Principles Of Data Science

Author: Sinan Ozdemir
Editor: Packt Publishing Ltd
ISBN: 1785888927
File Size: 30,47 MB
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Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how to perform real-world data science tasks with R and Python Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn Get to know the five most important steps of data science Use your data intelligently and learn how to handle it with care Bridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results Build and evaluate baseline machine learning models Explore the most effective metrics to determine the success of your machine learning models Create data visualizations that communicate actionable insights Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.

Principles Of Data Science

Author: Hamid R. Arabnia
Editor: Springer Nature
ISBN: 303043981X
File Size: 73,23 MB
Format: PDF, ePub
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This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice

Principles Of Strategic Data Science

Author: Dr Peter Prevos
Editor: Packt Publishing Ltd
ISBN: 1838985506
File Size: 76,78 MB
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Take the strategic and systematic approach to analyze data to solve business problems Key Features Gain detailed information about the theory of data science Augment your coding knowledge with practical data science techniques for efficient data analysis Learn practical ways to strategically and systematically use data Book Description Principles of Strategic Data Science is created to help you join the dots between mathematics, programming, and business analysis. With a unique approach that bridges the gap between mathematics and computer science, this book takes you through the entire data science pipeline. The book begins by explaining what data science is and how organizations can use it to revolutionize the way they use their data. It then discusses the criteria for the soundness of data products and how to best visualize information. As you progress, you’ll discover the strategic aspects of data science by learning the five-phase framework that enables you to enhance the value you extract from data. The final chapter of the book discusses the role of a data science manager in helping an organization take the data-driven approach. By the end of this book, you’ll have a good understanding of data science and how it can enable you to extract value from your data. What you will learn Get familiar with the five most important steps of data science Use the Conway diagram to visualize the technical skills of the data science team Understand the limitations of data science from a mathematical and ethical perspective Get a quick overview of machine learning Gain insight into the purpose of using data science in your work Understand the role of data science managers and their expectations Who this book is for This book is ideal for data scientists and data analysts who are looking for a practical guide to strategically and systematically use data. This book is also useful for those who want to understand in detail what is data science and how can an organization take the data-driven approach. Prior programming knowledge of Python and R is assumed.

Ethics And Data Science

Author: Mike Loukides
Editor:
ISBN: 1492078220
File Size: 77,68 MB
Format: PDF, ePub
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As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C's) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.

Principles And Methods For Data Science

Author:
Editor: Elsevier
ISBN: 0444642129
File Size: 18,97 MB
Format: PDF, Mobi
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Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Principles and Methods for Data Science

Data Science From Scratch

Author: Joel Grus
Editor: O'Reilly Media
ISBN: 1492041106
File Size: 59,35 MB
Format: PDF, Docs
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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. With this updated second edition, 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.

Feature Engineering For Machine Learning

Author: Alice Zheng
Editor: "O'Reilly Media, Inc."
ISBN: 1491953195
File Size: 15,28 MB
Format: PDF, ePub, Mobi
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Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques

Data Science For Business

Author: Foster Provost
Editor: "O'Reilly Media, Inc."
ISBN: 144937428X
File Size: 69,12 MB
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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

Principles Of Managerial Statistics And Data Science

Author: Roberto Rivera
Editor: John Wiley & Sons
ISBN: 1119486416
File Size: 35,80 MB
Format: PDF, Kindle
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Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include: Assessing if searches during a police stop in San Diego are dependent on driver’s race Visualizing the association between fat percentage and moisture percentage in Canadian cheese Modeling taxi fares in Chicago using data from millions of rides Analyzing mean sales per unit of legal marijuana products in Washington state Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook: Includes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory Relies on Minitab to present how to perform tasks with a computer Presents and motivates use of data that comes from open portals Focuses on developing an intuition on how the procedures work Exposes readers to the potential in Big Data and current failures of its use Supplementary material includes: a companion website that houses PowerPoint slides; an Instructor's Manual with tips, a syllabus model, and project ideas; R code to reproduce examples and case studies; and information about the open portal data Features an appendix with solutions to some practice problems Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.

Practical Statistics For Data Scientists

Author: Peter Bruce
Editor: O'Reilly Media
ISBN: 1492072915
File Size: 49,13 MB
Format: PDF, Docs
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Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data

Marketing Strategy

Author: Robert W. Palmatier
Editor: Macmillan International Higher Education
ISBN: 1137526246
File Size: 14,58 MB
Format: PDF, ePub, Mobi
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A brand new textbook with an innovative and exciting approach to marketing strategy. Moving away from the outdated 4Ps model to a new approach that reflects real-world companies responding to a differing and dynamic customer base. Research-based and action-orientated, it equips students with the tools to succeed in today's competitive markets.

Big Data Management

Author: Peter Ghavami
Editor: de Gruyter
ISBN: 9783110662917
File Size: 66,71 MB
Format: PDF, ePub
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Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance

A General Introduction To Data Analytics

Author: João Moreira
Editor: John Wiley & Sons
ISBN: 1119296242
File Size: 66,22 MB
Format: PDF, ePub, Docs
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A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.

Data Science And Digital Business

Author: Fausto Pedro García Márquez
Editor: Springer
ISBN: 9783319956503
File Size: 14,65 MB
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This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business.

Data Science

Author: John D. Kelleher
Editor: MIT Press
ISBN: 0262347032
File Size: 10,47 MB
Format: PDF, Mobi
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A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

Data Science From Scratch

Author: Scott Harvey
Editor: Createspace Independent Publishing Platform
ISBN: 9781718726697
File Size: 78,50 MB
Format: PDF, ePub, Docs
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Read for FREE with Kindle Unlimited! Data Science from Scratch: Comprehensive guide with essential principles of Data Science (Beginner's guide) Do you want to learn data Science from scratch? Data is a commodity, but without ways to process it, its value is questionable. Data science is a multidisciplinary field whose goal is to extract value from data in all it's forms. This ebook explores the field of data science through data and its structure as well as the high-level process that you can use to transform data into value. Data science is a process. That's not to say it's mechanical and void of creativity. But, when you dig into the stages of processing data, from munging data sources and data cleansing to machine learning and eventually visualisation, you see that unique steps are involved in transforming raw data into insight. The steps that you use can also vary. In exploratory data analysis, you might have a cleansed data set that's ready to import into R, and you visualise your result but don't deploy the model in a production environment. In another environment, you might be dealing with real-world data and require a process of data merging and cleansing in addition to data scaling and preparation before you can train your machine learning model. Here Is A Preview Of What You'll Learn... What is data science ? What is structured data? The data science process Basic course on Python How to run Python on your computer? Explore the machine learning landscape, particularly neural nets Explore recommender systems, natural language processing, network analysis Much, much more! ACT NOW! Click the orange BUY button at the top of this page! Then you can begin reading Data Science from Scratch: Comprehensive guide with essential principles of Data Science (Beginner's guide) on your Kindle device, computer, tablet or smartphone.

Data Visualization And Text Principles And Practices

Author: Thomas Miller
Editor: Pearson FT Press
ISBN: 9780134308913
File Size: 43,74 MB
Format: PDF, Docs
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Data visualization is increasingly central to predictive analytics and data science. The book focuses on all three application areas of data visualization: exploratory data analysis, model diagnostics, and presentation graphics. Built on the same structure and approach as other books in Thomas W. Miller's popular Modeling Techniques series, it has been carefully designed to serve multiple audiences: business managers, analysts, programmers, and students. Miller begins with core principles, revealing why some data visualizations effectively present information and others don't. He reviews the science of human perception and cognition, proven principles of graphic design, and the growing role of visualization throughout data science -- including examples such as the visualization of time, networks, and maps. Drawing on his pioneering experience teaching data visualization, Miller begins each chapter by stating a real business problem. He explains why the problem is important, describes a relevant dataset, and guides you through solving it with leading open-source tools such as R, Python, D3, and Gephi. (All R and Python code is set apart, so managers or analysts who aren't interested in programming can easily skip it.) Like other books in this series, Data Visualization Principles and Practices draws realistic examples from key application areas, including marketing, finance, sports analytics, web and network data science, text analytics, and social network analysis. Examples include cross-sectional data, time series, network, and spatial data. Readers will discover advanced methods for constructing static and interactive graphics, building web-browser-based presentations, and even creating "information art.""

Data Science For Business And Decision Making

Author: Luiz Paulo Fávero
Editor: Academic Press
ISBN: 9780128112168
File Size: 38,56 MB
Format: PDF, ePub
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Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. Combines statistics and operations research modeling to teach the principles of business analytics Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs

Data Science From Scratch

Author: William Gray
Editor:
ISBN: 9781687276094
File Size: 44,90 MB
Format: PDF, ePub, Mobi
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You Are About To Build Your Knowledge Of Data Science To Perhaps Build A Career Out Of It Even If You Are A Complete Beginner! The most valuable resource is no longer oil and gold; data reigns supreme these days! And if data is the most valuable resource, perhaps the field of data science is the most critical of them all! It is so lucrative that the median entry level starting salary of a data scientist is $98,000! If you think I'm making this up, just think of the Cambridge Analytica story of how it was used in the 2016 Presidential elections in the US to influence people's voting decisions! I'm not being political here; whether true or not, data was used and it, to some extent, was seen to be effective in influencing people! All that is the realm of data science! And it is not just Cambridge Analytica that uses data on a massive scale. Data is used to tell which ad suggestions show up when you are browsing on your favorite website, the kind of videos you see on YouTube for instance, the friend suggestions we see on Facebook, the stuff you see on your newsfeed, the emails that land in your spam folder, our credit rating, how much we pay for insurance, the products/movies that Amazon, Netflix and other online stores display to you and much more! For all these things to be possible, lots of data (an estimated 2.5 exabytes were being generated every single day in 2012, according to IBM) has to be collected, analyzed, interpreted and manipulated to serve a given purpose! Does all this sound like music to your ears? Would you want to understand the inner workings of key concepts of data science, including high performance computing, big data analysis, data infrastructure issues, machine learning, data mining, deep learning and more? This book has a comprehensive introduction to the field of data science to help you to have an above average understanding of data science to get you started. In it, you will learn: What data science is all about, including how it works, how it is used in everyday life and more The fundamentals of computer science and the place of data science in today's highly interconnected society Fundamentals of machine learning, including the intricacies of machine learning in data science and its application in everyday life Natural language processing, automation and artificial intelligence with respect to big data and data science The role of python programming language in modern day data science Data modeling, including the place of data modelers in data science Voice recognition as an important area of data science The concept of distributed systems and big data and their place in data science The concept of data visualization as part of data science The impact of smart technology on data entry processes And much more! The book uses beginner friendly, easy to follow, language that will ultimately help you to start seeing how to apply machine learning and big data analysis in solving everyday problems in the world! If you've ever wanted to dip your feet into the murky and interestingly mysterious world of data science, now is the time to get in! What are you waiting for? Click Buy Now In 1-Click or Buy Now at the top of this page to get started!

The Data Science Design Manual

Author: Steven S. Skiena
Editor: Springer
ISBN: 3319554441
File Size: 80,82 MB
Format: PDF, ePub, Mobi
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This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)