keen eyes and warm hearts, they invite readers to imagine the world of tomorrow, a fascinating world of connected cities and communities. They capture and convey the depth and richness of the worldwide smart city movement. Smart Cities, Smart Future describes the impact of smart city projects on people in towns, cities and nations around the world. The book includes descriptions of ongoing smart city projects in North America, Europe, Asia and the Middle East. Looking Ahead to an Urban World No two smart cities are alike. No one can say with certainty or precision what the term “smart city” means. There is no standard definition or common template. Today, smart cities are works in progress. They emerge from our hopes and our dreams. This book provides you with the knowledge and insight you need to participate in the smart city movement. It explains how smart cities are “systems of systems” and introduces key concepts such as interoperability, open standards, resiliency, agility, adaptability and continuous improvement. Includes Detailed Glossary of Terms and Essential Vocabulary The book includes a detailed comprehensive glossary of essential smart city terms. The glossary will become your indispensable resource as you engage more deeply with the smart city movement and become more involved in planning our common future in an urban world. Carefully Researched and Crisply Written Smart Cities, Smart Future is carefully researched and fully documented. It includes interviews with leaders and experts in multiple disciplines essential to the development of smart cities, towns, regions, states and nations. Written in the clean style of modern journalism, the book offers a strong and compelling narrative of a changing world. It reminds us that we are responsible for choosing our destiny and determining the shape of things to come. The smart city movement is gaining speed and momentum. Read this book, and enjoy the ride!

Love Your Nearest Neighboor

Author : Data Science Notebooks
ISBN : 1096122936
Genre :
File Size : 42. 19 MB
Format : PDF
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This machine learning notebook makes a great gift for any data scientist, programmer, mathematician, student or engineer working in artificial intelligence. This journal has 120 pages of blank lined paper and can be used for writing notes, thoughts and lists, for doodling or sketching, and also as a diary, organizer or daily / weekly planner.

Data Science From Scratch

Author : Joel Grus
ISBN : 9781491904398
Genre : Computers
File Size : 68. 18 MB
Format : PDF, Kindle
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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Data Analytics For Businesses 2019

Author : Riley Adams
ISBN : 1989632122
Genre : Computers
File Size : 39. 75 MB
Format : PDF, Kindle
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Are you looking for new ways to grow your business, with resources you already have? Do you want to know how the big players like Netflix, Amazon, or Shopify use data analytics to MULTIPLY their growth? Keep listening to learn how to use data analytics to maximise YOUR business. Yes, you have customers that love your product. However, you're having trouble finding new customers and captivating their attention. You realized you're also losing customers, and you have no clue what you can do to prevent this from happening. How do I stand out in a crowd of businesses? How do I target my perfect client and make them choose ME? If this sounds like you, Data Analytics for Businesses if the guide you need. This book will walk you through the fundamental principles of data science and how to apply the "data analytic mindset" when approaching your business. You will learn how to extract valuable insights from data sources you ALREADY HAVE, and make informed business decisions to help you achieve your goals. With real-world examples of how to apply data analytics to your business, this book does what others fail to do. Break the process down step by step, so you can optimize unique parts of your business; such as improving customer loyalty or reducing churn. This guide also helps you understand the many data-mining techniques in use today. Discover the value of applied data science for business decision-making. You'll learn how to think data-analytically, and make connections between data sources to unveil insights you've never imagined. In this book you will learn: ✔︎ Why every company should be leveraging Data Analytics ✔︎ The difference between Big Data, Data Science and Data Analytics. ✔︎ How to achieve your goals by applying data-analytical thinking to your business ✔︎ The recommended data mining techniques for each of your business goals. ✔︎ The most important thing to remember when extracting knowledge from your data. ✔︎ How to use data analytics to improve brand loyalty and customer experience. ✔︎ How to hire the best data scientist, and more. If you are overwhelmed by this whole new topic of data analytics, don't be. This guide is designed for beginners, with all the guidance you need to understand the fundamentals of harnessing data analytics for your business. So even if you have never heard about data analytics until today, I promise we will walk through this step- by-step. By the end of this, you'll be able to think analytically and make informed business decisions. This book illustrates how EASY it is to find success by just applying a few principles. So stop reading this description, and start reading Data Analytics for Businesses instead. Scroll up, and CLICK BUY now!

Data Science In Higher Education

Author : Jesse Lawson
ISBN : 1515206467
Genre :
File Size : 76. 22 MB
Format : PDF, Mobi
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Be the Change your Institution Needs What are leaders in research saying about Data Science in Higher Education? "Where has this book been all these years? This is THE starting point for researchers looking for a leg up in today's college environment. Two parts discussion, one part methodology, and one part witty humor. I love it!" "Buy this book for your analysts. They and your college will thank you." "This is the only book on data science specific for higher education research that covers both theory and practice. I'm not a programmer at all, and I found this book very enjoyable. You wont regret it -- I know I don't!" "When our department was tasked with coming up with a predictive 'machine-learning' model, we hired Jesse to help us. His charisma and knowledge are unmatched, and this book only helps to breathe fresh life into issues in research today that are all too often swept under the rug." Discover the tools to take your institution to the next level! Data Science in higher education is the process of turning raw institutional data into actionable intelligence. With this introduction to foundational topics in machine learning and predictive analytics, ambitious leaders in research can develop and employ sophisticated predictive models to better inform their institution's decision-making process. You don't need an advanced degree in math or statistics to do data science. With the open-source statistical programming language R, you'll learn how to tackle real-life institutional data challenges (with actual institutional data!) by going step-by-step through different case studies. Topics include: Simple, Multiple, & Logistic Regression Techniques, and Naive Bayes Classifiers Best Practices for Data Scientists in Higher Education Narrative-style stories, gotchas, and insights from actual data science jobs at colleges and universities "Forget the textbooks. This is a book on data science written for institutional researchers *by* an institutional researcher. You need this book."------------------------------------------ Data Science is the art of carefully picking through that pile of book pages and putting together a complete book. It's the art of developing a narrative for your data, so that all the raw information that your institution warehouses and reports in bar charts and histograms is replaced with actionable intelligence. Here's what we know: Data science can and should be an integral part of college and university operations. Institutional effectiveness should be working side-by-side with faculty and educators to collect, clean, and mine through data of current and past students' behaviors in order to better empower counseling and advisement services (whether virtual or otherwise). Data itself should be considered an asset to an institution, and the data mining process a necessary function of institutional operations. So how do we do it? It starts with a solid perspective and great research tools. With Data Science in Higher Education you'll learn about and solve real-world institutional problems with open-source tools and machine learning research techniques. Using R, you'll tackle case studies from real colleges and develop predictive analytical solutions to problems that colleges and universities face to this day.

Data Science For Business 2019 2 Books In 1

Author : Riley Adams
ISBN : 199917707X
Genre : Computers
File Size : 29. 43 MB
Format : PDF, Mobi
Download : 999
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★This book includes 2 Manuscripts★ Are you looking for new ways to grow your business, with resources you already have? Do you want to know how the big players like Netflix, Amazon, or Shopify use data analytics to MULTIPLY their growth? Keep listening to learn how to use data analytics to maximize YOUR business.

Using Data To Improve Learning For All

Author : Nancy Love
ISBN : 1412960851
Genre : Education
File Size : 89. 46 MB
Format : PDF, ePub, Docs
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School leaders will discover how to implement collaborative inquiry, use data systematically and effectively, and establish an equitable school climate to improve outcomes for all students.

Machine Learning With Go

Author : Daniel Whitenack
ISBN : 9781785883903
Genre : Computers
File Size : 80. 83 MB
Format : PDF
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Build simple, maintainable, and easy to deploy machine learning applications. About This Book Build simple, but powerful, machine learning applications that leverage Go's standard library along with popular Go packages. Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go Understand when and how to integrate certain types of machine learning model in Go applications. Who This Book Is For This book is for Go developers who are familiar with the Go syntax and can develop, build, and run basic Go programs. If you want to explore the field of machine learning and you love Go, then this book is for you! Machine Learning with Go will give readers the practical skills to perform the most common machine learning tasks with Go. Familiarity with some statistics and math topics is necessary. What You Will Learn Learn about data gathering, organization, parsing, and cleaning. Explore matrices, linear algebra, statistics, and probability. See how to evaluate and validate models. Look at regression, classification, clustering. Learn about neural networks and deep learning Utilize times series models and anomaly detection. Get to grip with techniques for deploying and distributing analyses and models. Optimize machine learning workflow techniques In Detail The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios. Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages. Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations. Style and approach This book connects the fundamental, theoretical concepts behind Machine Learning to practical implementations using the Go programming language.

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