Modern Data Science with R

Filename: modern-data-science-with-r.pdf
ISBN: 9781498724494
Release Date: 2017-03-16
Number of pages: 582
Author: Benjamin S. Baumer
Publisher: CRC Press

Download and read online Modern Data Science with R in PDF and EPUB Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling statistical questions. Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses.


Data Science f r Dummies

Filename: data-science-f-r-dummies.pdf
ISBN: 9783527806751
Release Date: 2016-04-22
Number of pages: 382
Author: Lillian Pierson
Publisher: John Wiley & Sons

Download and read online Data Science f r Dummies in PDF and EPUB Daten, Daten, Daten ?, Sie haben schon Kenntnisse in Excel und Statistik, wissen aber noch nicht, wie all die Datens?tze helfen sollen, bessere Entscheidungen zu treffen? Von Lillian Pierson bekommen Sie das daf?r notwendige Handwerkszeug: Bauen Sie Ihre Kenntnisse in Statistik, Programmierung und Visualisierung aus. Nutzen Sie Python, R, SQL, Excel und KNIME. Zahlreiche Beispiele veranschaulichen die vorgestellten Methoden und Techniken. So k?nnen Sie die Erkenntnisse dieses Buches auf Ihre Daten ?bertragen und aus deren Analyse unmittelbare Schl?sse und Konsequenzen ziehen.


Data Science

Filename: data-science.pdf
ISBN: 9783743936294
Release Date: 2017-07-10
Number of pages: 208
Author: Michael Oettinger
Publisher: tredition

Download and read online Data Science in PDF and EPUB Data Scientisten (m/w) sind derzeit auf dem Jobmarkt heißbegehrt. In Amerika sind erfahrene Data Scientisten so beliebt wie eine Getränkebude in der Wüste. Aber auch in Deutschland ist eine steigende Nachfrage nach diesem Skillprofil erkennbar. Immer mehr Unternehmen bauen "Analytics"-Abteilungen auf bzw. aus und suchen entsprechende Mitarbeiter. Nur: was macht eigentlich ein Data Scientist? Irgendetwas mit künstlicher Intelligenz, Machine Learning, Data-Mining, Python-Programmierung und Big Data. So genau weiß es eigentlich niemand ... Das Buch ist eine Einführung und Übersicht über das weitumfassende Themengebiet Data Science. Es werden die Datenquellen (Datenbanken, Data-Warehouse, Hadoop etc.) und die Softwareprodukte für die Datenanalyse vorgestellt (Data-Science-Plattformen, ML Bibliotheken). Die wichtigsten Verfahren des Machine Learnings werden ebenso behandelt wie beispielhafte Anwendungsfälle aus verschiedenen Branchen.


Data Science For Dummies

Filename: data-science-for-dummies.pdf
ISBN: 9781119327653
Release Date: 2017-02-21
Number of pages: 384
Author: Lillian Pierson
Publisher: John Wiley & Sons

Download and read online Data Science For Dummies in PDF and EPUB Your ticket to breaking into the field of data science! Jobs in data science are projected to outpace the number of people with data science skills—making those with the knowledge to fill a data science position a hot commodity in the coming years. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of an organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.


What Is Data Science

Filename: what-is-data-science.pdf
ISBN: 9781449336097
Release Date: 2011-04-10
Number of pages: 22
Author: Mike Loukides
Publisher: "O'Reilly Media, Inc."

Download and read online What Is Data Science in PDF and EPUB We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of "data-driven apps." Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by "data science." A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products.


Data Science f r Unternehmen

Filename: data-science-f-r-unternehmen.pdf
ISBN: 9783958455481
Release Date: 2017-10-25
Number of pages: 432
Author: Foster Provost
Publisher: MITP-Verlags GmbH & Co. KG

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Parallel Computing for Data Science

Filename: parallel-computing-for-data-science.pdf
ISBN: 9781466587038
Release Date: 2015-06-04
Number of pages: 328
Author: Norman Matloff
Publisher: CRC Press

Download and read online Parallel Computing for Data Science in PDF and EPUB Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic "n observations, p variables" matrix format but also from time series, network graph models, and numerous other structures common in data science. The examples illustrate the range of issues encountered in parallel programming. With the main focus on computation, the book shows how to compute on three types of platforms: multicore systems, clusters, and graphics processing units (GPUs). It also discusses software packages that span more than one type of hardware and can be used from more than one type of programming language. Readers will find that the foundation established in this book will generalize well to other languages, such as Python and Julia.


Process Mining

Filename: process-mining.pdf
ISBN: 9783662498514
Release Date: 2016-04-15
Number of pages: 467
Author: Wil M.P. van der Aalst
Publisher: Springer

Download and read online Process Mining in PDF and EPUB This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.


Data Science mit Python f r Dummies

Filename: data-science-mit-python-f-r-dummies.pdf
ISBN: 3527712089
Release Date: 2016
Number of pages: 432
Author: John Paul Mueller
Publisher: John Wiley & Sons

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Machine Learning mit Python

Filename: machine-learning-mit-python.pdf
ISBN: 9783958454248
Release Date: 2016-11-22
Number of pages: 424
Author: Sebastian Raschka
Publisher: MITP-Verlags GmbH & Co. KG

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Data Science mit Python f r Dummies

Filename: data-science-mit-python-f-r-dummies.pdf
ISBN: 9783527807147
Release Date: 2016-05-05
Number of pages: 424
Author: John Paul Mueller
Publisher: John Wiley & Sons

Download and read online Data Science mit Python f r Dummies in PDF and EPUB Sie wollen sich ernsthaft mit wissenschaftlicher Datenanalyse besch?ftigen und wissen, dass Sie da an Python nur schwer vorbeikommen? Dann ist dieses das richtige Buch f?r Sie. John Paul Mueller erkl?rt Ihnen, was Sie in Python beherrschen m?ssen, um sich der Datenanalyse zu widmen inklusive Objekten, Funktionen, Modulen und Bibliotheken. Au?erdem erl?utert er die wichtigsten Bibliotheken f?r die Datenanalyse wie NumPy, SciPy, BeautifulSoup, Pandas, und MatPlobLib. So lernen Sie Python f?r die Datenanalyse richtig einsetzen.


Principles of Data Science

Filename: principles-of-data-science.pdf
ISBN: 9781785888922
Release Date: 2016-12-16
Number of pages: 388
Author: Sinan Ozdemir
Publisher: Packt Publishing Ltd

Download and read online Principles of Data Science in PDF and EPUB 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.


Python Data Science Essentials

Filename: python-data-science-essentials.pdf
ISBN: 9781786462831
Release Date: 2016-10-28
Number of pages: 378
Author: Alberto Boschetti
Publisher: Packt Publishing Ltd

Download and read online Python Data Science Essentials in PDF and EPUB Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.


Data Science and Big Data An Environment of Computational Intelligence

Filename: data-science-and-big-data-an-environment-of-computational-intelligence.pdf
ISBN: 9783319534749
Release Date: 2017-04-17
Number of pages: 303
Author: Witold Pedrycz
Publisher: Springer

Download and read online Data Science and Big Data An Environment of Computational Intelligence in PDF and EPUB This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business.Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today’s knowledge-driven economy.Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address data analytics needs.The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.


Data Science from Scratch

Filename: data-science-from-scratch.pdf
ISBN: 9781491904398
Release Date: 2015-04-14
Number of pages: 330
Author: Joel Grus
Publisher: "O'Reilly Media, Inc."

Download and read online Data Science from Scratch in PDF and EPUB 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