Search results for: learning-to-love-data-science

Learning to Love Data Science

Author : Mike Barlow
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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.

Data Science Fundamentals Part 1

Author : Jonathan Dinu
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20 Hours of Video Instruction Data Science Fundamentals LiveLessons teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The videos present you with applied, example-driven lessons in Python and its associated ecosystem of libraries, where you get your hands dirty with real datasets and see real results. Description If nothing else, by the end of this video course you will have analyzed a number of datasets from the wild, built a handful of applications, and applied machine learning algorithms in meaningful ways to get real results. And along the way you learn the best practices and computational techniques used by a professional data scientist. More specifically, you learn how to acquire data that is openly accessible on the Internet by working with APIs. You learn how to parse XML and JSON data to load it into a relational database. About the Instructor Jonathan Dinu is an author, researcher, and most importantly, an educator. He is currently pursuing a Ph.D. in Computer Science at Carnegie Mellon's Human Computer Interaction Institute (HCII), where he is working to democratize machine learning and artificial intelligence through interpretable and interactive algorithms. Previously, he founded Zipfian Academy (an immersive data science training program acquired by Galvanize), has taught classes at the University of San Francisco, and has built a Data Visualization MOOC with Udacity. In addition to his professional data science experience, he has run data science trainings for a Fortune 500 company and taught workshops at Strata, PyData, and DataWeek (among others). He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and in a former life he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Jonathan has always had a passion for sharing the things he has learned in the most creative ways he can. When he is not working with students, you can find him blogging about data, visualization, and education at hopelessoptimism.com or rambling on Twitter jonathandinu. Skill Level Beginner What You Will Learn How to get up and running with a Python data science environment The essentials of Python 3, including object-oriented programming The basics of the data science process and what each step entails How to build a simple (yet powerful) recommendation engine for Air...

Data Science

Author : Benjamin Smith
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Have you ever wondered what the fuss about data is all about? What do data scientists do? What is machine learning and artificial intelligence exactly? Are they the same? Do you love working with data? If your answer is yes, then you are in the right place.Data science helps make predictions and provides insights by using data analysis, statistics, and machine learning. A data scientist is an individual who can perform the duties of a data analyst, statistician, and a machine learning engineer. There are four main pillars of data science: domain expertise, mathematics, communication, and computer science. Once you get the hang of these four components, you will be closer to enhancing your overall understanding of data science. Are you wondering how you can do this? Well, you don't have to look any further because this book has all the answers you need.For learning data science, you will find numerous resources online, and hence it can be very overwhelming. A good way to learn data science and technology related to it regarding machine learning and artificial intelligence is to make a structure on how to go about it and learn the topics related to data science one by one. This book will provide you with all the information you need about data science and has a few tips and tricks that would make your learning easier. Throughout this book, you will learn more about: ●What is data science?●Why learning data science in the 21st century makes sense●Tools required for data science●Technical and non-technical skills every data scientist must have●Artificial intelligence and machine learning●Using tools like Hadoop, R, Python, and SQL●How to create a visualization of the data set●Various cases and applications of data science●Step by step examplesWith 19 chapters and various subsections, this book will help anyone who wants to learn about data science. You will find tips for learning pre-requisites and also tips regarding the use of various tools and programming languages that are mentioned in the book. By the end of this book, you would have gathered all the necessary information that would help you to become a data scientist. The tips will be very useful to you during programming, analyzing, or visualizing data.If you are eager and want to learn more about data science, take matters into your hands, and grab your copy today

Build a Career in Data Science

Author : Emily Robinson
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Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder

Data Science for Business 2019 2 BOOKS IN 1

Author : Riley Adams
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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.

Data Science in Higher Education

Author : Jesse Lawson
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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.

Introduction to Python for the Computer and Data Sciences

Author : Paul Deitel
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For introductory-level Python programming and/or data-science courses. A groundbreaking, flexible approach to computer science and data science The Deitels' Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs), and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science. The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.

Data Analytics for Businesses 2019

Author : Riley Adams
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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!

From Learning to Love

Author : Harry Frederick Harlow
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From the 1930's to the 1970's, Harry Harlow's experimental findings have changed the way psychologists view learning, thinking, and motivation. The papers selected for From Learning to Love study behavior principles and describe better techniques for examining them. They discuss in depth the development of learning, motivation, and the affectional system as well as the interrelationships between love, aggression, and fear. A comprehensive collection, From Learning to Love is a valuable resource for psychiatrists, child care specialists, and parents who want to understand how their children develop.

Python Data Science

Author : Andrew Park
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★ 55% OFF for Bookstores! NOW at $ 16.64 instead of $ 36.97! LAST DAYS! ★ Do you want to learn More about Data Science or how to master it with Python?Your Customers Will Love This Amazing Guide!If you want to learn more about Data Science or how to master it with the Python Programming Language, then keep reading. Data Science is one of the biggest buzzwords in the business world nowadays. Many businesses know the importance of collecting information, but as they can collect so much data in a short period, the real question is: "what is the next step?" Data Science includes all the different steps that you take with the data: collecting and cleaning them if they come from more than one source, analyzing them, applying Machine Learning algorithms and models, and then presenting your findings from the analysis with some good Data Visualizations. And this is what you will learn in Python Data Science. You will learn about the main steps that are needed to correctly implement Data Science techniques and the algorithms to help you sort through the data and see some amazing results. Some of the topics that we will discuss inside include: What data science is all about and why so many companies are using it to give them a competitive edge. Why Python and how to use it to implement Data Science What is the intersection between Machine Learning and Data Science and how to combine them The main Data Structures & Object-Oriented Python, with practical codes and exercises to use Python Functions and Modules in Python The 7 most important algorithms and models in Data Science Data Aggregation and Group Operations 9 important Data Mining techniques in Data Science Interaction with databases and data in the cloud And Much More! Where most books only focus on how collecting and cleaning the data, this book goes further, providing guidance on how to perform a proper analysis in order to extract precious information that may be vital for a business. Don't miss the opportunity to learn more about these topics. Even if you have never implemented Data Science techniques, learning them is easier than it looks. You just need the right guidance. And Python Data Science provides all the knowledge you need in a simple and practical way. Regardless of your previous experience, you will learn, the techniques to manipulate and process datasets, the principles of Python programming, and its most important real-world applications. Would You Like To Know More?Buy It NOW And Let Your Customers Get Addicted To This Amazing Book!

Using Data to Improve Learning for All

Author : Nancy Love
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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.

Data Science Fundamentals Part 2

Author : Jonathan Dinu
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21+ Hours of Video Instruction Data Science Fundamentals Part II teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The videos present you with applied, example-driven lessons in Python and its associated ecosystem of libraries, where you get your hands dirty with real datasets and see real results. Description If nothing else, by the end of this video course you will have analyzed a number of datasets from the wild, built a handful of applications, and applied machine learning algorithms in meaningful ways to get real results. And all along the way you learn the best practices and computational techniques used by professional data scientists. You get hands-on experience with the PyData ecosystem by manipulating and modeling data. You explore and transform data with the pandas library, perform statistical analysis with SciPy and NumPy, build regression models with statsmodels, and train machine learning algorithms with scikit-learn. All throughout the course you learn to test your assumptions and models by engaging in rigorous validation. Finally, you learn how to share your results through effective data visualization. Code: https://github.com/hopelessoptimism/data-science-fundamentals Resources: http://hopelessoptimism.com/data-science-fundamentals Forum: https://gitter.im/data-science-fundamentals Data: http://insideairbnb.com/get-the-data.html About the Instructor Jonathan Dinu is an author, researcher, and most importantly educator. He is currently pursuing a Ph.D. in Computer Science at Carnegie Mellon's Human Computer Interaction Institute (HCII) where he is working to democratize machine learning and artificial intelligence through interpretable and interactive algorithms. Previously, he founded Zipfian Academy (an immersive data science training program acquired by Galvanize), has taught classes at the University of San Francisco, and has built a Data Visualization MOOC with Udacity. In addition to his professional data science experience, he has run data science trainings for a Fortune 500 company and taught workshops at Strata, PyData, and DataWeek (among others). He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and in a former life he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Jonathan has always had a passion for sharing the thing...

Python Data Science A Step By Step Guide to Data Analysis What a Beginner Needs to Know About Machine Learning and Artificial Intelligenc

Author : Axel Ross
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55% DISCOUNT FOR BOOKSTORES! Attract new customers with this book. They will love it! Geared mainly toward beginners readers, the topic of Python Data Science is getting more and more discussed today as companies increasingly require professionals who can manage Python, Machine Learning and Artificial Intelligence. "Many people think that python, data science, machine learning and artificial intelligence are difficult concepts to understand. Data science uses scientific strategies and Science to process data and to separate information from it. It chips away at a similar idea as Big Data and Data Mining. It requires ground-breaking equipment alongside a useful calculation and programming to take care of the data issues or to process the data for acquiring meaningful learning from it. The present information patterns are giving us 80% of data in unstructured mannered while rest 20% organized in organization for snappy dissecting. The unorganized or semi-organized details require processing to make it valuable for the present-day business person condition. For the most part, this information or details are produced from the wide assortments of sources, for example, content records, money related logs, instruments and sensors, and sight and sound structures. Drawing important and profitable experiences from this information require propelled calculations and tools. This Science is proposing an offer for this purpose, and this is making it a useful science for the present-day mechanical world. The improvement and exceedingly useful inquire about in the world of Computer Science and Technology has made the importance of its most basic and essential of concepts ascend by a thousand-crease. This principle concept is the thing that we have been everlastingly alluding to as data, and it is this data that solitary holds the way to everything in the world. The greatest of organizations and firms of the world have fabricated their establishment and philosophies and determine a unique piece of their pay totally through data. Fundamentally, the value and importance of data can be comprehended by the straightforward certainty that a legitimate store/distribution center of data is a million times more profitable than mine of pure gold in the advanced world. Like this, the vast spread and escalated examines in the field of data has genuinely opened up a lot of potential outcomes and doors (as far as a calling) wherein curating such vast amounts of data are the absolute most lucrative employments a specialized individual can discover today. This guide will focus on the following: Applications and role of data science Data science and applications GUI programming with Tkinter. Working with raw data Build your own sentiment analysis tool Exploration of NLTK K-means clustering Operations on data Variable scope and lifetime in python functions Machine learning & neural networks Principal components analysis Setting up your TensorFlow environment And more! Don't miss the opportunity to learn more about these topics. The future has never been closer and the opportunities it offers are endless. Even if you are a beginner, if you are starting from scratch, this book will allow you to understand topics that you have already heard about and that fascinate you, but that you probably never had the courage to go into." This book is a real gold mine. It has already sold hundreds of thousands of copies and received rave reviews from readers all over the world.Don't pass up the chance to have this book in your store!

Journey to Data Scientist

Author : Kate Strachnyi
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When author Kate Strachnyi wanted to learn more about data science, she went straight to the source. In a series of more than twenty interviews, she asks leading data scientists questions about starting in the field and the future of the industry. With their stories, learn about the many different positions available for data scientists, the criteria recruiters look for when hiring, the best options for building your portfolio, the recruitment and interviewing process, the typical workday for a data scientist, the changing industry and its impact on other industries, the wide variety of projects that use data science, and the skills that can complement and improve your work. Strachnyi's interview subjects include team members from some of the world's largest organizations, including LinkedIn, Pinterest, Bloomberg, and IBM. These men and women graciously explain how they fell in love with data science and list the must-have skills that would make you an invaluable member of a team. Their advice gives you invaluable insight into the world of data science and the best ways you yourself can contribute to amazing research projects and the development of new technology.

It s All Analytics

Author : Scott Burk
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It's All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690) Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, "analytics," is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and terminology? This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series.

Big Data Analytics with SAS

Author : David Pope
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Leverage the capabilities of SAS to process and analyze Big Data About This Book Combine SAS with platforms such as Hadoop, SAP HANA, and Cloud Foundry-based platforms for effecient Big Data analytics Learn how to use the web browser-based SAS Studio and iPython Jupyter Notebook interfaces with SAS Practical, real-world examples on predictive modeling, forecasting, optimizing and reporting your Big Data analysis with SAS Who This Book Is For SAS professionals and data analysts who wish to perform analytics on Big Data using SAS to gain actionable insights will find this book to be very useful. If you are a data science professional looking to perform large-scale analytics with SAS, this book will also help you. A basic understanding of SAS will be helpful, but is not mandatory. What You Will Learn Configure a free version of SAS in order do hands-on exercises dealing with data management, analysis, and reporting. Understand the basic concepts of the SAS language which consists of the data step (for data preparation) and procedures (or PROCs) for analysis. Make use of the web browser based SAS Studio and iPython Jupyter Notebook interfaces for coding in the SAS, DS2, and FedSQL programming languages. Understand how the DS2 programming language plays an important role in Big Data preparation and analysis using SAS Integrate and work efficiently with Big Data platforms like Hadoop, SAP HANA, and cloud foundry based systems. In Detail SAS has been recognized by Money Magazine and Payscale as one of the top business skills to learn in order to advance one's career. Through innovative data management, analytics, and business intelligence software and services, SAS helps customers solve their business problems by allowing them to make better decisions faster. This book introduces the reader to the SAS and how they can use SAS to perform efficient analysis on any size data, including Big Data. The reader will learn how to prepare data for analysis, perform predictive, forecasting, and optimization analysis and then deploy or report on the results of these analyses. While performing the coding examples within this book the reader will learn how to use the web browser based SAS Studio and iPython Jupyter Notebook interfaces for working with SAS. Finally, the reader will learn how SAS's architecture is engineered and designed to scale up and/or out and be combined with the open source offerings such as Hadoop, Python, and R. By the end of this book, you will be able to clearly understand how you can efficiently analyze Big Data using SAS. Style and approach The book starts off by introducing the reader to SAS and the SAS programming language which provides data management, analytical, and reporting capabilities. Most chapters include hands on examples which highlights how SAS provides The Power to Know©. The reader will learn that if they are looking to perform large-scale data analysis that SAS provides an open platform engineered and designed to scale both up and out which allows the power of SAS to combine with open source offerings such as Hadoop, Python, and R.

Data Mining Practical Machine Learning Tools and Techniques

Author : Ian H. Witten
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Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

The Essential Guide on Data Science

Author : Andrew Park
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★ 55% OFF for Bookstores! NOW at $ 21,97 instead of $ 31.97! ★ Are you interested in knowing more about the fascinating world of Data Science? Your Customers Will Love This Amazing Step-By-Step Guide! This book offers you the fundamental knowledge you need to get started, but keep in mind that no book or even teacher can do everything for you. You need to work hard by putting each building block in its place as you advance. Data Science is a massive field of study that requires years of learning and practice before you can master it. This shouldn't discourage you, however! Embrace it as a challenge that you can undertake in order to broaden your horizons and improve your knowledge of all that is Data Science and machine learning. Data Science is a highly complex topic that has continuously been developed for decades. It is constantly evolving, and it can be challenging to keep up with all the past, present, and future concepts. With that being said, this isn't supposed to discourage you from pursuing this field. To start out this guidebook, we are going to take a look at what Data Science is all about, why it is important, and why we would want to work with this process in the first place as well. We will then take some time in order to learn the lifecycle of Data Science, and how we need to go through a series of steps like the finding the right data, preparing the data, coming up with the right model, and more. All of this and more will be discussed in this guidebook so that you can go from start to finish with your own Data Science project. This is just the start of some of the amazing things that we are able to do when it is time to start on Data Science. We are able to spend our time looking at what machine learning is all about, the different types of machine learning, and how we are able to put it all together to makes sure that we can create the right algorithms and models when it is time to sort through our data and find the right patterns and insights in the process. In "The Essential Guide on Data Science" you will discover: What is Data Science? Why is Data Science So Important? The Benefits of Python Data Science The Lifecycle of Data Science The Importance of Visuals with Data Science A Look at Data Mining The Real-Life Applications of Data Science There are a lot of benefits that we are able to see when it comes to working on Data Science, and many companies in a lot of different industries are going to work with this in order to ensure that we will be able to handle how to work with their customers, how to beat out the competition, and so much more. When you are ready to work with the idea of Data Science, and you want to work with all of the different parts that are found with it, then make sure to check out this guidebook to help you get started. You can position yourself to use your deep knowledge and understanding of all the cutting-edge technologies obtained from this book to contribute to the growth of any company and land yourself a new high paying and rewarding job! Buy It NOW and Let Your Customers Get Addicted to This Amazing Book!

Data Science for Business 2019 2 BOOKS In 1

Author : Matt Henderson
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★☆★ This book includes 2 Manuscripts: Data Analytics for Businesses 2019 + Machine Learning for Beginners 2019.★☆★ 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. 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!

Smart Cities Smart Future

Author : Mike Barlow
File Size : 32.31 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!