Advertisement
| bs in statistics and data science: 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. |
| bs in statistics and data science: 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. |
| bs in statistics and data science: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice. |
| bs in statistics and data science: 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 |
| bs in statistics and data science: 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. |
| bs in statistics and data science: Statistics and Data Analysis for Financial Engineering David Ruppert, David S. Matteson, 2015-04-21 The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. |
| bs in statistics and data science: All of Statistics Larry Wasserman, 2013-12-11 Taken literally, the title All of Statistics is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data. |
| bs in statistics and data science: 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. |
| bs in statistics and data science: 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. |
| bs in statistics and data science: Statistics for Food Scientists Frank Rossi, Victor Mirtchev, 2015-10-06 The practical approached championed in this book have led to increasing the quality on many successful products through providing a better understanding of consumer needs, current product and process performance and a desired future state. In 2009, Frank Rossi and Viktor Mirtchev brought their practical statistical thinking forward and created the course Statistics for Food Scientists. The intent of the course was to help product and process developers increase the probability of their project's success through the incorporation of practical statistical thinking in their challenges. The course has since grown and has become the basis of this book. - Presents detailed descriptions of statistical concepts and commonly used statistical tools to better analyze data and interpret results - Demonstrates thorough examples and specific practical problems of what food scientists face in their work and how the tools of statistics can help them to make more informed decisions - Provides information to show how statistical tools are applied to improve research results, enhance product quality, and promote overall product development |
| bs in statistics and data science: SPSS Statistics for Data Analysis and Visualization Keith McCormick, Jesus Salcedo, 2017-05-01 Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code These hidden tools can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need. |
| bs in statistics and data science: The Data Science Handbook Field Cady, 2017-02-28 A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon. |
| bs in statistics and data science: Probability Theory III Yurij V. Prokhorov, Albert N. Shiryaev, 2013-03-14 This volume of the Encyclopaedia is a survey of stochastic calculus, an increasingly important part of probability, authored by well-known experts in the field. The book addresses graduate students and researchers in probability theory and mathematical statistics, as well as physicists and engineers who need to apply stochastic methods. |
| bs in statistics and data science: Numsense! Data Science for the Layman Annalyn Ng, 2017-03-24 Used in Stanford's CS102 Big Data (Spring 2017) course. Want to get started on data science? Our promise: no math added. This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly. Popular concepts covered include: A/B Testing Anomaly Detection Association Rules Clustering Decision Trees and Random Forests Regression Analysis Social Network Analysis Neural Networks Features: Intuitive explanations and visuals Real-world applications to illustrate each algorithm Point summaries at the end of each chapter Reference sheets comparing the pros and cons of algorithms Glossary list of commonly-used terms With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions. |
| bs in statistics and data science: Progress in Statistics J. Gani, 1975-02-01 |
| bs in statistics and data science: Data Science: Theory and Applications , 2021-03-03 Data Science: Theory and Applications, Volume 44 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of interesting topics, including Modeling extreme climatic events using the generalized extreme value distribution, Bayesian Methods in Data Science, Mathematical Modeling in Health Economic Evaluations, Data Science in Cancer Genomics, Blockchain Technology: Theory and Practice, Statistical outline of animal home ranges, an application of set estimation, Application of Data Handling Techniques to Predict Pavement Performance, Analysis of individual treatment effects for enhanced inferences in medicine, and more. Additional sections cover Nonparametric Data Science: Testing Hypotheses in Large Complex Data, From Urban Mobility Problems to Data Science Solutions, and Data Structures and Artificial Intelligence Methods. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Data Science: Theory and Applications |
| bs in statistics and data science: OpenIntro Statistics David Diez, Christopher Barr, Mine Çetinkaya-Rundel, 2015-07-02 The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. We feature real data whenever possible, and files for the entire textbook are freely available at openintro.org. Visit our website, openintro.org. We provide free videos, statistical software labs, lecture slides, course management tools, and many other helpful resources. |
| bs in statistics and data science: 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. |
| bs in statistics and data science: Proceedings of the Second Seattle Symposium in Biostatistics Danyu Lin, Patrick J. Heagerty, 2012-12-06 This volume contains a selection of papers presented at the Second Seattle Symposium in Biostatistics: Analysis of Correlated Data. The symposium was held in 2000 to celebrate the 30th anniversary of the University of Washington School of Public Health and Community Medicine. It featured keynote lectures by Norman Breslow, David Cox and Ross Prentice and 16 invited presentations by other prominent researchers. The papers contained in this volume encompass recent methodological advances in several important areas, such as longitudinal data, multivariate failure time data and genetic data, as well as innovative applications of the existing theory and methods. This volume is a valuable reference for researchers and practitioners in the field of correlated data analysis. |
| bs in statistics and data science: Coding the Matrix Philip N. Klein, 2013-07 An engaging introduction to vectors and matrices and the algorithms that operate on them, intended for the student who knows how to program. Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by doing, writing programs to implement the mathematical concepts and using them to carry out tasks and explore the applications. Examples include: error-correcting codes, transformations in graphics, face detection, encryption and secret-sharing, integer factoring, removing perspective from an image, PageRank (Google's ranking algorithm), and cancer detection from cell features. A companion web site, codingthematrix.com provides data and support code. Most of the assignments can be auto-graded online. Over two hundred illustrations, including a selection of relevant xkcd comics. Chapters: The Function, The Field, The Vector, The Vector Space, The Matrix, The Basis, Dimension, Gaussian Elimination, The Inner Product, Special Bases, The Singular Value Decomposition, The Eigenvector, The Linear Program A new edition of this text, incorporating corrections and an expanded index, has been issued as of September 4, 2013, and will soon be available on Amazon. |
| bs in statistics and data science: 101 Careers in Mathematics: Fourth Edition Deanna Haunsperger, Robert Thompson, 2019-09-24 What can you do with a degree in math? This book addresses this question with 125 career profiles written by people with degrees and backgrounds in mathematics. With job titles ranging from sports analyst to science writer to inventory specialist to CEO, the volume provides ample evidence that one really can do nearly anything with a degree in mathematics. These professionals share how their mathematical education shaped their career choices and how mathematics, or the skills acquired in a mathematics education, is used in their daily work. The degrees earned by the authors profiled here are a good mix of bachelors, masters, and PhDs. With 114 completely new profiles since the third edition, the careers featured within accurately reflect current trends in the job market. College mathematics faculty, high school teachers, and career counselors will all find this a useful resource. Career centers, mathematics departments, and student lounges should have a copy available for student browsing. In addition to the career profiles, the volume contains essays from career counseling professionals on the topics of job-searching, interviewing, and applying to graduate school. |
| bs in statistics and data science: Nonparametric Methods P. R. Krishnaiah, Pranab Kumar Sen, 1984 Classical developments. Linear models. Order statistics and empitical distribution. Estimation procedures. Stochastic aproximation and density estimation. Life testing and reliability. Miscellaneous topics. Applications. Tables. |
| bs in statistics and data science: Predictive Analytics Eric Siegel, 2016-01-12 Mesmerizing & fascinating... —The Seattle Post-Intelligencer The Freakonomics of big data. —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a |
| bs in statistics and data science: Bioinformatics For Dummies Jean-Michel Claverie, Cedric Notredame, 2011-02-10 Were you always curious about biology but were afraid to sit through long hours of dense reading? Did you like the subject when you were in high school but had other plans after you graduated? Now you can explore the human genome and analyze DNA without ever leaving your desktop! Bioinformatics For Dummies is packed with valuable information that introduces you to this exciting new discipline. This easy-to-follow guide leads you step by step through every bioinformatics task that can be done over the Internet. Forget long equations, computer-geek gibberish, and installing bulky programs that slow down your computer. You’ll be amazed at all the things you can accomplish just by logging on and following these trusty directions. You get the tools you need to: Analyze all types of sequences Use all types of databases Work with DNA and protein sequences Conduct similarity searches Build a multiple sequence alignment Edit and publish alignments Visualize protein 3-D structures Construct phylogenetic trees This up-to-date second edition includes newly created and popular databases and Internet programs as well as multiple new genomes. It provides tips for using servers and places to seek resources to find out about what’s going on in the bioinformatics world. Bioinformatics For Dummies will show you how to get the most out of your PC and the right Web tools so you'll be searching databases and analyzing sequences like a pro! |
| bs in statistics and data science: Theory of Point Estimation Erich L. Lehmann, George Casella, 2006-05-02 This second, much enlarged edition by Lehmann and Casella of Lehmann's classic text on point estimation maintains the outlook and general style of the first edition. All of the topics are updated, while an entirely new chapter on Bayesian and hierarchical Bayesian approaches is provided, and there is much new material on simultaneous estimation. Each chapter concludes with a Notes section which contains suggestions for further study. This is a companion volume to the second edition of Lehmann's Testing Statistical Hypotheses. |
| bs in statistics and data science: 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/ |
| bs in statistics and data science: Think Bayes Allen B. Downey, 2021-05-18 If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and you'll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but there aren't many resources available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start. Use your programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing Get started with simple examples, using coins, dice, and a bowl of cookies Learn computational methods for solving real-world problems |
| bs in statistics and data science: Introduction to Bioinformatics Arthur M. Lesk, 2019 Lesk provides an accessible and thorough introduction to a subject which is becoming a fundamental part of biological science today. The text generates an understanding of the biological background of bioinformatics. |
| bs in statistics and data science: Critical Sports Studies Nicholas Villanueva, Jr., 2019-12-05 Critical Sports Studies: A Document Reader provides students with a selection of essays that examine social problems in sport. Readers are challenged to critically consider various topics to better understand how the global phenomenon of sport can lead to challenges both on and off the field. The opening chapter introduces the study of sport in society as an academic discipline. Later chapters cover amateurism in sport, sports and politics, and the role of media in |
| bs in statistics and data science: 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 |
| bs in statistics and data science: Principles and Methods for Data Science , 2020-05-27 Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more. |
| bs in statistics and data science: White Awareness Judy H. Katz, 1978 Stage 1. |
| bs in statistics and data science: Machine Learning and Data Science Daniel D. Gutierrez, 2015 This book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. |
| bs in statistics and data science: Introduction to Probability Joseph K. Blitzstein, Jessica Hwang, 2014-07-24 Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment. |
| bs in statistics and data science: Principles of Applied Statistics D. R. Cox, Christl A. Donnelly, 2011-07-28 Applied statistics is more than data analysis, but it is easy to lose sight of the big picture. David Cox and Christl Donnelly distil decades of scientific experience into usable principles for the successful application of statistics, showing how good statistical strategy shapes every stage of an investigation. As you advance from research or policy question, to study design, through modelling and interpretation, and finally to meaningful conclusions, this book will be a valuable guide. Over a hundred illustrations from a wide variety of real applications make the conceptual points concrete, illuminating your path and deepening your understanding. This book is essential reading for anyone who makes extensive use of statistical methods in their work. |
| bs in statistics and data science: Quantitative Financial Analytics: The Path To Investment Profits Edward E Williams, John A Dobelman, 2017-07-20 This book provides a comprehensive treatment of the important aspects of investment theory, security analysis, and portfolio selection, with a quantitative emphasis not to be found in most other investment texts.The statistical analysis framework of markets and institutions in the book meets the need for advanced undergraduates and graduate students in quantitative disciplines, who wish to apply their craft to the world of investments. In addition, entrepreneurs will find the volume to be especially useful. It also contains a clearly detailed explanation of many recent developments in portfolio and capital market theory as well as a thorough procedural discussion of security analysis. Professionals preparing for the CPA, CFA, and or CFP examinations will also benefit from a close scrutiny of the many problems following each chapter.The level of difficulty progresses through the textbook with more advanced treatment appearing in the latter sections of each chapter, and the last chapters of the volume. |
| bs in statistics and data science: Graduate STEM Education for the 21st Century National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on Revitalizing Graduate STEM Education for the 21st Century, 2018-09-21 The U.S. system of graduate education in science, technology, engineering, and mathematics (STEM) has served the nation and its science and engineering enterprise extremely well. Over the course of their education, graduate students become involved in advancing the frontiers of discovery, as well as in making significant contributions to the growth of the U.S. economy, its national security, and the health and well-being of its people. However, continuous, dramatic innovations in research methods and technologies, changes in the nature and availability of work, shifts in demographics, and expansions in the scope of occupations needing STEM expertise raise questions about how well the current STEM graduate education system is meeting the full array of 21st century needs. Indeed, recent surveys of employers and graduates and studies of graduate education suggest that many graduate programs do not adequately prepare students to translate their knowledge into impact in multiple careers. Graduate STEM Education for the 21st Century examines the current state of U.S. graduate STEM education. This report explores how the system might best respond to ongoing developments in the conduct of research on evidence-based teaching practices and in the needs and interests of its students and the broader society it seeks to serve. This will be an essential resource for the primary stakeholders in the U.S. STEM enterprise, including federal and state policymakers, public and private funders, institutions of higher education, their administrators and faculty, leaders in business and industry, and the students the system is intended to educate. |
| bs in statistics and data science: Probability and Statistics Michael J. Evans, Jeffrey S. Rosenthal, 2004 Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students. |
| bs in statistics and data science: Think Stats Allen B. Downey, 2011-07-01 If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts. Develop your understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Learn topics not usually covered in an introductory course, such as Bayesian estimation Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data |
| bs in statistics and data science: Introduction to Probability, Statistics, and Random Processes Hossein Pishro-Nik, 2014-08-15 The book covers basic concepts such as random experiments, probability axioms, conditional probability, and counting methods, single and multiple random variables (discrete, continuous, and mixed), as well as moment-generating functions, characteristic functions, random vectors, and inequalities; limit theorems and convergence; introduction to Bayesian and classical statistics; random processes including processing of random signals, Poisson processes, discrete-time and continuous-time Markov chains, and Brownian motion; simulation using MATLAB and R. |
Major Academic Plan - BS in Statistics: Data Science
Students with a Data Science emphasis leave BYU with a multi-faceted, disciplined, and flexible approach to data, a rich vocabulary for working with others in data-focused disciplines, and a …
Statistics (BS) - North Carolina State University
The Bachelor of Science in Statistics curriculum provides foundational training for careers in statistics and data science, and also prepares students for graduate study in statistics or …
BS IN STATISTICS - catalog.csus.edu
The statistics bachelor’s degree provides students with a solid foundation in the theory, methods, and applications of statistical analysis. Students will analyze real world data sets, model …
UT-BS-Statistics-and-Data-Science-4-Yr-Plan-22-24-Catalog
The University of Texas at Austin Bachelor of Science, Statistics & Data Science, 120 Hours 2022-2024 Catalog Expires Summer 2030 Four-year Degree Suggestion (for planning …
STATISTICS AND DATA SCIENCE BS - UC Santa Barbara
STATISTICS AND DATA SCIENCE, B.S. – 2022-2023 (TRANSFER STUDENTS) This schedule is just an example and should be used as a guide. o This schedule assumes the student has …
Statistics and Data Science, B.S. - Loyola Marymount University
Students understand the implications of study design, consider ethics in data collection, can select appropriate statistical methods for data analysis, and can explain the limitations of their …
STATISTICAL DATA SCIENCE (BS) - catalog.uconn.edu
STATISTICAL DATA SCIENCE (BS) The Statistical Data Science major gives students a broad training in the following core areas of data science:
Statistics: Data Science, BS - catalog.okstate.edu
Courses used to satisfy the General Education English Composition, U.S. History, American Government, and Mathematics or Statistics requirements will not count toward the 54-hour …
BACHELOR OF SCIENCE IN STATISTICS AND DATA …
The BSc. in Statistics and Data Science programme is designed to produce graduates who are well-acquainted with fundamental concepts and techniques in the field by exposing students to …
Statistics & Data Science - IIT Kanpur
Beginning academic year 2021-2022, the Department of Mathematics & Statistics at the Indian Institute of Technology, Kanpur will offer a new BS program in “Statistics and Data Science”.
Statistics, Data Science, BS - bulletin.uakron.edu
Official degree requirements are established at the time of transfer and admission to the degree-granting college. Students should refer to the Degree Progress Report (Stellic) which is …
Bachelor of Science (BS) Applied Statistics and Data Science
Bachelor of Science (BS) Applied Statistics and Data Science 2024-2026 ... The 2024-2026 list of core courses can be found in the 2024-2026 Undergraduate Catalog: www.utrgv.edu/catalog > …
Statistics: Data Science, BS - Oklahoma State …
Requirements for Students Matriculating in or before Academic Year 2024-2025. Learn more about University Academic Regulation. 3.1 (http://catalog.okstate.edu/university-academic …
ut-bs-statistics-and-data-science-degree-checklist-22-24-catal…
Bachelor of Science in Statistics and Data Science (BS) 2022–24 Checklist ... ADDITIONAL GRADUATION REQUIREMENTS
Major Academic Plan - BS in Statistics: Data Science
Statisticians apply sophisticated methods to increasingly massive data sets to discover insights into important business, government, environmental, and health policy questions.
Bachelor of Science in Statistics and Data Science
The Bachelor of Science in Statistics and Data Science (SDS) provides students with foundational training and marketable skills in statistics and data science. The curriculum is designed to …
Statistical Science and Data Analytics BS - Temple University
The Department of Statistics, Operations and Data Science offers the Bachelor of Science in Statistical Science and Data Analytics (SSDA). This degree provides students with knowledge …
BS in Statistics: Data Science (696236) MAP Sheet
Students with a Data Science emphasis leave BYU with a multi-faceted, disciplined, and flexible approach to data, a rich vocabulary for working with others in data-focused disciplines, and a …
Statistics and Actuarial Science - University of Iowa
Statistics majors may not earn a major in data science. The Department of Statistics and Actuarial Science and the Department of Computer Science collaborate to offer the major in data …
Curriculum of BS (H) Statistics - Government College …
Curriculum of BS (H) Statistics SEMESTER-I STA-301 Introduction to Statistics 4(3-1) Meanings of Statistics. Main branches of Statistics. Types of variables. Measurements scales. Statistical …
BS in Data Science - University of Missouri
The BS in Data Science is an interdisciplinary degree built on a core triad of fields: statistics, mathematics, and computer science. Students in this ... within the chosen focus area of …
BS in Data Science - catalog.utoledo.edu
BS in Data Science 1 BS IN DATA SCIENCE The B.S. in Data Science is an interdisciplinary program that requires a minimum of 120 hours of coursework and is designed to provide the …
Statistics (B.S.) All Tracks - UC Davis
and explanatory models for large and complex data. It is recommended for students interested in pursuing graduate programs in statistics, machine learning, or data science, as well as for …
Statistics - 5-Year Bachelor of Science and Master of Science …
degree (BS) and a Master of Science (MS) in Statistical Data Science graduate degree in five years. ... for the combined BS in Statistics /MS in Statistical Data Science. It is recognized that …
Data Science, B.S. - University of California, Irvine
Data Science, B.S. 1 Data Science, B.S. The Data Science Major prepares students for a career in data analysis, combining foundational statistical concepts with computational principles from …
Undergraduate Manual - UC Davis
data. It is recommended for students interested in pursuing graduate programs in statistics, machine learning, or data science, as well as for students interested in learning statistical …
Data Science, BS - University of Iowa
Data Science, BS 1 Data Science, BS Learning Outcomes Data Curation Skills Graduates will be able to: • understand issues associated with data collection, ... computer science or statistics, a …
BS STATISTICS CURRICULUM - UPLB CAS Institute of Statistics
Introductory Data Analytics PR. STAT 151, STAT 175, and CMSC 21 STAT 190. Special Problems PR. COI STAT 199. Undergraduate Seminar PR. COI Units 3 3 3 3 2 1 15 Total number of …
6FKRRO RI $UWV DQG 6FLHQFHV &XUULFXOXP (IIHFWLYH …
&rxuvh 'hvfulswlrq &uhglw3uri3uh uhtxlvlwhv 6fkrro ri $uwv dqg 6flhqfhv %dfkhoru ri 6flhqfh lq 'dwd 6flhqfh &xuulfxoxp (iihfwlyh $whqhr gh 'dydr 8qlyhuvlw\ 3d\ 8qlwv
BS in Statistics: Statistical Science (695220) MAP Sheet
BS in Statistics: Statistical Science (695220) MAP Sheet Physical and Mathematical Sciences, Statistics ... STAT 426 - Data Science Methods and Applications in Statistics 3.0 STA 4 35-N …
Statistics: Data Science, BS - Oklahoma State …
Statistics: Data Science, BS 1 STATISTICS: DATA SCIENCE, BS Degree Requirements Requirements for Students Matriculating in or before Academic Year 2024-2025.
B.S. Degree – Statistics Major The Ohio State University …
Jun 3, 2022 · The program requirements for the Bachelor of Science degree with a major in Statistics are given below. A minimum of 50 credit hours (excluding prerequisites and GE and …
B.S. in Data Science - Rowan University
Mar 5, 2024 · B.S. in Data Science Updated 3/5/24 p. 1 of 3 Academic Program Guide for New First-Year Students (Effective Fall 2023) Department of Mathematics …
DATA SCIENCE - bulletin.columbia.edu
Computer Science A exam, along with an exemption from COMS W1004 Introduction to Computer Science and Programming in Java. However, we recommend that you take COMS …
Statistics, BS - University of Iowa
Statistics, BS Mathematical Statistics Track This sample plan is currently being reviewed and will be added at a later date. Statistical Computing and Data Science Track This sample plan is …
FAQs for Applied Statistics and Data Analytics - Rochester …
The College of Science has renamed the BS degree in applied statistics and actuarial science to applied statistics and data analytics. Why did we change the degree name? The new name …
Statistical Science and Data Analytics BS - Temple University
Statistics, Operations and Data Science Department 1810 Liacouras Walk, Room 339 airoldi@temple.edu Lauren Burns, Deputy Chair Statistics, Operations and Data Science …
Computational and Data Sciences, BS - George Mason …
The BS is a transformative approach that integrates science at George Mason University based on the combination of real-world computer science skills, data acquisition and analysis, …
STATISTICS Undergraduate Programs - UC Davis
Statistics 1 STATISTICS College of Letters & Science Jiming Jiang, Ph.D., Chairperson of the Department; term ends June 30, 2026. The Department of Statistics offers undergraduate …
Bachelor of Science in Statistics and Data Science (BS) …
study other than Statistics and Data Sciences 12 The following courses in statistics and data sciences: 28 a. Core courses for the major: SDS 313 SDS 315 SDS 431 SDS 334 ... Bachelor …
Curriculum Guidelines for Undergraduate Programs in …
Data Science is experiencing rapid and unplanned growth, spurred by the proliferation of complex and rich data in science, industry and government. Fueled in part by reports such as the widely …
Annexure-C Curriculum for BS Data Science
16 5 CS302 Advanced Statistics Domain Core 3(2-3) 17 6 CS318 CS306, CS307 Data Mining Domain Core 3(2-3) 18 7 CS329 CS330 Data Visualization Domain Core 3(2-3) ... The …
Revised General Merit List for the Admission of BS Statistics, …
STATISTICS, COMPUTER SCIENCE 66.131 PU-CSS 25-JUN-2023 17042 78 100 19.50 0 85.631 6 126316 Fatima Usman Saqib Usman 8-Feb-04 No 1081 1100 I.C.S BISE Lahore IA 2022 …
Statistics: Data Science, BS - Oklahoma State …
Statistics: Data Science, BS 1 STATISTICS: DATA SCIENCE, BS Example Plan of Study Finish in Four Plan of Study The plan below is an example of how students can successfully complete …
Also, students cannot get credit for CMPSC 8 at UCSB if …
STATISTICS AND DATA SCIENCE MAJOR, B.S. – 2023-2024 To be admitted to the full major, students must complete all pre-major courses with a 2.5 or higher UC grade-point average. All …
STATISTICS AND APPLIED PROBABILITY DEPARTMENT …
College of Letters and Science for list of additional degree requirements. University of California, Santa Barbara PRE-STATISTICS AND DATA SCIENCE MAJOR, B.A. or B.S. – 2023-2024 To …
PLACEMENT SUMMARY - Indian Institute of Technology Madras
DEPARTMENT-WISE DATA ON REGISTERED AND PLACED CANDIDATES. B.TECH Salary Range 36 52 56 47 45 66 22 28 28 37 38 36 27 39 15 20 0 10 20 30 40 50 60 70 No. of …
STATISTICS, BS - guide.wisc.edu
Statistics, BS 1 STATISTICS, BS REQUIREMENT S UNIVERSITY GENERAL EDUCATION REQUIREMENTS All undergraduate students at the University of Wisconsin–Madison are …
Data Science, BS - University of Wisconsin–Madison
Data Science, BS 1 DATA SCIENCE, BS HOW TO GET IN HOW TO GET IN To declare the data science major, student should meet with a data science major advisor prior to attaining senior …
ut-bs-statistics-and-data-science-degree-checklist-24-26-cat…
study other than Statistics and Data Sciences 12 The following courses in statistics and data sciences: 28 a. Core courses for the major: SDS 313 SDS 315 SDS 431 SDS 334 ... Bachelor …
ut-bs-statistics-and-data-science-4-yr-plan-22-24-catalog
Bachelor of Science, Statistics & Data Science, 120 Hours 2022-2024 Catalog Expires Summer 2030 Four-year Degree Suggestion (for planning purposes only) Currently enrolled students …
Major Academic Plan - BS in Statistics: Applied Statistics
BS in Statistics: Applied Statistics & Analytics (695234) 2020-2021 Program Requirements (50.5 Credit Hours) STAT 121 - Principles of Statistics 3.0 ... STAT 426 - Data Science Methods and …
Statistics and Data Science (BS) + Economics (BS): Sample …
Statistics I STAT 5 10 (3)** Intro. Probability and Statistics II STAT 511(3)** Intro. to0 the SAS Computing STAT 725 (1) Statistics and Data Science (BS) + Economics (BS): Sample …
DATA SCIENCES, B.S. (SCIENCE)
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 …
Data Analytics, BS (College of Letters and Science)
Data Analytics, BS (College of Letters and Science) 1 DATA ANALYTICS, BS (COLLEGE OF LETTERS AND SCIENCE) There is data all around us. Businesses are looking to hire people …
FEE/ DUES SCHEDULE - University of the Punjab
Archaelogy, Statistical Sciences (Except BS Statistics & BS Bio-Statistics) Space Science, English (2Years ADP), Political Science ... BS Software Engineering, Data Sciences 90,000 …
B.SC. (CBCS) (DATA SCIENCE) SYLLABUS (With Mathematics …
BS-606-O Optional Data Science Project 4 4 100 Note: 1. No of credits = No of theory hours for teaching = twice the no. of credits for practical teaching. ... Approved B.Sc. (Data Science) in …
Statistics: Statistics and Data Science, MS - University of …
STATISTICS: STATISTICS AND DATA SCIENCE, MS This is a named option (formally documented sub-major) professional ... •Students who have BS degrees or expected to obtain …
UNIVERSITY OF THE PHILIPPINES CEBU College of Science
College of Science Bachelor of Science in Statistics Study Plan 2021 Curriculum, Approved during the 1369th UP BOR meeting on 25 March 2021 Effective First Semester, A.Y. 2021 - 2022 ...
Biological Data Science, MS
Biology, BS Environmental Science, BA Environmental Science, BS Pharmacology and Toxicology, BS Statistics, BS Acceptance to the graduate program requires a separate …
Data Science Undergraduate Program Example Four Year Plan
Statistics) Bio_Sci Sci 1 Sci 2 . Year 3 Fall Winter Spring . STAT 156A (Mathematical Statistics With Applications For Data Science I) STAT 156B (Mathematical Statistics With Applications …
Statistics and Actuarial Science - catalog.registrar.uiowa.edu
The Department of Statistics and Actuarial Science and the Department of Computer Science collaborate to offer the major in data science. The BS in data science is administered by the …
Data Science, BS - Marquette University
Data Science, BS 1 Data Science, BS Program Co-Directors: Daniel Rowe, Ph.D and Michael Zimmer, Ph.D Data Science is the emerging field that seeks to extract and quantify knowledge …
Welcome to the Statistics Major Data Science Emphasis
Aug 19, 2024 · C S 180 - Intro to Data Science 3.0 HLTH 440 - Statistical Computing in Epi 3.0 IS 520 - Spreadsheet Automation 3.0 STAT 286 - Data Science Ecosystems 3.0 are Requirement …
Mathematical and Statistical Sciences - University of Colorado …
2 Mathematical and Statistical Sciences • Option 2: Have a GPA of 3.5 or better in upper-division math courses taken at CU Denver and must complete an honors project.
Data Science, B.S. - Saint Louis University
Data Science, B.S. 1 DATA SCIENCE, B.S. The Saint Louis University Bachelor of Science in Data Science is an interdisciplinary program supported by the Department of Computer ...
Data Science and Statistics, Bachelor of Science (B
Data Science and Statistics, Bachelor of Science (B.S.) 1 DATA SCIENCE AND STATISTICS, BACHELOR OF SCIENCE (B.S.) Program Objectives Upon successful completion of this …
CERTIFICATE PROGRAMME IN DATA SCIENCE & MACHINE …
Statistics for Data Science Module 2 Measures and descriptors of data Distributions Estimation Exploratory data analysis Hypothesis testing and evaluation PROGRAMME MODULES The …
