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| data science and libraries: Data Science for Librarians Yunfei Du, Hammad Rauf Khan, 2020-03-26 More data, more problems -- A new strand of librarianship -- Data creation and collection -- Data for the academic librarian -- Research data services and the library ecosystem -- Data sources -- Data curation (archiving/preservation) -- Data storage, management, and retrieval -- Data analysis and visualization -- Data ethics and policies -- Data for public libraries and special libraries -- Conclusion: library, information, and data science. |
| data science and libraries: Data Science in the Library Joel Herndon, 2022 This book considers the current environment for data driven research, instruction, and consultation from a variety of faculty and library perspectives and suggests strategies for engaging with the tools and methods of data driven research. |
| data science and libraries: Data Science for Librarians Yunfei Du, Hammad Rauf Khan, 2020-03-26 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. Such skills 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. |
| data science and libraries: Data Science in the Library Joel Herndon, 2021-08-26 This book explores the rapid expansion of data sources, visualizations, and analytics created in the last decade and explores the strategies, tools, and approaches that educators and information specialists are employing to train a new generation of data professionals. |
| data science and libraries: Data Science from Scratch Joel Grus, 2015-04-14 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 |
| data science and libraries: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data science and libraries: Practical Data Science for Information Professionals David Stuart, 2020-07-24 Practical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, providing readers with an overview of data science and a framework for its application. It provides detailed examples and analysis on real data sets to explore the basics of the subject in three principle areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining. As well as highlighting a wealth of user-friendly data science tools, the book also includes some example code in two of the most popular programming languages (R and Python) to demonstrate the ease with which the information professional can move beyond the graphical user interface and achieve significant analysis with just a few lines of code. After reading, readers will understand: · the growing importance of data science · the role of the information professional in data science · some of the most important tools and methods that information professionals can use. Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals will provide a practical introduction to the topic specifically designed for the information community. It will appeal to librarians and information professionals all around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to use the lessons learned within. |
| data science and libraries: Introducing Data Science Davy Cielen, Arno Meysman, 2016-05-02 Summary Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started. About the Book Introducing Data ScienceIntroducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. What’s Inside Handling large data Introduction to machine learning Using Python to work with data Writing data science algorithms About the Reader This book assumes you're comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required. About the Authors Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors. Table of Contents Data science in a big data world The data science process Machine learning Handling large data on a single computer First steps in big data Join the NoSQL movement The rise of graph databases Text mining and text analytics Data visualization to the end user |
| data science and libraries: Graph Algorithms Mark Needham, Amy E. Hodler, 2019-05-16 Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark |
| data science and libraries: The Data Librarian’s Handbook Robin Rice, John Southall, 2016-12-20 An insider’s guide to data librarianship packed full of practical examples and advice for any library and information professional learning to deal with data. Interest in data has been growing in recent years. Support for this peculiar class of digital information – its use, preservation and curation, and how to support researchers’ production and consumption of it in ever greater volumes to create new knowledge, is needed more than ever. Many librarians and information professionals are finding their working life is pulling them toward data support or research data management but lack the skills required. The Data Librarian’s Handbook, written by two data librarians with over 30 years’ combined experience, unpicks the everyday role of the data librarian and offers practical guidance on how to collect, curate and crunch data for economic, social and scientific purposes. With contemporary case studies from a range of institutions and disciplines, tips for best practice, study aids and links to key resources, this book is a must-read for all new entrants to the field, library and information students and working professionals. Key topics covered include: • the evolution of data libraries and data archives • handling data compared to other forms of information • managing and curating data to ensure effective use and longevity • how to incorporate data literacy into mainstream library instruction and information literacy training • how to develop an effective institutional research data management (RDM) policy and infrastructure • how to support and review a data management plan (DMP) for a project, a key requirement for most research funders • approaches for developing, managing and promoting data repositories • handling and sharing confidential or sensitive data • supporting open scholarship and open science, ensuring data are discoverable, accessible, intelligible and assessable. This title is for the practising data librarian, possibly new in their post with little experience of providing data support. It is also for managers and policy-makers, public service librarians, research data management coordinators and data support staff. It will also appeal to students and lecturers in iSchools and other library and information degree programmes where academic research support is taught. |
| data science and libraries: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout. |
| data science and libraries: R Data Science Quick Reference Thomas Mailund, 2019-08-07 In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. What You Will LearnImport data with readrWork with categories using forcats, time and dates with lubridate, and strings with stringrFormat data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applicationsVisualize data with ggplot2 and fit data to models using modelr Who This Book Is For Programmers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended. |
| data science and libraries: Mining Social Media Lam Thuy Vo, 2019-11-25 BuzzFeed News Senior Reporter Lam Thuy Vo explains how to mine, process, and analyze data from the social web in meaningful ways with the Python programming language. Did fake Twitter accounts help sway a presidential election? What can Facebook and Reddit archives tell us about human behavior? In Mining Social Media, senior BuzzFeed reporter Lam Thuy Vo shows you how to use Python and key data analysis tools to find the stories buried in social media. Whether you're a professional journalist, an academic researcher, or a citizen investigator, you'll learn how to use technical tools to collect and analyze data from social media sources to build compelling, data-driven stories. Learn how to: Write Python scripts and use APIs to gather data from the social web Download data archives and dig through them for insights Inspect HTML downloaded from websites for useful content Format, aggregate, sort, and filter your collected data using Google Sheets Create data visualizations to illustrate your discoveries Perform advanced data analysis using Python, Jupyter Notebooks, and the pandas library Apply what you've learned to research topics on your own Social media is filled with thousands of hidden stories just waiting to be told. Learn to use the data-sleuthing tools that professionals use to write your own data-driven stories. |
| data science and libraries: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| data science and libraries: Hands-On Data Science for Librarians Sarah Lin, Dorris Scott, 2023 Librarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping, working with maps, creating interactive reports, machine learning, and others. While there's a place for theory, ethics, and statistical methods, librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work, no matter what type of library they work at (academic, public or special). By walking through each skill and its application to library work before walking the reader through each line of code, this book will support librarians who want to apply data science in their daily work. Hands-On Data Science for Librarians is intended for librarians (and other information professionals) in any library type (public, academic or special) as well as graduate students in library and information science (LIS)-- |
| data science and libraries: Data Science John D. Kelleher, Brendan Tierney, 2018-04-13 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 and libraries: Databrarianship Lynda M. Kellam, Kristi Thompson, 2016 With the appearance of big data, open data, and particularly research data curation on many libraries' radar screens, data service has become a critically important topic for academic libraries. Drawing on the expertise of a diverse community of practitioners, this collection of case studies, original research, survey chapters, and theoretical explorations presents a wide-ranging look at the field of academic data librarianship. By covering the data lifecycle from collection development to preservation, examining the challenges of working with different forms of data, and exploring service models suited to a variety of library types, this volume provides a toolbox of strategies that will allow librarians and administrators to respond creatively and effectively to the data deluge. Edited by Kristi Thompson and Lynda Kellam, Databrarianship: The Academic Data Librarian in Theory and Practice provides advice and insight on data services for all types of academic libraries and will be of interest to library educators--Publisher's website. |
| data science and libraries: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. 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, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data science and libraries: The Accidental Data Scientist Amy L. Affelt, 2015 Amy Affelt, author of The Accidental Data Scientist, notes that Librarians and information professionals have always worked with data in order to meet the information needs of their constituents, thus 'Big Data' is not a new concept for them. With The Accidental Data Scientist, Amy Affelt shows information professionals how to leverage their skills and training to master emerging tools, techniques, and vocabulary; create mission-critical Big Data research deliverables; and discover rewarding new career opportunities by embracing their inner Data Scientist. |
| data science and libraries: Data Science Bookcamp Leonard Apeltsin, 2021-12-07 Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution |
| data science and libraries: Digital Libraries: The Era of Big Data and Data Science Michelangelo Ceci, Stefano Ferilli, Antonella Poggi, 2020-01-22 This book constitutes the thoroughly refereed proceedings of the 16th Italian Research Conference on Digital Libraries, IRCDL 2020, held in Bari, Italy, in January 2020. The 12 full papers and 6 short papers presented were carefully selected from 26 submissions. The papers are organized in topical sections on information retrieval, bid data and data science in DL; cultural heritage; open science. |
| data science and libraries: Python for Data Science For Dummies John Paul Mueller, Luca Massaron, 2015-06-23 Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover. |
| data science and libraries: Artificial Intelligence and Machine Learning in Libraries Jason Griffey, 2019-01-01 This issue of Library Technology Reports argues that the near future of library work will be enormously impacted and perhaps forever changed as a result of artificial intelligence (AI) and machine learning systems becoming commonplace. |
| data science and libraries: A First Course in Machine Learning Simon Rogers, Mark Girolami, 2016-10-14 Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/ |
| data science and libraries: The Crystal Ball Instruction Manual, Volume One Stephen Davies, 2020-08-10 A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks. This engaging read will allow any dedicated learner to build the skills necessary to contribute to the Data Science revolution, regardless of background. |
| data science and libraries: Data Management for Libraries Laura Krier, Carly A. Strasser, 2014 Since the National Science Foundation joined the National Institutes of Health in requiring that grant proposals include a data management plan, academic librarians have been inundated with related requests from faculty and campus-based grant consulting offices. Data management is a new service area for many library staff, requiring careful planning and implementation. This guide offers a start-to-finish primer on understanding, building, and maintaining a data management service, showing another way the academic library can be invaluable to researchers. Krier and Strasser of the California Digital Library guide readers through every step of a data management plan by Offering convincing arguments to persuade researchers to create a data management plan, with advice on collaborating with them Laying out all the foundations of starting a service, complete with sample data librarian job descriptions and data management plans Providing tips for conducting successful data management interviews Leading readers through making decisions about repositories and other infrastructure Addressing sensitive questions such as ownership, intellectual property, sharing and access, metadata, and preservation This LITA guide will help academic librarians work with researchers, faculty, and other stakeholders to effectively organize, preserve, and provide access to research data. |
| data science and libraries: Data Science Live Book Pablo Casas, 2018-03-16 This book is a practical guide to problems that commonly arise when developing a machine learning project. The book's topics are: Exploratory data analysis Data Preparation Selecting best variables Assessing Model Performance More information on predictive modeling will be included soon. This book tries to demonstrate what it says with short and well-explained examples. This is valid for both theoretical and practical aspects (through comments in the code). This book, as well as the development of a data project, is not linear. The chapters are related among them. For example, the missing values chapter can lead to the cardinality reduction in categorical variables. Or you can read the data type chapter and then change the way you deal with missing values. You¿ll find references to other websites so you can expand your study, this book is just another step in the learning journey. It's open-source and can be found at http://livebook.datascienceheroes.com |
| data science and libraries: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 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 process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing 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. |
| data science and libraries: The Essentials of Data Science: Knowledge Discovery Using R Graham J. Williams, 2017-07-28 The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book. |
| data science and libraries: A Librarian's Guide to Graphs, Data and the Semantic Web James Powell, 2015-07-09 Graphs are about connections, and are an important part of our connected and data-driven world. A Librarian's Guide to Graphs, Data and the Semantic Web is geared toward library and information science professionals, including librarians, software developers and information systems architects who want to understand the fundamentals of graph theory, how it is used to represent and explore data, and how it relates to the semantic web. This title provides a firm grounding in the field at a level suitable for a broad audience, with an emphasis on open source solutions and what problems these tools solve at a conceptual level, with minimal emphasis on algorithms or mathematics. The text will also be of special interest to data science librarians and data professionals, since it introduces many graph theory concepts by exploring data-driven networks from various scientific disciplines. The first two chapters consider graphs in theory and the science of networks, before the following chapters cover networks in various disciplines. Remaining chapters move on to library networks, graph tools, graph analysis libraries, information problems and network solutions, and semantic graphs and the semantic web. - Provides an accessible introduction to network science that is suitable for a broad audience - Devotes several chapters to a survey of how graph theory has been used in a number of scientific data-driven disciplines - Explores how graph theory could aid library and information scientists |
| data science and libraries: Reference and Information Services Kay Ann Cassell, Uma Hiremath, 2013 Search skills of today bear little resemblance to searches through print publications. Reference service has become much more complex than in the past, and is in a constant state of flux. Learning the skill sets of a worthy reference librarian can be challenging, unending, rewarding, and-- yes, fun. |
| data science and libraries: Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems Mani, Nandita S., Cawley, Michelle A., 2022-05-06 Beyond providing space for data science activities, academic libraries are often overlooked in the data science landscape that is emerging at academic research institutions. Although some academic libraries are collaborating in specific ways in a small subset of institutions, there is much untapped potential for developing partnerships. As library and information science roles continue to evolve to be more data-centric and interdisciplinary, and as research using a variety of data types continues to proliferate, it is imperative to further explore the dynamics between libraries and the data science ecosystems in which they are a part. The Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems provides a global perspective on current and future trends concerning the integration of data science in libraries. It provides both a foundational base of knowledge around data science and explores numerous ways academicians can reskill their staff, engage in the research enterprise, contribute to curriculum development, and help build a stronger ecosystem where libraries are part of data science. Covering topics such as data science initiatives, digital humanities, and student engagement, this book is an indispensable resource for librarians, information professionals, academic institutions, researchers, academic libraries, and academicians. |
| data science and libraries: Library Improvement Through Data Analytics Lesley S. J. Farmer, Alan M. Safer, 2016-07-27 This book shows how to act on and make sense of data in libraries. Using a range of techniques, tools and methodologies it explains how data can be used to help inform decision making at every level.Sound data analytics is the foundation for making an evidence-based case for libraries, in addition to guiding myriad organizational decisions, from optimizing operations for efficiency to responding to community needs. Designed to be useful for beginners as well as those with a background in data, this book introduces the basics of a six point framework that can be applied to a variety of library settings for effective system based, data-driven management. Library Improvement Through Data Analytics includes:- the basics of statistical concepts- recommended data sources for various library functions and processes, and guidance for using census, university, or - - government data in analysis- techniques for cleaning data- matching data to appropriate data analysis methods- how to make descriptive statistics more powerful by spotlighting relationships- 14 practical case studies, covering topics such as access and retrieval, digitization, e-book collection development, staffing, facilities, and instruction.This book's clear, concise coverage will enable librarians, archivists, curators and technologists of every experience level to gain a better understanding of statistics in order to facilitate library improvement. |
| data science and libraries: Data Science for Librarians Yunfei Du, Hammad Rauf Khan, 2020-03-26 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. Such skills 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. |
| data science and libraries: Big Data Shocks Andrew Weiss, 2018 Big Data Shocks examines the roots of big data, the current climate and rising stars in this world. The book explores the issues raised by big data and discusses theoretical as well as practical approaches to managing information whose scope exists beyond the human scale. |
| data science and libraries: Curating Research Data Lisa R. Johnston, 2016-11-01 Data are becoming the proverbial coin of the digital realm: a research commodity that might purchase reputation credit in a disciplinary culture of data sharing, or buy transparency when faced with funding agency mandates or publisher scrutiny. Unlike most monetary systems, however, digital data can flow in all too great an abundance. Not only does this currency actually grow on trees, but it comes from animals, books, thoughts, and each of us! And that is what makes data curation so essential. The abundance of digital research data challenges library and information science professionals to harness this flow of information streaming from research discovery and scholarly pursuit and preserve the unique evidence for future use. Volume One of Curating Research Data explores the variety of reasons, motivations, and drivers for why data curation services are needed in the context of academic and disciplinary data repository efforts. Twelve chapters, divided into three parts, take an in-depth look at the complex practice of data curation as it emerges around us. Part I sets the stage for data curation by describing current policies, data sharing cultures, and collaborative efforts currently underway that impact potential services. Part II brings several key issues, such as cost recovery and marketing strategy, into focus for practitioners when considering how to put data curation services in action. Finally, Part III describes the full lifecycle of data by examining the ethical and practical reuse issues that data curation practitioners must consider as we strive to prepare data for the future. Digital data is ubiquitous and rapidly reshaping how scholarship progresses now and into the future. The information expertise of librarians can help ensure the resiliency of digital data, and the information it represents, by addressing how the meaning, integrity, and provenance of digital data generated by researchers today will be captured and conveyed to future researchers. |
| data science and libraries: A Python Data Analyst’s Toolkit Gayathri Rajagopalan, 2021-02-21 Explore the fundamentals of data analysis, and statistics with case studies using Python. This book will show you how to confidently write code in Python, and use various Python libraries and functions for analyzing any dataset. The code is presented in Jupyter notebooks that can further be adapted and extended. This book is divided into three parts – programming with Python, data analysis and visualization, and statistics. You'll start with an introduction to Python – the syntax, functions, conditional statements, data types, and different types of containers. You'll then review more advanced concepts like regular expressions, handling of files, and solving mathematical problems with Python. The second part of the book, will cover Python libraries used for data analysis. There will be an introductory chapter covering basic concepts and terminology, and one chapter each on NumPy(the scientific computation library), Pandas (the data wrangling library) and visualization libraries like Matplotlib and Seaborn. Case studies will be included as examples to help readers understand some real-world applications of data analysis. The final chapters of book focus on statistics, elucidating important principles in statistics that are relevant to data science. These topics include probability, Bayes theorem, permutations and combinations, and hypothesis testing (ANOVA, Chi-squared test, z-test, and t-test), and how the Scipy library enables simplification of tedious calculations involved in statistics. What You'll Learn Further your programming and analytical skills with Python Solve mathematical problems in calculus, and set theory and algebra with Python Work with various libraries in Python to structure, analyze, and visualize data Tackle real-life case studies using Python Review essential statistical concepts and use the Scipy library to solve problems in statistics Who This Book Is For Professionals working in the field of data science interested in enhancing skills in Python, data analysis and statistics. |
| data science and libraries: Programming Skills For Data Science Freeman, Programming Skills for Data Science brings together all the foundation skills needed to transform raw data into actionable insights for domains ranging from urban planning to precision medicine, even if you have no programming or data science experience. Guided by expert instructors Michael Freeman and Joel Ross, this book will help learners install the tools required to solve professional-level data science problems, including widely used R language, RStudio integrated development environment, and Git version-control system. It explains how to wrangle data into a form where it can be easily used, analyzed, and visualized so others can see the patterns uncovered. Step by step, students will master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. |
| data science and libraries: JavaScript for Data Science Maya Gans, Toby Hodges, Greg Wilson, 2020 JavaScript is the language of the web. Originally developed for making browser-based interfaces more dynamic, it is now used for large-scale software projects of all kinds, including scientific visualization tools and data services. However, most researchers and data scientists have little or no experience with it. This book is designed to fill that void. It introduces readers to JavaScript's power and idiosyncrasies, and guides them through the key features of the modern version of the language and its tools and libraries. The book places equal focus on client- and server-side programming, and shows readers how to create interactive web content, build and test data services, and visualize data in the browser-- |
| data science and libraries: Data Science for Librarians Jason Miller, 2024-05-07 Discover the transformative potential of data science in the world of libraries with this comprehensive guide tailored specifically for librarians seeking to enhance their professional expertise. Delving into the intersection of information science and cutting-edge data analytics, this book equips readers with the knowledge and skills needed to harness the power of data for informed decision-making and innovative service delivery. From understanding the fundamentals of data science to implementing advanced techniques like machine learning and text mining, each chapter offers practical insights and real-world examples that illuminate the path forward. Readers will learn how to collect, clean, and analyze data effectively, uncovering valuable insights that can drive strategic initiatives and optimize library resources. But this book is more than just a technical manual-it's a roadmap for librarians navigating the complexities of the digital age. With a focus on ethical considerations, privacy protection, and staying ahead of emerging trends, it empowers librarians to leverage data responsibly and ethically, ensuring that their practices uphold the core values of librarianship. Whether you're a seasoned professional looking to expand your skill set or a newcomer eager to explore the possibilities of data science, this book is your indispensable companion on the journey to unlocking the full potential of libraries in the 21st century. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with minimum time …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, released in …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process from …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical barriers …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be collected, …
The Complete Collection of Data Science Cheat Sheets
learning and data science. Abid holds a master’s degree in Technology Management and a bachelor’s degree in Telecommunication Engineering. His vision is to build an AI product using …
Data Science from Scratch - Internet Archive
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. …
PyTond: Eficient Python Data Science on the Shoulders of …
PyTond: Efficient Python Data Science on the Shoulders of Databases Hesam Shahrokhi School of Informatics University of Edinburgh Edinburgh, United Kingdom hesam.shahrokhi@ed.ac.uk …
Mastering Data Science with C++: Performance and Innovation
3 1.3.3 Marketing and Customer Analytics. . . . . . . . . . . . . . . . . . .24 1.3.4 Autonomous Vehicles and Robotics. . . . . . . . . . . . . . . . . . .25
An Introduction to Python for Data Science - nimhd.nih.gov
data science libraries . are powerful. Examples include: o : Numpy - for linear algebra and high-level mathematical functions o . Pandas - for handling data structures and manipulating tables …
A Review on Python for Data Science, Machine Learning and …
Large Number of Libraries: Python has many libraries so that no specific code has to be written separately. 1.2 Python for Data Science Data Science is the field of study that combines …
Big Data Analytic Concepts in Libraries: A Systematic
Data Science Concepts and Libraries . IFLA (2018) stressed Data science operates across a continuum and can cover research involving profound mathematical and software engineering …
Data Science from Scratch
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. …
ROSES24 Open Source Tools, Frameworks, and Libraries
Science Data Officer Demitri Muna Program Officer ROSES24 Open Source Tools, Frameworks, and Libraries HQ-SMD-CSDO-ROSES@mail.nasa.gov . ... and Libraries Supplement for open …
NVIDIA DLI COURSE CATALOG
ACCELERATED DATA SCIENCE Fundamentals of Accelerated Data Science with RAPIDS Learn how to perform multiple analysis tasks on large data sets using RAPIDS, a collection of …
Numpy Handbook - GitHub
1. NumPy Introduction NumPy is the core library for scientific computing in Python. The central object in the NumPy library is the NumPy array. The NumPy array is a high-performance …
Data Science from Scratch - Emory University
It has lots of useful data science–related libraries. I am hesitant to call Python my favorite programming language. There are other languages I find more pleasant, better-designed, or …
Libraries, Archives, and the Digital Humanities, Isabel Galina …
Jan 18, 2024 · archivists. Acute “skills and management gaps in libraries” have been recognized which highlight the need for greater automation in library work, the facilitation of computational …
Comparative Analysis of Data Visualization Libraries …
Ali Hassan Sial et al., International Journal of Advanced Trends in Computer Science and Engineering, 10(1), January –Volume 10, No.1, January February 2021, 277 - 281 277 …
Demystifying ``Bad'' Error Messages in Data Science Libraries
about this issue in the domain of data science. Yet, as data science is experiencing a phenomenal growth in recent years, developing and debugging data science programs have become an …
Biomedical Data Science BME 4760 Section 1422 Class …
1. Understand data science techniques in the biomedical domain, 2. Understand the limitations of each technique with respect to biomedical data, 3. Learn to use biomedical data science …
What is a Data Librarian?: A Content Analysis of Job …
sciences and they go on to assess the concepts used in data science to serve as a basis for data librarianship. Semeler et al. (2017) comment that data librarians “focus on disseminating the …
Course Description - IIT Bombay
Programming Basics (Python programming, R, Data Structures), Visualization/Plotting, Data Science Libraries (Pandas, PyPlot, matplotlib) Databases, GPUs/CUDA programming, …
Demystifying ``Bad'' Error Messages in Data Science Libraries
about this issue in the domain of data science. Yet, as data science is experiencing a phenomenal growth in recent years, developing and debugging data science programs have become an …
Understanding Performance Concerns in the API …
•The performance of popular data science libraries (e.g., pandas, numpy) is also vital for improving application efficiency and developer productivity Painfully slow execution time and …
Machine Learning in Python: Main Developments and …
computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into …
Data Science from Scratch - cdn.oreillystatic.com
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. …
NVIDIA DLI COURSE CATALOG
ACCELERATED DATA SCIENCE Fundamentals of Accelerated Data Science with RAPIDS Learn how to perform multiple analysis tasks on large data sets using RAPIDS, a collection of …
A DEMONSTRATION PROJECT - The Library of Congress
digital libraries, intelligent data analytics, and augmented description: a demonstration project final report prepared by elizabeth lorang, leen-kiat soh, yi liu, and chulwoo pack university libraries …
Machine Learning Tools & Libraries on Python - University of …
The following tools & libraries are to be updated sometime in the future by the writers: ... ordinary differential equation solvers and other tasks common in science and engineering. SciPy builds …
A Unified Data Infrastructure Architecture
Data Science Libraries (Spark, Pandas, NumPy, Dask) Experiment Tracking (Weights and Biases, Comet, MLflow) Model Registry (Algorithmia, MLflow, Sagemaker) Visualization …
Data Science with Groovy - Object Computing
Concise syntax including DSL friendly import java.util.List; import java.util.ArrayList; class Main {private List keepShorterThan(List strings, int length)
Empowering Future Professionals through a Data Fellowship …
• Increasing demand for data science skills in the workforce o According to the U.S. Bureau of Labor Statistics, data science field will grow about 28% through 2026 • FSU investments in …
Course Syllabus | Data Science & AI Certificate - Noble Desktop
Learn Python, SQL, automation, and machine learning to become a Data Scientist. Gain Python programming, data analysis, SQL querying, and predictive modeling skills. Perfect for …
Multidisciplinary Graduate Engineering Course Syllabus
Data Science libraries written for Python: NumPy, Pandas, SciPy, and Scikit-learn. This class gives you the fundamental knowledge for applying for jobs that involve data analysis, such as …
The Prospect of 5th Industrial Revolution and Academic …
A. The Link between 5th IR, Data Science and Academic Libraries Data librarians in academic libraries must understand how data science skills and techniques could be jointly utilised to …
1 arXiv:2208.09672v1 [cs.DB] 20 Aug 2022 - ResearchGate
Comparing graph data science libraries for querying and analysing datasets: towards data science queries on graphs? Genoveva Vargas-Solar1, Pierre Marrec2, and Mirian Halfeld Ferrari …
Foundational Python for Data Science - pearsoncmg.com
Python for Data Science Kennedy R. Behrman ... Data Science Libraries 83 7 NumPy85 Sci8 Py103 Pandas9 113 V10isualization Libraries 135 Machine Lear11 ning Libraries 153 Natural …
Automating Data Lineage and Pipeline Extraction - VLDB
user queries, and a data science task to generate pipelines. Yan and He propose a system for recommending data preparation steps [25]. Their system uses a dataset of 4 million notebooks …
SOAR: A Synthesis Approach for Data Science API …
School of Computer Science Carnegie Mellon University Pittsburgh, USA clegoues@cs.cmu.edu Abstract—With the growth of the open-source data science community, both the number of …
DATA 201 Database Technologies for Data Analytics
Department of Applied Data Science DATA 201 Database Technologies for Data Analytics Sections 21 and 71 Spring 2025 Course and Contact Information ... Jupyter notebook, and …
Comparing Graph Data Science Libraries for Querying and
Comparing Graph Data Science Libraries for Querying and Analysing Datasets: Towards Data Science Queries on Graphs Genoveva Vargas-Solar1(B), Pierre Marrec2, and Mirian Halfeld …
CONTENTS IN DETAIL - No Starch Press
Contents in Detail xi 5 WORKING WITH DATABASES 73 Relational Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .74
CSCI 127: Joy and Beauty of Data - Montana State University
• Description: Provides a gentle introduction to the exciting world of big data and data science. Students expand their ability to solve problems with Python by learning to deploy lists, files, …
DATA SCIENCE IN SCANNING PROBE MICROSCOPY: …
Jun 24, 2015 · iii ABSTRACT Scanning probe microscopy (SPM) has allowed researchers to measure materials structural and functional properties, such as atomic displacements and …
Roles and Responsibilities – Libraries, Librarians and Data
Within the UK, data-related library activity is largely represented by the local data libraries/support services established at the Universities of Edinburgh and Oxford in the 1980s and the London …
Data Visualization
Comparison b/t Matplotlib and Seaborn Python Libraries in Jupyter Notebook. Part 2 Interactive Visuals | Plotly, Bokeh, Tableau, etc. ... Data visualization is the graphical representation of ...
Boa Meets Python: A Boa Dataset of Data Science Software in …
have filtered the projects that perform Data Science tasks. For this filtering, we have applied two methods: 1) search for Data Science related keywords in the description of the project, 2) …
A New PyROOT: Modern, Interoperable and More Pythonic
ity with Python data science libraries by making it possible to read ROOT data into NumPy and pandas. Last, as a response to a common request from our users, the build system of …
Using MATLAB and Python Together - MathWorks
Data types will be automatically converted where possible. Some MATLAB data types need to be converted. Note: The default numeric type is integer in Python and double in MATLAB when …
Comparative Analysis of Data Visualization Libraries …
field of expertise, named ‘Data Science’. This paper focuses on the comparative study and evaluation of the data science libraries used in Python Programming Languages, named …
Purdue University Libraries and School of Information Studies …
May 5, 2020 · 7 Data Science - Spring 2020 Purdue University Libraries and School of Information Studies 8 S ince the beginning of this century (and even before), geospatial …
Digital Libraries: The Era of Big Data and Data Science
information retrieval, big data and data science in digital libraries, digital libraries for cultural heritage, and open science. The papers were selected after a rigorous evaluation of a …
Python Data Science - 103.203.175.90:81
Chapter 4: The Best Python Libraries for Data Science, and How They Can Help Get the Job Done NumPy SciPy Pandas Matplotlib Scikit-Learn Theano TensorFlow Keras ... Data science …
Joel Grus Data Science From Scratch - obiemaps.oberlin.edu
data science How commonly-used data science techniques work (learning by implementing them) What is Map-Reduce and how to do it in Python Other applications such as NLP, Network …
