Search Results: 'data science'


495 results for "data science"
Executive Data Science By Roger Peng
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In this concise book you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash... More > course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.< Less
The Art of Data Science By Roger Peng, Elizabeth Matsui
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This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have... More > carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.< Less
Data Science Programming In Python By Anita Raichand
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Learn Data Science Programming in Python including munging, aggregating, and visualizing data.
R Programming for Data Science By Roger Peng
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Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated.... More > But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.< Less
Methods for Data Science: III - Data Visualization and R By James Godfrey
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This reference provides topical discussions of data visualization and R programming techniques. The approach is to introduce R language syntax and provide hands-on examples of R programming using... More > topics of interest to data scientists. The emphasis is on using the R function architecture to employ structured programming techniques. The focus is on illustrating programming examples within the context of this hands-on approach.< Less
Data Science and Analytics for Ordinary People By Jeffrey Strickland
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Data Science and Analytics for Ordinary People is a collection of blogs I have written on LinkedIn over the past year. As I continue to perform big data analytics, I continue to discover, not only my... More > weaknesses in communicating the information, but new insights into using the information obtained from analytics and communicating it. These are the kinds of things I blog about and are contained herein. Data science and analytics have been used as synonyms on occasion. In reality data science includes data modeling, data mining, data analysis, database architecture and so on. Analytics is what we do to make sense of the data. That is, we take data and turn it into information for business decision makers. This our course implies that we translate our data science jargon into English.< Less
Report Writing for Data Science in R By Roger Peng
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This book teaches the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducibility is the idea that data analyses should be published or made available with... More > their data and software code so that others may verify the findings and build upon them. The need for reproducible report writing is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This book will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.< Less
Practical Approaches to Learning Data Sciences By Raghuram Srinivas
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Data Scientist has been declared the top job by many surveys in the year 2016. Data Scientist made the top of the list based on a composite score of median reported salary, job openings and career... More > opportunities. This is a new field and there aren’t many professionals and programmers familiar with this field and programming paradigm. This book is an attempt to introduce the core concepts of this exciting new field with practical and easy to understand examples. < Less
Doomsday: December 21, 2012 By Data Knowles
eBook (ePub): $1.49
December 21, 2012 is quickly approaching! Jan and David uncover some terrible events that are happening. What will they do?
Data as Capta By Johanna Drucker
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As humanists adopt the methods of empirical sciences to produce visualizations in digital data mining, the question of whether the foundations of humanistic interpretation are served by such... More > approaches arises. This project poses a critique of the concept of data, suggests an alternative concept, capta, and then explores some systematic means of creating visualizations rooted in humanistic approaches.< Less