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| data science internship program: Hadoop For Dummies Dirk deRoos, 2014-04-14 Let Hadoop For Dummies help harness the power of your data and rein in the information overload Big data has become big business, and companies and organizations of all sizes are struggling to find ways to retrieve valuable information from their massive data sets with becoming overwhelmed. Enter Hadoop and this easy-to-understand For Dummies guide. Hadoop For Dummies helps readers understand the value of big data, make a business case for using Hadoop, navigate the Hadoop ecosystem, and build and manage Hadoop applications and clusters. Explains the origins of Hadoop, its economic benefits, and its functionality and practical applications Helps you find your way around the Hadoop ecosystem, program MapReduce, utilize design patterns, and get your Hadoop cluster up and running quickly and easily Details how to use Hadoop applications for data mining, web analytics and personalization, large-scale text processing, data science, and problem-solving Shows you how to improve the value of your Hadoop cluster, maximize your investment in Hadoop, and avoid common pitfalls when building your Hadoop cluster From programmers challenged with building and maintaining affordable, scaleable data systems to administrators who must deal with huge volumes of information effectively and efficiently, this how-to has something to help you with Hadoop. |
| data science internship program: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 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. |
| data science internship program: The Great Cloud Migration Michael C. Daconta, 2013 - Learn how to migrate your applications to the cloud! - Learn how to overcome your senior management's concerns about Cloud Security and Interoperability! - Learn how to explain cloud computing, big data and linked data to your organization! - Learn how to develop a robust Cloud Implementation Strategy! - Learn how a Technical Cloud Broker can ease your migration to the cloud! This book will answer the key questions that every organization is asking about emerging technologies like Cloud Computing, Big Data and Linked Data. Written by a seasoned expert and author/co-author of 11 other technical books, this book deftly guides you with real-world experience, case studies, illustrative diagrams and in-depth analysis. * How do you migrate your software applications to the cloud? This book is your definitive guide to migrating applications to the cloud! It explains all the options, tradeoffs, challenges and obstacles to the migration. It provides a migration lifecycle and process you can follow to migrate each application. It provides in-depth case studies: an Infrastructure-as-a-Service case study and a Platform-as-a-Service case study. It covers the difference between application migration and data migration to the cloud and walks you through how to do both well. It covers migration to all the major cloud providers to include Amazon Web Services (AWS), Google AppEngine and Microsoft Azure. * How do you develop a sound implementation strategy for the migration to the cloud? This book leverages Mr. Daconta's 25 years of leadership experience, from the Military to Corporate Executive teams to the Office of the CIO in the Department of Homeland Security, to guide you through the development of a practical and sound implementation strategy. The book's Triple-A Strategy: Assessment, Architecture then Action is must reading for every project lead and IT manager! * This book covers twenty migration scenarios! Application and data migration to the cloud |
| data science internship program: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data science internship program: Envisioning the Data Science Discipline National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-03-05 The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation's ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses. The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction. This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education. |
| data science internship program: Clinical Psychology Internship for Underrepresented Students Anatasia Kim, Abigail Johal, 2021-08-16 This text introduces Taking Flight, a year-long clinical psychology internship program to be implemented for students of color and first-generation college-bound students. The program offers hands-on opportunities for participants to develop skills that will propel them to seek advanced degrees in mental health. The book offers a comprehensive internship curriculum based on a culturally affirming mentorship framework that aims to increase interns’ exposure of clinical psychology, build confidence in their ability, and foster a sense of belonging as a means to inspire educational and career pursuits in the field. Chapters cover topics such as common mental health concerns; self-reflections and insights; research and clinical approaches; capstone projects and presentations; and integration of knowledge, skills, and self-concept. An appendix includes worksheets to utilize throughout the course of the program. The program is designed to be operated within psychology departments in partnership with local high schools. The text will guide mental health providers and school professionals to executing this program in the hopes of ensuring a more diverse and inclusive clinical psychology workforce. |
| data science internship program: Total Internship Management Robert Shindell, Richard Bottner, 2016-01-12 Total Internship Management: The Employer's Guide to Building the Ultimate Internship Program bridges that gap with offer innovative articles by leading industry experts on typically unaddressed aspects of internship programs: understanding the next generation of college students, successful methods of assessment, effective coaching tactics, relevant legal issues, and detailed compensation data. With Total Internship Management, your organization will acquire the skills and knowledge to effectively utilize your interns. Based on the National Internship & Co-op Study - the largest internship research project in the United States over the past nine years - Total Internship Management draws from real life experiences from more than 500,000 college students and more than 500 organizations to help you bring effective techniques and practices to all facets of your internship program, from knowing if your organization is ready to start recruiting, to properly evaluating your interns on the job, to identifying and retaining future employees. |
| data science internship program: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-10-02 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. |
| data science internship program: Big Data at Work Thomas Davenport, 2014-02-04 Go ahead, be skeptical about big data. The author was—at first. When the term “big data” first came on the scene, bestselling author Tom Davenport (Competing on Analytics, Analytics at Work) thought it was just another example of technology hype. But his research in the years that followed changed his mind. Now, in clear, conversational language, Davenport explains what big data means—and why everyone in business needs to know about it. Big Data at Work covers all the bases: what big data means from a technical, consumer, and management perspective; what its opportunities and costs are; where it can have real business impact; and which aspects of this hot topic have been oversold. This book will help you understand: • Why big data is important to you and your organization • What technology you need to manage it • How big data could change your job, your company, and your industry • How to hire, rent, or develop the kinds of people who make big data work • The key success factors in implementing any big data project • How big data is leading to a new approach to managing analytics With dozens of company examples, including UPS, GE, Amazon, United Healthcare, Citigroup, and many others, this book will help you seize all opportunities—from improving decisions, products, and services to strengthening customer relationships. It will show you how to put big data to work in your own organization so that you too can harness the power of this ever-evolving new resource. |
| data science internship program: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 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 internship program: Building Data Science Teams DJ Patil, 2011-09-15 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 internship program: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| data science internship program: Closing the Analytics Talent Gap Jennifer Priestley, Robert McGrath, 2021-05-04 How can we recruit out of your program? We have a project – how do we reach out to your students? If we do research together who owns it? We have employees who need to upskill in analytics – can you help me with that? How much does all of this cost? Managers and executives are increasingly asking university professors such questions as they deal with a critical shortage of skilled data analysts. At the same time, academics are asking such questions as: How can I bring a real analytical project in the classroom? How can I get real data to help my students develop the skills necessary to be a data scientist? Is what I am teaching in the classroom aligned with the demands of the market for analytical talent? After spending several years answering almost daily e-mails and telephone calls from business managers asking for staffing help and aiding fellow academics with their analytics teaching needs, Dr. Jennifer Priestley of Kennesaw State University and Dr. Robert McGrath of the University of New Hampshire wrote Closing the Analytics Talent Gap: An Executive’s Guide to Working with Universities. The book builds a bridge between university analytics programs and business organizations. It promotes a dialog that enables executives to learn how universities can help them find strategically important personnel and universities to learn how they can develop and educate this personnel. Organizations are facing previously unforeseen challenges related to the translation of massive amounts of data – structured and unstructured, static and in-motion, voice, text, and image – into information to solve current challenges and anticipate new ones. The advent of analytics and data science also presents universities with unforeseen challenges of providing learning through application. This book helps both organizations with finding data natives and universities with educating students to develop the facility to work in a multi-faceted and complex data environment. . |
| data science internship program: Fifty Challenging Problems in Probability with Solutions Frederick Mosteller, 2012-04-26 Remarkable puzzlers, graded in difficulty, illustrate elementary and advanced aspects of probability. These problems were selected for originality, general interest, or because they demonstrate valuable techniques. Also includes detailed solutions. |
| data science internship program: 50 Ways to Get a Job Dev Aujla, 2018-04-03 A new personalized way to find the perfect job—while staying calm during the process. You are so much more than a resume or job application, but how can you communicate that to your potential employer? You need to learn to ask the right questions, stop using job sites, and start doing the work that actually counts. Based on information gained from over 400,000 individuals who have used these exercises, this book reveals career expert Dev Aujla’s tried-and-tested method for job seekers at every stage of their career. Filled with anecdotes and advice from professionals ranging from a wilderness guide to an architect, it includes quick-step exercises that help you avoid the common pitfalls of navigating a modern career. Whether you've just decided to start the hunt or you're gearing up for a big interview, 50 Ways to Get a Job will keep you poised, on-track, and motivated right up to landing your dream career. |
| data science internship program: Analytics and Big Data: The Davenport Collection (6 Items) Thomas H. Davenport, Jeanne G. Harris, 2014-08-12 The Analytics and Big Data collection offers a “greatest hits” digital compilation of ideas from world-renowned thought leader Thomas Davenport, who helped popularize the terms analytics and big data in the workplace. An agile and prolific thinker, Davenport has written or coauthored more than a dozen bestselling books. Several of these titles are offered together for the first time in this curated digital bundle, including: Big Data at Work, Competing on Analytics, Analytics at Work, and Keeping Up with the Quants. The collection also includes Davenport’s popular Harvard Business Review articles, “Data Scientist: The Sexiest Job of the 21st Century” (2012) and “Analytics 3.0” (2013). Combined, these works cover all the bases on analytics and big data: what each term means; the ramifications of each from a technical, consumer, and management perspective; and where each can have the biggest impact on your business. Whether you’re an executive, a manager, or a student wanting to learn more, Analytics and Big Data is the most comprehensive collection you’ll find on the ever-growing phenomenon of digital data and analysis—and how you can make this rising business trend work for you. Named one of the ten “Masters of the New Economy” by CIO magazine, Thomas Davenport has helped hundreds of companies revitalize their management practices. He combines his interests in research, teaching, and business management as the President’s Distinguished Professor of Information Technology & Management at Babson College. Davenport has also taught at Harvard Business School, the University of Chicago, Dartmouth’s Tuck School of Business, and the University of Texas at Austin and has directed research centers at Accenture, McKinsey & Company, Ernst & Young, and CSC. He is also an independent Senior Advisor to Deloitte Analytics. |
| data science internship program: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| data science internship program: BEST Lorena Infante Lara, Laura Daniel, Roger Chalkley, 2020-02-18 BEST: Implementing Career Development Activities for Biomedical Research Trainees provides an instructional guide for institutions wanting to create, supplement or improve their career and professional development offerings. Each chapter provides an exclusive perspective from an administrator from the 17 Broadening Experiences in Scientific Training (BEST) institutions. The book can aid institutions who train graduate students in a variety of careers by teaching faculty and staff how to create and implement career development programming, how to highlight the effectiveness of offerings, how to demonstrate that creating a program from scratch is doable, and how to inform faculty and staff on getting institutional buy-in. This is a must-have for graduate school deans and faculty and staff who want to implement and institutionalize career development programing at their institutions. It is also ideal for graduate students and postdocs. - Provides an instructional guide for institutions wanting to create or supplement their career and professional development offerings - Contains perspectives from administrators from the 17 Broadening Experiences in Scientific Training (BEST) institutions - Addresses what graduate students and postdoctoral populations can implement now to help broaden career outcomes |
| data science internship program: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| data science internship program: Intern Talk Anthony Louis, 2020-09-15 From navigating interviews and crafting r sum s to effective networking and personal branding, Intern Talk is a career coach and adviser disguised as a book. It not only guides students in the pursuit of professional opportunities but also offers a somewhat novel approach to achieving a lifetime of career success. |
| data science internship program: InternQube Michael True, 2013-04-15 Version 2.0 |
| data science internship program: Data Science Concepts and Techniques with Applications Usman Qamar, Muhammad Summair Raza, 2023-04-02 This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. This book delivers a systematic, carefully thoughtful material on Data Science. from the Foreword by Witold Pedrycz, U Alberta, Canada. |
| data science internship program: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
| data science internship program: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| data science internship program: Data Processing for Education , 1968 |
| data science internship program: Community-Based Transformational Learning Christian Winterbottom, Jody S. Nicholson, F. Dan Richard, 2020-05-14 Rooted in the work of community – school collaborations, this text focuses on connecting the rigors of the classroom with the ambiguity of lived community experience. Community-Based Transformational Learning (CBTL) draws on the increasing evidence that course-learning conducted in an applied, community setting, can positively transform students' professional and personal identity and creates new ways of thinking and working in university courses and pre-professional experiences. To illustrate the different ways to successfully implement community-based learning, examples are provided of experiences integrated in courses across multiple disciplines across an American university whose mission is focused on teaching. Topics covered include refugee and immigration transition issues, incarceration and health needs with international examples of community experiences from Jamaica, Korea and Belize. Qualitative and quantitative data depict how these experiences impact students and each chapter presents how community engagement has been established as an effective approach in the different disciplines, including computer science and sports management. The authors demonstrate how CBTL experiences can be transformative when students are provided a chance to connect the academic commitment to community aims, but also provides suggestions for overcoming challenges and pit-falls in developing these experiences. |
| data science internship program: 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 internship program: Creativity in Intelligent Technologies and Data Science Alla G. Kravets, Peter P. Groumpos, Maxim Shcherbakov, Marina Kultsova, 2019-08-29 This two-volume set constitutes the proceedings of the Third Conference on Creativity in Intellectual Technologies and Data Science, CIT&DS 2019, held in Volgograd, Russia, in September 2019. The 67 full papers, 1 short paper and 3 keynote papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in topical sections in the two volumes. Part I: cyber-physical systems and Big Data-driven world. Part II: artificial intelligence and deep learning technologies for creative tasks; intelligent technologies in social engineering. |
| data science internship program: Analytics and Knowledge Management Suliman Hawamdeh, Hsia-Ching Chang, 2018-08-06 The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms. |
| data science internship program: Advice To A Young Scientist P. B. Medawar, 2008-08-01 To those interested in a life in science, Sir Peter Medawar, Nobel laureate, deflates the myths of invincibility, superiority, and genius; instead, he demonstrates it is common sense and an inquiring mind that are essential to the scientist's calling. He deflates the myths surrounding scientists -- invincibility, superiority, and genius; instead, he argues that it is common sense and an inquiring mind that are essential to the makeup of a scientist. He delivers many wry observations on how to choose a research topic, how to get along wih collaborators and older scientists and administrators, how (and how not) to present a scientific paper, and how to cope with culturally superior specialists in the arts and humanities. |
| data science internship program: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data science internship program: Revenue Administration Handbook Raúl Félix Junquera-Varela, Cristian Óliver Lucas-Mas, 2024-03-20 Revenue Administration Handbook provides a comprehensive overview of the structure and management of tax and customs administrations, covering topics such as tax policy design considerations that impact tax administration, institutional setup and strategic planning, analyticalcapacities and maturity models, core business processes, and tax sanctions. It also presents pioneering work in the field of digital transformation and how to build data science capabilities, including a roadmap for policy makers and tax officials on how to incorporate and manage disruptive technologies, such as machine learning, into building modern revenue administrations while taking into account their respective maturity levels. This practical manual provides examples from real-life World Bank projects so that policy makers, tax officials, information technology experts, and information and communication technology providers can better understand the needs of revenue administrations to design and implement the most appropriate technology solutions. This reference work is intended to serve as a tool to facilitate the progress and modernization of tax and customs administrations worldwide, and to reach not only tax experts and policy makers, but also other government officials, businesses and academic communities, as well as the larger public, since all are relevant stakeholders with an active role in day-to-day revenue administration operations. ------------------------- “This is a very timely and useful reference for tax practitioners and stakeholders, coming at a time when tax administrators continue to grapple with the challenge of how to accelerate the modernization of technology systems to remain effective in a rapidly advancing and technologydriven business environment.†? MOSES WASIKE, Senior financial management specialist, World Bank “This is an impressive piece of work that pulls together many different strains on tax administration.†? JEFFREY OWENS, Director, Global Tax Policy Center, Vienna University “Applying several technologies discussed in this handbook in an innovative manner will definitely help leapfrog countries to pursue a digital transformation agenda, especially in the areas of efficiency, productivity, and citizen satisfaction.†? |
| data science internship program: Next Gen PhD Melanie V. Sinche, 2016-08-22 For decades, top scientists in colleges and universities pursued a clear path to success: enroll in a prestigious graduate program, conduct research, publish papers, complete the PhD, pursue postdoctoral work. With perseverance and a bit of luck, a tenure-track professorship awaited at the end. In today’s academic job market, this scenario represents the exception. As the number of newly conferred science PhDs keeps rising, the number of tenured professorships remains stubbornly stagnant. “Next Gen PhD: A Guide to Career Paths in Science is a practical and thorough manual for the entire career transition process, from defining personal interests and deciding on a career path all the way to day one of a new job. Written by experienced career counselor Melanie Sinche, it is geared toward postdocs and graduate students who may not have access to effective career counseling or mentorship or are not satisfied with what they have received thus far.” —Teegan A. Dellibovi-Ragheb, Science “With its focus on PhD level scientists, this book fills a gap in job search and career information literature. It’s a must-read for those contemplating or actively pursuing studies in the subject area, as well as those who provide guidance to undergraduates, graduate students, and postdoctoral scholars.” —Alan Farber, Library Journal (starred review) |
| data science internship program: Promoting Statistical Practice and Collaboration in Developing Countries O. Olawale Awe, Kim Love, Eric A. Vance, 2022-06-07 Rarely, but just often enough to rebuild hope, something happens to confound my pessimism about the recent unprecedented happenings in the world. This book is the most recent instance, and I think that all its readers will join me in rejoicing at the good it seeks to do. It is an example of the kind of international comity and collaboration that we could and should undertake to solve various societal problems. This book is a beautiful example of the power of the possible. [It] provides a blueprint for how the LISA 2020 model can be replicated in other fields. Civil engineers, or accountants, or nurses, or any other profession could follow this outline to share expertise and build capacity and promote progress in other countries. It also contains some tutorials for statistical literacy across several fields. The details would change, of course, but ideas are durable, and the generalizations seem pretty straightforward. This book shows every other profession where and how to stand in order to move the world. I urge every researcher to get a copy! —David Banks from the Foreword Promoting Statistical Practice and Collaboration in Developing Countries provides new insights into the current issues and opportunities in international statistics education, statistical consulting, and collaboration, particularly in developing countries around the world. The book addresses the topics discussed in individual chapters from the perspectives of the historical context, the present state, and future directions of statistical training and practice, so that readers may fully understand the challenges and opportunities in the field of statistics and data science, especially in developing countries. Features • Reference point on statistical practice in developing countries for researchers, scholars, students, and practitioners • Comprehensive source of state-of-the-art knowledge on creating statistical collaboration laboratories within the field of data science and statistics • Collection of innovative statistical teaching and learning techniques in developing countries Each chapter consists of independent case study contributions on a particular theme that are developed with a common structure and format. The common goal across the chapters is to enhance the exchange of diverse educational and action-oriented information among our intended audiences, which include practitioners, researchers, students, and statistics educators in developing countries. |
| data science internship program: NASA/NSTA space science student involvement program , 199? |
| data science internship program: Agile Enterprise Engineering: Smart Application of Human Factors Sergey V. Zykov, Amitoj Singh, 2020-02-27 This concise book provides a survival toolkit for efficient, large-scale software development. Discussing a multi-contextual research framework that aims to harness human-related factors in order to improve flexibility, it includes a carefully selected blend of models, methods, practices, and case studies. To investigate mission-critical communication aspects in system engineering, it also examines diverse, i.e. cross-cultural and multinational, environments. This book helps students better organize their knowledge bases, and presents conceptual frameworks, handy practices and case-based examples of agile development in diverse environments. Together with the authors’ previous books, Crisis Management for Software Development and Knowledge Transfer (2016) and Managing Software Crisis: A Smart Way to Enterprise Agility (2018), it constitutes a comprehensive reference resource adds value to this book. |
| data science internship program: Strategies for Student Success in Higher Education Hagai Gringarten, Raúl Fernández-Calienes, 2024-07-30 This book brings together both leading-edge research and practical insights on the first-year experience in higher education. Written by a large team of experts, the text integrates a variety of multidisciplinary approaches and real-life case studies into an effective pedagogical resource for the higher education scholarly audience of both professors and administrators to address the needs of first-year students in higher education. The book includes material authored by 39 professors and professionals from more than 20 universities and higher education organizations from across the USA, Canada, the Philippines, and Germany. This book offers insights for disciplines including business administration and management, communications, counseling, education, law and governance, mental health and psychology, sociology, and others. Scholars and practitioners in a variety of higher education areas can benefit from it in terms of their work in academic success, advising, campus safety, career services, dual enrollment programs, emergency management, mathematics education, service learning, student well-being, technology management, and other areas. |
| data science internship program: SQL Pocket Guide Alice Zhao, 2021-08-26 If you use SQL in your day-to-day work as a data analyst, data scientist, or data engineer, this popular pocket guide is your ideal on-the-job reference. You'll find many examples that address the language's complexities, along with key aspects of SQL used in Microsoft SQL Server, MySQL, Oracle Database, PostgreSQL, and SQLite. In this updated edition, author Alice Zhao describes how these database management systems implement SQL syntax for both querying and making changes to a database. You'll find details on data types and conversions, regular expression syntax, window functions, pivoting and unpivoting, and more. Quickly look up how to perform specific tasks using SQL Apply the book's syntax examples to your own queries Update SQL queries to work in five different database management systems NEW: Connect Python and R to a relational database NEW: Look up frequently asked SQL questions in the How Do I? chapter |
| data science internship program: Digital Humanities and Laboratories Urszula Pawlicka-Deger, Christopher Thomson, 2023-11-09 This book · includes contributions from a diverse, international range of scholars and practitioners and this volume examines the ways laboratories of all kinds contribute to digital research and pedagogy. · Acknowledging that they are emerging amid varied cultural and scientific traditions, the volume considers how they lead to the specification of digital humanities and how a locally situated knowledge production is embedded in the global infrastructure system. · consolidates the discussion on the role of the laboratory in DH and brings digital humanists into the interdisciplinary debate concerning the notion of a laboratory as a critical site in the generation of experimental knowledge. Positioning the discussion in relation to ongoing debates in DH, the volume argues that laboratory studies are in an excellent position to capitalize on the theories and knowledge developed in the DH field and open up new research inquiries. · clearly demonstrates that the laboratory is a key site for theoretical and political analyses of digital humanities and will thus be of interest to scholars, students and practitioners engaged in the study of DH, culture, media, heritage and infrastructure. |
| data science internship program: Commerce, Justice, Science, and Related Agencies Appropriations for 2017: Justification of the budget estimates United States. Congress. House. Committee on Appropriations. Subcommittee on Commerce, Justice, Science, and Related Agencies, 2016 |
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 …
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 …
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, …
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 …
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 …
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 …
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 …
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 …
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, …
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 …
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 …
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 …
