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# Data Science

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**📒Practical Statistics For Data Scientists ✍ Peter Bruce**

**Practical Statistics for Data Scientists**

✏Author :

**Peter Bruce**

✏Publisher :

**O'Reilly Media**

✏Release Date :

**2020-04-10**

✏Pages :

**368**

✏ISBN :

**9781492072911**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Practical Statistics for Data Scientists Book Summary :** 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

**📒Python Real World Data Science ✍ Dusty Phillips**

**Python Real World Data Science**

✏Author :

**Dusty Phillips**

✏Publisher :

**Packt Publishing Ltd**

✏Release Date :

**2016-06-10**

✏Pages :

**1255**

✏ISBN :

**9781786468413**

✏Available Language :

**English, Spanish, And French**

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**✏Python Real World Data Science Book Summary :** Unleash the power of Python and its robust data science capabilities About This Book Unleash the power of Python 3 objects Learn to use powerful Python libraries for effective data processing and analysis Harness the power of Python to analyze data and create insightful predictive models Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Who This Book Is For Entry-level analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Python's data science capabilities for doing real-world data analysis. What You Will Learn Install and setup Python Implement objects in Python by creating classes and defining methods Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis Create effective visualizations for presenting your data using Matplotlib Process and analyze data using the time series capabilities of pandas Interact with different kind of database systems, such as file, disk format, Mongo, and Redis Apply data mining concepts to real-world problems Compute on big data, including real-time data from the Internet Explore how to use different machine learning models to ask different questions of your data In Detail The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you'll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it's time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls. Style and approach This course includes all the resources that will help you jump into the data science field with Python and learn how to make sense of data. The aim is to create a smooth learning path that will teach you how to get started with powerful Python libraries and perform various data science techniques in depth.

**📒Data Science ✍ Herbert Jones**

**Data Science**

✏Author :

**Herbert Jones**

✏Publisher :

✏Release Date :

**2020-01-03**

✏Pages :

**134**

✏ISBN :

**1647483042**

✏Available Language :

**English, Spanish, And French**

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**✏Data Science Book Summary :** 2 comprehensive manuscripts in 1 book Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data - That You Don't Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying

**📒Data Science For Undergraduates ✍ National Academies of Sciences, Engineering, and Medicine**

**Data Science for Undergraduates**

✏Author :

**National Academies of Sciences, Engineering, and Medicine**

✏Publisher :

**National Academies Press**

✏Release Date :

**2018-11-11**

✏Pages :

**138**

✏ISBN :

**9780309475594**

✏Available Language :

**English, Spanish, And French**

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**✏Data Science for Undergraduates Book Summary :** Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

**📒R For Data Science ✍ Hadley Wickham**

**R for Data Science**

✏Author :

**Hadley Wickham**

✏Publisher :

**"O'Reilly Media, Inc."**

✏Release Date :

**2016-12-12**

✏Pages :

**492**

✏ISBN :

**9781491910368**

✏Available Language :

**English, Spanish, And French**

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**✏R for Data Science Book Summary :** "This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"--

**📒Python For Data Science For Dummies ✍ John Paul Mueller**

**Python for Data Science For Dummies**

✏Author :

**John Paul Mueller**

✏Publisher :

**John Wiley & Sons**

✏Release Date :

**2019-02-27**

✏Pages :

**496**

✏ISBN :

**9781119547624**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Python for Data Science For Dummies Book Summary :** The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

**📒Data Science For Librarians ✍ Yunfei Du**

**Data Science for Librarians**

✏Author :

**Yunfei Du**

✏Publisher :

**ABC-CLIO**

✏Release Date :

**2020-03-26**

✏Pages :

**160**

✏ISBN :

**9781440871221**

✏Available Language :

**English, Spanish, And French**

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**✏Data Science for Librarians Book Summary :** This unique textbook intersects traditional library science with data science principles that readers will find useful in implementing or improving data services within their libraries. Data Science for Librarians introduces data science to students and practitioners in library services. Writing for academic, public, and school library managers; library science students; and library and information science educators, authors Yunfei Du and Hammad Rauf Khan provide a thorough overview of conceptual and practical tools for data librarian practice. Partially due to how quickly data science evolves, libraries have yet to recognize core competencies and skills required to perform the job duties of a data librarian. As society transitions from the information age into the era of big data, librarians and information professionals require new knowledge and skills to stay current and take on new job roles, such as data librarianship. Skills such as data curation, research data management, statistical analysis, business analytics, visualization, smart city data, and learning analytics are relevant in library services today and will become increasingly so in the near future. This text serves as a tool for library and information science students and educators working on data science curriculum design. Reviews fundamental concepts and principles of data science Offers a practical overview of tools and software Highlights skills and services needed in the 21st-century academic library Covers the entire research data life cycle and the librarian's role at each stage Provides insight into how library science and data science intersect

**📒Introduction To Data Science ✍ Rafael A. Irizarry**

**Introduction to Data Science**

✏Author :

**Rafael A. Irizarry**

✏Publisher :

**CRC Press**

✏Release Date :

**2019-11-20**

✏Pages :

**713**

✏ISBN :

**9781000708035**

✏Available Language :

**English, Spanish, And French**

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**✏Introduction to Data Science Book Summary :** Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

**📒Data Science From Scratch ✍ Joel Grus**

**Data Science from Scratch**

✏Author :

**Joel Grus**

✏Publisher :

**O'Reilly Media**

✏Release Date :

**2019-04-12**

✏Pages :

**406**

✏ISBN :

**9781492041108**

✏Available Language :

**English, Spanish, And French**

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**✏Data Science from Scratch Book Summary :** 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.

**📒An Introduction To Data Science ✍ Jeffrey S. Saltz**

**An Introduction to Data Science**

✏Author :

**Jeffrey S. Saltz**

✏Publisher :

**SAGE Publications**

✏Release Date :

**2017-08-25**

✏Pages :

**288**

✏ISBN :

**9781506377513**

✏Available Language :

**English, Spanish, And French**

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**✏An Introduction to Data Science Book Summary :** An Introduction to Data Science by Jeffrey S. Saltz and Jeffrey M. Stanton is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science. Needing no prior coding experience or a deep understanding of statistics, this book uses the R programming language and RStudio® platform to make data science welcoming and accessible for all learners. After introducing the basics of data science, the book builds on each previous concept to explain R programming from the ground up. Readers will learn essential skills in data science through demonstrations of how to use data to construct models, predict outcomes, and visualize data.

**📒Data Science ✍ Vijay Kotu**

**Data Science**

✏Author :

**Vijay Kotu**

✏Publisher :

**Morgan Kaufmann**

✏Release Date :

**2018-11-27**

✏Pages :

**568**

✏ISBN :

**9780128147627**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Data Science Book Summary :** Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner

**📒Data Science Programming All In One For Dummies ✍ John Paul Mueller**

**Data Science Programming All In One For Dummies**

✏Author :

**John Paul Mueller**

✏Publisher :

**John Wiley & Sons**

✏Release Date :

**2020-01-09**

✏Pages :

**768**

✏ISBN :

**9781119626114**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Data Science Programming All In One For Dummies Book Summary :** Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!

**📒Data Science ✍ William Vance**

**Data Science**

✏Author :

**William Vance**

✏Publisher :

**joiningthedotstv**

✏Release Date :

**2020-07-24**

✏Pages :

**92**

✏ISBN :

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Data Science Book Summary :** This book will introduce you to the digital world. Data science is one of the most amazing and trending fields in the digital era. Data science is what makes us humans what we are today. Not limited to computer-driven technologies, this book will guide you to visualize the digital facts and connections of our brain with data science, how to draw conclusions from simple information, and how to develop patterns for understanding different solutions for a similar problem. But our brains can only take us so far when it comes to raw computing. Our brains can't keep up with the amount of data we can capture, and with the extent of our curiosity. So we turned towards machines that are able to capture and store terabytes of information and to do part of the work for us, like recognizing patterns, creating connections, and supplying us with accurate results. Data science is a field where you will be able to get to learn every modern technique. Keeping in mind all these facts, we thought of writing this book targeting the data science beginner. This book provides an overview of data science, teaching you: · What is data science, and how it has emerged · What are the responsibilities of a data scientist and the fundamentals of data science · Overall process with the life cycle of data science · How data science tools, like statistics, probability, etc. · Help to draw insights from data · Basic concept about data modeling, and featurization · How to work with data variables and data science tools · How to visualize the data · How to work with machine learning algorithms and Artificial Neural Networks · Concepts of decision trees and cloud computing. We have included everything a beginner needs to venture into the data science world. Don’t waste another second. Now is your chance to get started!

**📒Data Science Projects With Python ✍ Stephen Klosterman**

**Data Science Projects with Python**

✏Author :

**Stephen Klosterman**

✏Publisher :

**Packt Publishing Ltd**

✏Release Date :

**2019-04-30**

✏Pages :

**374**

✏ISBN :

**9781838552602**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Data Science Projects with Python Book Summary :** Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features Learn techniques to use data to identify the exact problem to be solved Visualize data using different graphs Identify how to select an appropriate algorithm for data extraction Book Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. By the end of this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learn Install the required packages to set up a data science coding environment Load data into a Jupyter Notebook running Python Use Matplotlib to create data visualizations Fit a model using scikit-learn Use lasso and ridge regression to reduce overfitting Fit and tune a random forest model and compare performance with logistic regression Create visuals using the output of the Jupyter Notebook Who this book is for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.

**📒Roundtable On Data Science Postsecondary Education ✍ National Academies of Sciences, Engineering, and Medicine**

**Roundtable on Data Science Postsecondary Education**

✏Author :

**National Academies of Sciences, Engineering, and Medicine**

✏Publisher :

**National Academies Press**

✏Release Date :

**2020-10-02**

✏Pages :

**223**

✏ISBN :

**9780309677707**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Roundtable on Data Science Postsecondary Education Book Summary :** Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.

**📒Practical Data Science ✍ Andreas François Vermeulen**

**Practical Data Science**

✏Author :

**Andreas François Vermeulen**

✏Publisher :

**Apress**

✏Release Date :

**2018-02-21**

✏Pages :

**805**

✏ISBN :

**9781484230541**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Practical Data Science Book Summary :** Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers

**📒Introduction To Biomedical Data Science ✍ Robert Hoyt**

**Introduction to Biomedical Data Science**

✏Author :

**Robert Hoyt**

✏Publisher :

**Lulu.com**

✏Release Date :

**2019-11-25**

✏Pages :

**258**

✏ISBN :

**9781794761735**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Introduction to Biomedical Data Science Book Summary :** Introduction to Biomedical Data Science aims to fill the data science knowledge gap experienced by many clinical, administrative and technical staff. The textbook begins with an overview of what biomedical data science is and then embarks on a tour of topics beginning with spreadsheet tips and tricks and ending with artificial intelligence. In between, important topics are covered such as biostatistics, data visualization, database systems, big data, programming languages, bioinformatics, and machine learning. The textbook is available as a paperback and ebook. Visit the companion website at https: //www.informaticseducation.org for more information. Key features: Real healthcare datasets are used for examples and exercises; Knowledge of a programming language or higher math is not required; Multiple free or open source software programs are presented; YouTube videos are embedded in most chapters; Extensive resources chapter for further reading and learning; PowerPoints and an Instructor Manual

**📒Data Science For Dummies ✍ Lillian Pierson**

**Data Science For Dummies**

✏Author :

**Lillian Pierson**

✏Publisher :

**John Wiley & Sons**

✏Release Date :

**2015-03-09**

✏Pages :

**408**

✏ISBN :

**9781118841556**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Data Science For Dummies Book Summary :** "Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles in organizations. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their 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."--Provided by publisher.

**Innovations in Classification Data Science and Information Systems**

✏Author :

**Daniel Baier**

✏Publisher :

**Springer Science & Business Media**

✏Release Date :

**2006-03-30**

✏Pages :

**616**

✏ISBN :

**9783540269816**

✏Available Language :

**English, Spanish, And French**

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**✏Innovations in Classification Data Science and Information Systems Book Summary :** The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques.

**📒Doing Data Science ✍ Cathy O'Neil**

**Doing Data Science**

✏Author :

**Cathy O'Neil**

✏Publisher :

**"O'Reilly Media, Inc."**

✏Release Date :

**2013-10-09**

✏Pages :

**408**

✏ISBN :

**9781449363901**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Doing Data Science Book Summary :** Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that's so clouded in hype? This insightful book, based on Columbia University's Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you're familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O'Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

**📒Building Data Science Teams ✍ DJ Patil**

**Building Data Science Teams**

✏Author :

**DJ Patil**

✏Publisher :

**"O'Reilly Media, Inc."**

✏Release Date :

**2011-09-15**

✏Pages :

**24**

✏ISBN :

**9781449316778**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Building Data Science Teams Book Summary :** As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success. Topics include: What it means to be "data driven." The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team.

**📒Data Science And Big Data Analytics ✍ EMC Education Services**

**Data Science and Big Data Analytics**

✏Author :

**EMC Education Services**

✏Publisher :

**John Wiley & Sons**

✏Release Date :

**2015-01-05**

✏Pages :

**432**

✏ISBN :

**9781118876053**

✏Available Language :

**English, Spanish, And French**

**Click Here To Get Book**

**✏Data Science and Big Data Analytics Book Summary :** Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Corresponding data sets are available at www.wiley.com/go/9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

**📒Beginning Data Science With R ✍ Manas A. Pathak**

**Beginning Data Science with R**

✏Author :

**Manas A. Pathak**

✏Publisher :

**Springer**

✏Release Date :

**2014-12-08**

✏Pages :

**157**

✏ISBN :

**9783319120669**

✏Available Language :

**English, Spanish, And French**

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**✏Beginning Data Science with R Book Summary :** “We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library. The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.

**📒What Is Data Science ✍ Mike Loukides**

**What Is Data Science**

✏Author :

**Mike Loukides**

✏Publisher :

**"O'Reilly Media, Inc."**

✏Release Date :

**2011-04-10**

✏Pages :

**22**

✏ISBN :

**9781449336097**

✏Available Language :

**English, Spanish, And French**

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**✏What Is Data Science Book Summary :** 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.

**📒Web And Network Data Science ✍ Thomas W. Miller**

**Web and Network Data Science**

✏Author :

**Thomas W. Miller**

✏Publisher :

**FT Press**

✏Release Date :

**2014-12-19**

✏Pages :

**384**

✏ISBN :

**9780133887648**

✏Available Language :

**English, Spanish, And French**

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**✏Web and Network Data Science Book Summary :** Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

**📒Data Science For Business ✍ Foster Provost**

**Data Science for Business**

✏Author :

**Foster Provost**

✏Publisher :

**"O'Reilly Media, Inc."**

✏Release Date :

**2013-07-27**

✏Pages :

**414**

✏ISBN :

**9781449374280**

✏Available Language :

**English, Spanish, And French**

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**✏Data Science for Business Book Summary :** 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

**📒Agile Data Science ✍ Russell Jurney**

**Agile Data Science**

✏Author :

**Russell Jurney**

✏Publisher :

**"O'Reilly Media, Inc."**

✏Release Date :

**2013-10-15**

✏Pages :

**178**

✏ISBN :

**9781449326920**

✏Available Language :

**English, Spanish, And French**

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**✏Agile Data Science Book Summary :** Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track

**📒Data Science At The Command Line ✍ Jeroen Janssens**

**Data Science at the Command Line**

✏Author :

**Jeroen Janssens**

✏Publisher :

**"O'Reilly Media, Inc."**

✏Release Date :

**2014-09-25**

✏Pages :

**212**

✏ISBN :

**9781491947821**

✏Available Language :

**English, Spanish, And French**

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**✏Data Science at the Command Line Book Summary :** This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools. Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on plain text, CSV, HTML/XML, and JSON Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow using Drake Create reusable tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines using GNU Parallel Model data with dimensionality reduction, clustering, regression, and classification algorithms

**📒Data Science And Simulation In Transportation Research ✍ Janssens, Davy**

**Data Science and Simulation in Transportation Research**

✏Author :

**Janssens, Davy**

✏Publisher :

**IGI Global**

✏Release Date :

**2013-12-31**

✏Pages :

**350**

✏ISBN :

**9781466649217**

✏Available Language :

**English, Spanish, And French**

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**✏Data Science and Simulation in Transportation Research Book Summary :** Given its effective techniques and theories from various sources and fields, data science is playing a vital role in transportation research and the consequences of the inevitable switch to electronic vehicles. This fundamental insight provides a step towards the solution of this important challenge. Data Science and Simulation in Transportation Research highlights entirely new and detailed spatial-temporal micro-simulation methodologies for human mobility and the emerging dynamics of our society. Bringing together novel ideas grounded in big data from various data mining and transportation science sources, this book is an essential tool for professionals, students, and researchers in the fields of transportation research and data mining.

**📒Practical Data Science Cookbook ✍ Tony Ojeda**

**Practical Data Science Cookbook**

✏Author :

**Tony Ojeda**

✏Publisher :

**Packt Publishing Ltd**

✏Release Date :

**2014-09-25**

✏Pages :

**396**

✏ISBN :

**9781783980253**

✏Available Language :

**English, Spanish, And French**

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**✏Practical Data Science Cookbook Book Summary :** If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of data science projects, the steps in the data science pipeline, and the programming examples presented in this book. Since the book is formatted to walk you through the projects with examples and explanations along the way, no prior programming experience is required.