Advertisement
| data science curriculum undergraduate: 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 curriculum undergraduate: 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 curriculum undergraduate: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science curriculum undergraduate: Security Informatics Christopher C. Yang, Michael Chau, Jau-Hwang Wang, Hsinchun Chen, 2010-01-08 Intelligence and Security Informatics (ISI) is defined as the study of the development and use of advanced information systems and technologies for national, international, and societal security-related applications. With the rise of global terrorism, the field has been given an increasing amount of attention from academic researchers, law enforcement, intelligent experts, information technology consultants and practitioners. SECURITY INFORMATICS is global in scope and perspective. Leading experts will be invited as contributing authors from the US, UK, Denmark, Israel, Singapore, Hong Kong, Taiwan, Europe, etc. It is the first systematic, archival volume treatment of the field and will cover the very latest advances in ISI research and practice. It is organized in four major subject areas: (1) Information and Systems Security, (2) Information Sharing and Analysis in Security Informatics, (3) Infrastructure Protection and Emergency Responses, and (4) National Security and Terrorism Informatics. |
| data science curriculum undergraduate: A Hands-On Introduction to Data Science Chirag Shah, 2020-04-02 An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines. |
| data science curriculum undergraduate: 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 curriculum undergraduate: Healthcare Data Analytics Chandan K. Reddy, Charu C. Aggarwal, 2015-06-23 At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available |
| data science curriculum undergraduate: Topics in Biostatistics Walter T. Ambrosius, 2007-07-06 This book presents a multidisciplinary survey of biostatics methods, each illustrated with hands-on examples. It introduces advanced methods in statistics, including how to choose and work with statistical packages. Specific topics of interest include microarray analysis, missing data techniques, power and sample size, statistical methods in genetics. The book is an essential resource for researchers at every level of their career. |
| data science curriculum undergraduate: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
| data science curriculum undergraduate: 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 curriculum undergraduate: Keeping Track Jeannie Oakes, 2005-05-10 Selected by the American School Board Journal as a “Must Read” book when it was first published and named one of 60 “Books of the Century” by the University of South Carolina Museum of Education for its influence on American education, this provocative, carefully documented work shows how tracking—the system of grouping students for instruction on the basis of ability—reflects the class and racial inequalities of American society and helps to perpetuate them. For this new edition, Jeannie Oakes has added a new Preface and a new final chapter in which she discusses the “tracking wars” of the last twenty years, wars in which Keeping Track has played a central role. From reviews of the first edition:“Should be read by anyone who wishes to improve schools.”—M. Donald Thomas, American School Board Journal“[This] engaging [book] . . . has had an influence on educational thought and policy that few works of social science ever achieve.”—Tom Loveless in The Tracking Wars“Should be read by teachers, administrators, school board members, and parents.”—Georgia Lewis, Childhood Education“Valuable. . . . No one interested in the topic can afford not to attend to it.”—Kenneth A. Strike, Teachers College Record |
| data science curriculum undergraduate: The Routledge International Handbook of the Sociology of Education Michael W. Apple, Stephen J. Ball, Luis Armando Gandin, 2009-12-16 This collection brings together the work of a group of the world’s leading sociologists of education to explore and address key issues and concerns within the discipline. The chapters draw upon theory and research to provide ‘state of the art’ accounts of contemporary educational processes, global trends, and changing and enduring forms of social conflict and social inequality. The topics which are addressed are of international relevance and significance. |
| data science curriculum undergraduate: 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 curriculum undergraduate: Law and Policy for the Quantum Age Chris Jay Hoofnagle, Simson L. Garfinkel, 2022-01-06 The Quantum Age cuts through the hype to demystify quantum technologies, their development paths, and the policy issues they raise. |
| data science curriculum undergraduate: Building Faculty Learning Communities Milton D. Cox, Laurie Richlin, 2004-05-21 Changing our colleges and universities into learning institutions has become increasingly important at the same time it has become more difficult. Faculty learning communities have proven to be effective for addressing institutional challenges, from preparing the faculty of the future and reinvigorating senior faculty, to implementing new courses, curricula, and campus initiatives on diversity and technology. The results of faculty learning community programs parallel for faculty members the results of student learning communities for students, such as retention, deeper learning, respect for other cultures, and greater civic participation. The chapters in this issue of New Directions for Teaching and Learning describe from a practitioner's perspective the history, development, implementation, and results of faculty learning communities across a wide range of institutions and purposes. Institutions are invited to use this volume to initiate faculty learning communities on their campuses. This is the 97th issue of the quarterly journal New Directions for Teaching and Learning. |
| data science curriculum undergraduate: Introduction to Scientific Programming with Python Joakim Sundnes, 2020 This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies. |
| data science curriculum undergraduate: Mindset Mathematics Jo Boaler, Jen Munson, Cathy Williams, 2017-08-28 Engage students in mathematics using growth mindset techniques The most challenging parts of teaching mathematics are engaging students and helping them understand the connections between mathematics concepts. In this volume, you'll find a collection of low floor, high ceiling tasks that will help you do just that, by looking at the big ideas at the first-grade level through visualization, play, and investigation. During their work with tens of thousands of teachers, authors Jo Boaler, Jen Munson, and Cathy Williams heard the same message—that they want to incorporate more brain science into their math instruction, but they need guidance in the techniques that work best to get across the concepts they needed to teach. So the authors designed Mindset Mathematics around the principle of active student engagement, with tasks that reflect the latest brain science on learning. Open, creative, and visual math tasks have been shown to improve student test scores, and more importantly change their relationship with mathematics and start believing in their own potential. The tasks in Mindset Mathematics reflect the lessons from brain science that: There is no such thing as a math person - anyone can learn mathematics to high levels. Mistakes, struggle and challenge are the most important times for brain growth. Speed is unimportant in mathematics. Mathematics is a visual and beautiful subject, and our brains want to think visually about mathematics. With engaging questions, open-ended tasks, and four-color visuals that will help kids get excited about mathematics, Mindset Mathematics is organized around nine big ideas which emphasize the connections within the Common Core State Standards (CCSS) and can be used with any current curriculum. |
| data science curriculum undergraduate: Developing Analytic Talent Vincent Granville, 2014-03-24 Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. |
| data science curriculum undergraduate: Introduction to Data Technologies Paul Murrell, 2009-02-23 Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education. With a focus on computational tools, the book shows readers how to improve thei |
| data science curriculum undergraduate: Enhancing Effective Instruction and Learning Using Assessment Data Hong Jiao, Robert W. Lissitz, 2021 This book introduces theories and practices for using assessment data to enhance learning and instruction. Topics include reshaping the homework review process, iterative learning engineering, learning progressions, learning maps, score report designing, the use of psychosocial data, and the combination of adaptive testing and adaptive learning. In addition, studies proposing new methods and strategies, technical details about the collection and maintenance of process data, and examples illustrating proposed methods and/or software are included. Chapter 1, 4, 6, 8, and 9 discuss how to make valid interpretations of results and/or achieve more efficient instructions from various sources of data. Chapter 3 and 7 propose and evaluate new methods to promote students' learning by using evidence-based iterative learning engineering and supporting the teachers' use of assessment data, respectively. Chapter 2 provides technical details on the collection, storage, and security protection of process data. Chapter 5 introduces software for automating some aspects of developmental education and the use of predictive modeling. Chapter 10 describes the barriers to using psychosocial data for formative assessment purposes. Chapter 11 describes a conceptual framework for adaptive learning and testing and gives an example of a functional learning and assessment system. In summary, the book includes comprehensive perspectives of the recent development and challenges of using test data for formative assessment purposes. The chapters provide innovative theoretical frameworks, new perspectives on the use of data with technology, and how to build new methods based on existing theories. This book is a useful resource to researchers who are interested in using data and technology to inform decision making, facilitate instructional utility, and achieve better learning outcomes-- |
| data science curriculum undergraduate: Neural Networks with R Giuseppe Ciaburro, Balaji Venkateswaran, 2017-09-27 Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples. |
| data science curriculum undergraduate: 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 curriculum undergraduate: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| data science curriculum undergraduate: 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 curriculum undergraduate: War, Peace, and Security Jacques Fontanel, Manas Chatterji, 2008-10-13 In the name of international and domestic security, billions of dollars are wasted on unproductive military spending in both developed and developing countries, when millions are starving and living without basic human needs. This book contains articles relating to military spending, military industrial establishments, and peace keeping. |
| data science curriculum undergraduate: Data Science for Mathematicians Nathan Carter, 2020-09-15 Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them. |
| data science curriculum undergraduate: Thinking with Data Marsha Lovett, Priti Shah, 2007 First Published in 2007. Routledge is an imprint of Taylor & Francis, an informa company. |
| data science curriculum undergraduate: Mining of Massive Datasets Jure Leskovec, Jurij Leskovec, Anand Rajaraman, Jeffrey David Ullman, 2014-11-13 Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets. |
| data science curriculum undergraduate: Principles of Data Science Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau, 2020-07-08 This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice |
| data science curriculum undergraduate: Fundamentals of Statistical Inference , 1977 |
| data science curriculum undergraduate: 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 curriculum undergraduate: Communicating with Data Deborah Nolan, Sara Stoudt, 2021-03-25 Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component. Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication. |
| data science curriculum undergraduate: Computer Science Education Sue Sentance, Erik Barendsen, Nicol R. Howard, Carsten Schulte, 2023-02-23 Drawing together the most up-to-date research from experts all across the world, the second edition of Computer Science Education offers the most up-to-date coverage available on this developing subject, ideal for building confidence of new pre-service and in-service educators teaching a new discipline. It provides an international overview of key concepts, pedagogical approaches and assessment practices. Highlights of the second edition include: - New sections on machine learning and data-driven (epistemic) programming - A new focus on equity and inclusion in computer science education - Chapters updated throughout, including a revised chapter on relating ethical and societal aspects to knowledge-rich aspects of computer science education - A new set of chapters on the learning of programming, including design, pedagogy and misconceptions - A chapter on the way we use language in the computer science classroom. The book is structured to support the reader with chapter outlines, synopses and key points. Explanations of key concepts, real-life examples and reflective points keep the theory grounded in classroom practice. The book is accompanied by a companion website, including online summaries for each chapter, 3-minute video summaries by each author and an archived chapter on taxonomies and competencies from the first edition. |
| data science curriculum undergraduate: Minds in Play Yasmin Bettina Kafai, Yasmin B. Kafai, 1995 Video games more than any other media have brought technology into children's homes and hearts. Educators, psychologists, and parents are struck by the quality of engagement that stands in stark contrast to children's usual interest in school homework and other activities. Whereas most research efforts have concentrated on discussing the effects of game playing, this book takes a different stance. It takes a close look at games as a context for learning by placing children in the roles of producers rather than consumers of games. Kafai presents a constructionist vision of computer-based learning activities in schools. She follows a class of sixteen fourth-grade students from an inner-city public elementary school as they were programming games in Logo to teach fractions to third graders. The children transformed their classroom into a game design studio for six months, learning programming, writing stories and dialogues, constructing representations of fractions, creating package designs and advertisements, considering interface design issues, and devising teaching strategies. In this context, programming became a medium for children's personal and creative expression; in the design of their games children engaged their fantasies and built relationships with other pockets of reality that went beyond traditional school approaches. The ideas and discussions presented in this book address educators, researchers, and software and curriculum designers interested in children's learning and thinking with educational technologies. |
| data science curriculum undergraduate: 2022 CFA Program Curriculum Level I Box Set CFA Institute, 2021-05-04 Prepare for success on the 2022 CFA Level I exam with the latest official CFA® Program Curriculum. The 2022 CFA Program Curriculum Level I Box Set contains all the material you need to succeed on the Level I CFA exam in 2022. This set includes the full official curriculum for Level I and is part of the larger CFA Candidate Body of Knowledge (CBOK). Highly visual and intuitively organized, this box set allows you to: Learn from financial thought leaders. Access market-relevant instruction. Gain critical knowledge and skills. The set also includes practice questions to assist with your recall of key terms, concepts, and formulas. Perfect for anyone preparing for the 2022 Level I CFA exam, the 2022 CFA Program Curriculum Level I Box Set is a must-have resource for those seeking the foundational skills required to become a Chartered Financial Analyst®. |
| data science curriculum undergraduate: Getting Started with Data Science Murtaza Haider, 2015-12-14 Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon. |
| data science curriculum undergraduate: Transforming Digital Worlds Gobinda Chowdhury, Julie McLeod, Val Gillet, Peter Willett, 2018-03-17 This book constitutes the proceedings of the 13th International Conference on Transforming Digital Worlds, iConference 2018, held in Sheffield, UK, in March 2018. The 42 full papers and 40 short papers presented together with the abstracts of 3 invited talks in this volume were carefully reviewed and selected from 219 submissions. The papers address topics such as social media; communication studies and online communities; mobile information and cloud computing; data mining and data analytics; information retrieval; information behaviour and digital literacy; digital curation; and information education and libraries. |
| data science curriculum undergraduate: Guide to Teaching Data Science Orit Hazzan, Koby Mike, 2023-03-20 Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry. This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people. This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach). Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University. |
| data science curriculum undergraduate: Sustainable Digital Communities Anneli Sundqvist, Gerd Berget, Jan Nolin, Kjell Ivar Skjerdingstad, 2020-03-19 This volume constitutes the proceedings of the 15th International Conference on Sustainable Digital Communities, iConference 2020, held in Boras, Sweden, in March 2020. The 27 full papers and the 48 short papers presented in this volume were carefully reviewed and selected from 178 submissions. They cover topics such as: sustainable communities; social media; information behavior; information literacy; user experience; inclusion; education; public libraries; archives and records; future of work; open data; scientometrics; AI and machine learning; methodological innovation. |
| data science curriculum undergraduate: Diversity, Divergence, Dialogue Katharina Toeppe, Hui Yan, Samuel Kai Wah Chu, 2021-03-19 This two-volume set LNCS 12645-12646 constitutes the refereed proceedings of the 16th International Conference on Diversity, Divergence, Dialogue, iConference 2021, held in Beijing, China, in March 2021. The 32 full papers and the 59 short papers presented in this volume were carefully reviewed and selected from 225 submissions. They cover topics such as: AI and machine learning; data science; human-computer interaction; social media; digital humanities; education and information literacy; information behavior; information governance and ethics; archives and records; research methods; and institutional management. |
Computing Competencies for Undergraduate Data Science …
Knowledge Areas for Data Science that appeared in the first public draft of this project (available at http://dstf.acm.org/DSReportInitialFull.pdf). With the release of the first draft report, the ACM …
Data Science Program Guide - College of LSA
Students majoring in Data Science will learn computer programming, data analysis and database systems, and will learn to think critically about the process of understanding data.
Program Progression Guides - Purdue University
The Computer Scienceand Statistics Departments has suggested the following degree progression guide for the Data Science Degree. Students will work with their academic …
DATA SCIENCES UNDERGRADUATE HANDBOOK
Our curriculum covers fundamental programming techniques and skills, broad knowledge of data science foundations, mathematical foundations of computing, and advanced topics in …
Data Sciences @ Berkeley The Undergraduate Experience
Providing access for our students to the fundamental structure, principles, and ramifications of data-analytic thinking is a key educational desideratum for an increasingly data-rich world.
BS Data Science Curriculum - senate.uoregon.edu
One new course, DSCI 311, will be developed to cover three areas in depth: handling data in Python, including Pandas, data Cleaning, EDA, and handling text; Dimensionality and PCA; …
Data Science - Undergraduate Programs - University of Texas …
Curriculum requirements, the degree requires a sequences of courses in Mathematics, Data Science, and the chosen Domain Concentration. In addition, students must complete a year …
Curriculum Guidelines for Undergraduate Programs in Data …
Our curriculum guidelines are guided both by recent work on the relation of data science to the other sciences and the need for a workforce better able to meet the demands of the data …
Data Science, B.S. - Saint Louis University
SLU's curriculum is modeled upon guidelines for undergraduate programs in data science as endorsed by the American Statistical Association's Board of Directors. Classes are small and …
DATA SCIENCE Program Educational Objectives: Student …
In addition to completion of the Dearborn Discovery Core, the following courses are required to earn a B.S. degree in Data Science from UM-Dearborn. Take an additional 18 credit hours …
COURSES AND CURRICULUM OF SCHOOL OF DATA SCIENCE
Students must earn at least 12 credit units from B4 level elective courses for graduation. Students may choose available elective courses from any categories for further knowledge in the area.
Curriculum Guidelines for Undergraduate Programs in Data …
Our curriculum guidelines are guided both by recent work on the relation of data science to the other sciences as well as the need for a workforce better able to meet the demands of the data …
Computing Competencies for Undergraduate Data Science …
There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the …
Supplemental Material: Annu. Rev. Stat. Appl. 2017.
from the foundation of Data Science II by acquiring data across a spectrum of single and multiple sources and varying degrees of structure, incorporating real-world data throughout this …
Berkeley’s Undergraduate Data Science Curriculum: Year 1 …
Berkeley’s Data Science education program was created to make it possible for every undergraduate at Berkeley to engage capably and critically with data, and building on this …
ACM ISSUES COMPUTING COMPETENCIES FOR …
To gain a picture of the current landscape of undergraduate degrees in data science, the ACM Data Science Task Force sent a survey to undergraduate institutions across the county asking …
Program Progression Guides - Purdue University
The Computer Science and Statistics Departments has suggested the following degree progression guide for the Data Science Degree. Students will work with their academic …
Data Sciences, B.S. (Information Sciences and Technology)
All students in the major receive in-depth training in data sciences through a set of core courses. Additionally, data sciences students specialize in one of the following options: applied, …
6999 Computing and Data Science Bachelor of Engine…
big data systems, AI & data ethics, AI technologies, and applications. Bachelor of Engineering in Computer Science has been accredited by The Hong Kong Institution of Engineers. …
To recommend Curriculum, Model Study Plan, and Cour…
Curriculum, Model Study Plan, and Course Syllabi, for BS Data Science 6 | Page Areas overed in the S Data Science Numeric ode N ode Group ouse Group HE PU 0 MD Math Deficiency* 0 2 1 …
Student B.S. Information Sciences + Data Science (I…
STAT/IS/CS 107 Design; IS 400 Colloquium; Others approved by AdvisorData Science Discovery STAT 207 Data Science Exploration [1] CS 307 Modeling & Learning in Data Science …
B.Sc. DATA SCIENCE - pmu.edu
B.Sc. DATA SCIENCE CURRICULUM AND SYLLABUS (SEMESTER: I, II, III, IV, V and VI) ... "Learning Outcomes-Based Curriculum Framework for Undergraduate Education", January, …
BS Data Science Curriculum - senate.uoregon.edu
BS Data Science Curriculum Version 1.0, 29 August 2019 1 Executive Summary This document describes the courses and sequence in which said courses are to be taken to obtain a BS in data …
What is Data Science? - Computer Science and Engi…
Current View of Data Science •Data Science is an inherently interdisciplinary field •Data Science is the field that brings together domain data, computer science, and the …
Data Science Minor (Code: NB219N) Computer Scienc…
Please check with the Data Science Undergraduate Program directors/ advisors at datascience@rutgers.edu . Data Science Minor (Code: NB219N) Computer Science Track Curriculum …
An integrated, modular approach to data science e…
Jul 25, 2020 · equip all undergraduate students in the life sciences with basic competency in data 47 science. The EDUCE curriculum provides students with the most commonly needed 48 …
More Computing and Less Programming: A Proposal t…
Jun 7, 2021 · Another great transformative effect of the data science undergraduate curriculum has been on the student body. Scores of undergraduates serve every …
Data Sciences, B.S. (Information Sciences and …
Data Sciences integrate aspects of Computer Science, Informatics, and Statistics to yield powerful data science methods, systems, tools, and best practices that find applications …
Data Science, B.S. - Saint Louis University
curriculum is modeled upon guidelines for undergraduate programs in data science as endorsed by the American Statistical Association's Board ... Classes are small and are taught by …
BSc-DSA Flyer fall 2024 - East West University
Mathematics and Statistics. This is a four-year bachelor’s program with 130 credits. Data Science & Analytics is the subject which has worldwide demand, particularly in industry. The …
Data Mining Course in the Undergraduate Computer …
into undergraduate computer science program [1]. Their work motivated us to conduct our study on a larger scale. We also recommend a curriculum for undergraduate course of Data …
DATA SCIENCE, BS - guide.wisc.edu
Data Science, BS 1 DATA SCIENCE, BS REQUIREMENT S UNIVERSITY GENERAL EDUCATION REQUIREMENTS All undergraduate students at the University of Wisconsin-Madison are …
2025 NTU UG Prospectus - Nanyang Technological Uni…
science u.s. news shanghairanking qs world university rankings by subject engineering & technology environmental science library & information management …
IS2020 A Competency Model for Undergraduate Progra…
The IS2020 report is reflective of the IS discipline and is among the undergraduate curriculum volumes that define the computing disciplines. The Computing Curricula 2020 ( CC2020) …
Program Progression Guides - Purdue University
An undergraduate degree in the College of Science requires completion of the following degree requirements. ... A minimum of a C is required in all Data Science Major coursework …
STATISTICS AND DATA SCIENCE - University of Ca…
Mathematics 70% & Physical Science 60% NBT in Mathematics, AL & QL to be written Data science is a combination of mathematics, statistics, specialised programming, and advanced …
Bachelor of Science in Economics and Data Scien…
Stuart's and Computing's expertise and tradition of undergraduate education preparing students to add value to any organization on day one. This innovative cross-disciplinary …
Data Science B.S. - Academic Catalogue
THE CURRICULUM FOR THE B.S. IN DATA SCIENCE All students must meet the Degree and University Requirements. ... or MATH 4996 Undergraduate Honors Thesis or CS 4996 Undergraduate …
5-yr Integrated M Sc Data Science - Amrita Vishwa Vi…
AVV Integrated MSc Data Science , June 2022 Admission Page 1 of 76 5-yr Integrated M Sc –Data Science CURRICULUM AND SYLLABUS (From 2022 Admission Onwards) AVV …
FRAMEWORK UNDERGRADUATE CURRIC…
Undergraduate Curriculum Framework-2022 (UGCF-2022) which not only underlines the heart and. soul of the NEP 2020 in letter and spirit but also goes on to create a teaching …
Data Sciences, B.S. (Science) - undergraduate.bulletins.…
Data Sciences, B.S. (Science) 1 DATA SCIENCES, B.S. (SCIENCE) Begin Campus: Any Penn State Campus End Campus: University Park Degree Requirements For the Bachelor of …
Bachelor of Science in Data Science - catalog.iit.edu
Bachelor of Science in Data Science Curriculum Year 1 Semester 1 Credit Hours Semester 2 Credit Hours DS 100 3 MATH1 52 DS 151 3 CS 116 2 MATH 151 5 Ethics and Society 3 CS 115 2 …
Master of Science in Data Science & Analytics - Kenne…
Content listed in this curriculum sheet is subject to change. Please consult with your advisor regularly. ... The program is intended for professionals or students with undergraduate …
Interleaving Computational and Inferential Thinking in …
The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential …
REVISED SYLLABUS OF B.Sc. (Data Science) UNDER CBC…
Data Science is a fast-growing interdisciplinary field, focusing on the analysis of data to extract knowledge and insight. This course will introduce students to the collection. …
INTEGRATING THE HUMANITIES INTO DATA S…
In its 2018 consensus study report on undergraduate data science education, the National Academies of Sciences, Engineering, and Medicine (NASEM) in the United States called for …
Program Progression Guides - Purdue University
An undergraduate degree in the College of Science requires completion of the following degree requirements. ... A minimum of a C is required in all Data Science Major coursework …
From Geomatics to Geospatial Science and Sur…
Key words: curriculum review, geospatial science, geospatial data science, land surveying, geographic information science, accreditation, credit limits, undergraduate …
Data Science Major - Columbia University
2) Data Structures: COMS 3134, COMS 3136, or COMS 3137 3) Discrete Math: COMS 3203 4) Analysis of Algorithms: CSOR 4231 Electives: 5 Courses STAT: 2 from the following 1) STAT 3106 …
Interleaving Computational and Inferential Thinking: D…
Apr 30, 2021 · The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential …
March, 2020
BS (Data Science) a. Introduction to Data Science i. Dr Sohail (COMSATS) ii. Dr Manzoor (COMSATS) iii. Dr Hammad (FAST) b. Data Warehouse & Big Data Analytics i. Dr Affan (Software …
Interleaving Computational and Infere…
The undergraduate data science curriculum at the University of California, Berkeley is an-chored in five new courses that emphasize computational thinking, inferential …
Data Science Major - Northwestern University
Data Science 1 with R and Data Science 2 with R and Data Science 3 with R or STAT 303-1 & STAT 303-2 & STAT 303-3 Data Science 1 with Python and Data Science 2 with Python and Data …
A Hands-On Introduction to Data Science - Cambridge …
eld of data science in a practical and accessible manner, using ... exams, and curriculum suggestions. This entry-level textbook is ideally suited to readers ... machine learning courses to …
A fresh look at introductory data science - arXiv.org
Aug 4, 2020 · study of an introductory undergraduate course in data science that is designed to address these needs. O ered at Duke University, this course has no pre-requisites ... a full …
ACADEMIC CURRICULUM GUIDE - University of Arizona
Office of Academic and Faculty Affairs Roy P. Drachman Hall 1295 N. Martin Ave. PO Box 245163 Tucson, Arizona 85724-5163 At the University of Arizona Mel and Enid Zuckerman College of …
Developing an Interdisciplinary Data Scie…
inspiration and motivation for creating the undergraduate Data Science program, the program curriculum, and the governing body of the program. We aim to recruit undergraduates, …
An integrated, modular approach to data science e…
Jul 25, 2020 · integration of data science curriculum into undergraduate courses and programs across the life sciences. Availability and implementation: The EDUCE teaching and learning …
Data Science in 2020: Computing, Curricula, an…
Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years Aimee Schwab-McCoya*, Catherine M. Bakerb, and Rebecca E. Gaspera aDepartment of …
Computer Science Curricula 2023 - Association for Com…
Computer Science Curriculum 2008 [5]: This was presented as an interim revision of Computing Curricula 2001. Computer Science Curricula 2013 [6]: This was the most recent version of …
Curriculum Guidelines for Undergraduate Programs i…
an undergraduate degree in Data Science. Twenty-Þve faculty, comprised of computer sci- ... data, the curriculum can be both signiÞcantly streamlined and enhanced. The integration of …
Suggested Course Plan for a UC Riverside Major in Catal…
%PDF-1.7 %¡³Å× 1 0 obj > endobj 2 0 obj > endobj 3 0 obj > endobj 4 0 obj >/MediaBox[ 0 0 792 612]/Parent 2 0 R /Resources 14 0 R /Rotate 0/Type/Page>> endobj 5 0 obj [] …
Statistics and Data Science (BS): Sample Curriculum
Social Science (3) 16 credits 15‐17 credits 15 credits *****The BS also requires 120 credits; 45 in upper‐division courses. The number of free electives needed to meet these …
Lecture 1 Introduction to Data Science - Stanford Un…
•Unlike most data science or machine learning classes on campus, Datasci112 has no math or statistics prereqs. •To begin doing data science, you need to know how to program (a bit). So …
A fresh look at introductory data science - arXiv.org
study of an introductory undergraduate course in data science that is designed to address these needs. O ered at Duke University, this course has no pre-requisites ... a full data science …
CB024STA13 (BBusSc – Statistics and Data Science…
CB024STA13 (BBusSc – Statistics and Data Science) Curriculum Details 2025 ... CB024STA13 (BBusSc – Statistics and Data Science) Curriculum Details 2025 : YEAR 4 Course ... Strategic Thinking …
Master of Science in Data Science - University of Ne…
2 The University of New Haven is a private institution and is approved to operate by the California Bureau of Postsecondary Private Education. Supported by GalvanizeU which is …
Requirements for Major in Data Science and Analytics
Requirements for Major in Data Science and Analytics Applicable to cohorts AY2021/2022 and after Levels. Major Requirements: Cumulative Major Units. Pass-DSA1101 Introduction to Data …
