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| comp sci vs data science: Concepts in Statistical Mechanics Art Hobson, 1987 This reference reviews many principles and practices of microbiology in the cosmetic industry to address globalization of products. Supplying chapters from leading authorities around the world, this guide highlights emerging issues in nanotechnology, governmental regulation, and efficacy testing, as well as demonstrates the impact of microbiological testing in clinical studies. Emphasizing the globalization of products in industry, this source ranges from discussions of the evolution of cosmetic and drug microbiology in different countries to preservative efficacy testing, hurdle technology, and nanotechnology ... introduces emerging 'lab on a chip' technologies for the testing of microorganisms and their products at the molecular level ... describes critical factors that must be considered in the testing and selection of preservatives for product formulations ... presents an overview of skin microbiology ... and updates progress on global harmonization of microbiological test methods.--BOOK JACKET. |
| comp sci vs 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. |
| comp sci vs data science: 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. |
| comp sci vs 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. |
| comp sci vs data science: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks. |
| comp sci vs data science: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| comp sci vs data science: Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud, Global Edition Paul Deitel, 2021-05-31 |
| comp sci vs data science: The 9 Pitfalls of Data Science Gary Smith, Jay Cordes, 2019 The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. |
| comp sci vs data science: 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) |
| comp sci vs data science: Pro T-SQL 2012 Programmer's Guide Michael Coles, Scott Shaw, Jay Natarajan, Rudi Bruchez, 2012-11-29 Pro T–SQL 2012 Programmer’s Guide is every developer’s key to making full use of SQL Server 2012’s powerful, built–in Transact–SQL language. Discussing new and existing features, the book takes you on an expert guided tour of Transact–SQL functionality. Fully functioning examples and downloadable source code bring technically accurate and engaging treatment of Transact–SQL into your own hands. Step–by–step explanations ensure clarity, and an advocacy of best–practices will steer you down the road to success. Transact–SQL is the language developers and DBAs use to interact with SQL Server. It’s used for everything from querying data, to writing stored procedures, to managing the database. New features in T-SQL 2012 include full support for window functions, stored sequences, the ability to throw errors, data paging, and more. All these important new features are covered in this book. Developers and DBAs alike can benefit from the expressive power of Transact-SQL, and Pro T-SQL 2012 Programmer's Guide provides the gateway to success in applying this increasingly important database language to everyday business and technical tasks. |
| comp sci vs data science: Concise Survey of Computer Methods Peter Naur, 1974 |
| comp sci vs data science: Introduction to Data Science Laura Igual, Santi Seguí, 2017-02-22 This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website. |
| comp sci vs 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 |
| comp sci vs data science: 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 |
| comp sci vs 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. |
| comp sci vs data science: Statistical Learning and Data Science Mireille Gettler Summa, Leon Bottou, Bernard Goldfarb, Fionn Murtagh, Catherine Pardoux, Myriam Touati, 2011-12-19 Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor |
| comp sci vs data science: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| comp sci vs data science: The Art of Readable Code Dustin Boswell, Trevor Foucher, 2011-11-03 Chapter 5. Knowing What to Comment; What NOT to Comment; Don't Comment Just for the Sake of Commenting; Don't Comment Bad Names--Fix the Names Instead; Recording Your Thoughts; Include Director Commentary; Comment the Flaws in Your Code; Comment on Your Constants; Put Yourself in the Reader's Shoes; Anticipating Likely Questions; Advertising Likely Pitfalls; Big Picture Comments; Summary Comments; Final Thoughts--Getting Over Writer's Block; Summary; Chapter 6. Making Comments Precise and Compact; Keep Comments Compact; Avoid Ambiguous Pronouns; Polish Sloppy Sentences. |
| comp sci vs 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. |
| comp sci vs data science: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| comp sci vs data science: 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. |
| comp sci vs data science: Introduction to Machine Learning with Python Andreas C. Müller, Sarah Guido, 2016-09-26 Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.You'll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system. |
| comp sci vs data science: Python for Programmers Paul Deitel, Harvey Deitel, 2019-03-15 The professional programmer’s Deitel® guide to Python® with introductory artificial intelligence case studies Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python–one of the world’s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details. In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® WatsonTM, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, SparkTM and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google TranslateTM, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more. Features 500+ hands-on, real-world, live-code examples from snippets to case studies IPython + code in Jupyter® Notebooks Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions Procedural, functional-style and object-oriented programming Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames Static, dynamic and interactive visualizations Data experiences with real-world datasets and data sources Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® WatsonTM, machine learning, deep learning, computer vision, Hadoop®, SparkTM, NoSQL, IoT Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more Accompanying code examples are available here: http://ptgmedia.pearsoncmg.com/imprint_downloads/informit/bookreg/9780135224335/9780135224335_examples.zip. Register your product for convenient access to downloads, updates, and/or corrections as they become available. See inside book for more information. |
| comp sci vs data science: Fundamental Concepts in Computer Science Erol Gelenbe, 2009 This book presents fundamental contributions to computer science as written and recounted by those who made the contributions themselves. As such, it is a highly original approach to a OC living historyOCO of the field of computer science. The scope of the book is broad in that it covers all aspects of computer science, going from the theory of computation, the theory of programming, and the theory of computer system performance, all the way to computer hardware and to major numerical applications of computers. |
| comp sci vs data science: Data Pipelines Pocket Reference James Densmore, 2021-02-10 Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting |
| comp sci vs data science: Data Science and Big Data Computing Zaigham Mahmood, 2016-07-05 This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis. |
| comp sci vs data science: Guide to Teaching Computer Science Orit Hazzan, Tami Lapidot, Noa Ragonis, 2015-01-07 This textbook presents both a conceptual framework and detailed implementation guidelines for computer science (CS) teaching. Updated with the latest teaching approaches and trends, and expanded with new learning activities, the content of this new edition is clearly written and structured to be applicable to all levels of CS education and for any teaching organization. Features: provides 110 detailed learning activities; reviews curriculum and cross-curriculum topics in CS; explores the benefits of CS education research; describes strategies for cultivating problem-solving skills, for assessing learning processes, and for dealing with pupils’ misunderstandings; proposes active-learning-based classroom teaching methods, including lab-based teaching; discusses various types of questions that a CS instructor or trainer can use for a range of teaching situations; investigates thoroughly issues of lesson planning and course design; examines the first field teaching experiences gained by CS teachers. |
| comp sci vs 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. |
| comp sci vs data science: R Packages Hadley Wickham, 2015-03-26 Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham’s package development philosophy. In the process, you’ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You’ll learn to focus on what you want your package to do, rather than think about package structure. Learn about the most useful components of an R package, including vignettes and unit tests Automate anything you can, taking advantage of the years of development experience embodied in devtools Get tips on good style, such as organizing functions into files Streamline your development process with devtools Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN) Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr |
| comp sci vs data science: Introduction to Machine Learning Ethem Alpaydin, 2014-08-22 Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments. |
| comp sci vs data science: Building Data Science Teams DJ Patil, 2011-09-15 As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success. Topics include: What it means to be data driven. The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team. |
| comp sci vs data science: 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 |
| comp sci vs data science: A/AS Level Computer Science for WJEC/Eduqas Student Book Mark Thomas, Alistair Surrall, Adam Hamflett, 2017-10-05 Written for the WJEC/Eduqas A/AS Level Computer Science specifications for first teaching from 2015, this print student book helps students build their knowledge and master underlying computing principles and concepts. The student book develops computational thinking, programming and problem-solving skills. Suitable for all abilities, it puts computing into context and gives students a real-life view on professional applications of computing skills. Answers to end-of-chapter questions are located in the free online teacher's resource. A Cambridge Elevate enhanced edition is also available. |
| comp sci vs data science: The Fourth Paradigm Anthony J. G. Hey, 2009 Foreword. A transformed scientific method. Earth and environment. Health and wellbeing. Scientific infrastructure. Scholarly communication. |
| comp sci vs data science: Data Structures and Algorithm Analysis in Java, Third Edition Clifford A. Shaffer, 2012-09-06 Comprehensive treatment focuses on creation of efficient data structures and algorithms and selection or design of data structure best suited to specific problems. This edition uses Java as the programming language. |
| comp sci vs data science: 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. |
| comp sci vs data science: Problem Solving with Computers Paul Calter, 1973 |
| comp sci vs data science: Black Hole Focus Isaiah Hankel, 2014-05-05 ...an absurdly motivating book. –A.J. Jacobs, New York Times bestselling author Don’t get stuck on a career path you have no passion for. Don’t waste your intelligence on something that doesn’t really mean anything more to you than a paycheck. Let Isaiah Hankel help you define a focus so powerful that everything in your life will be pulled towards it. Create your purpose and change your life. Be focused. Be fulfilled. Be successful. Black Hole Focus has been endorsed by top names in business, entrepreneurship, and academia, including 4 times New York Times bestseller AJ Jacobs and Harvard Medical School Postdoc Director Dr. Jim Gould. The book is broken up into 3 different sections; the first section shows you why you need a purpose in life, the second section shows you how to find your new purpose, and the third section shows you how to achieve your goals when facing adversity. In this book, you will learn: How to understand what you really want in life and how to get it Why people with a powerful purpose live to 100 How to rapidly improve focus and change your life using the secret techniques of an international memory champion How people like Jim Carrey, Oprah Winfrey, and J.K. Rowling transformed pain into purpose How to start a business by avoiding willpower depletion and the life hack lie Black Hole Focus includes exclusive case studies from medical practitioners, research scientists, lawyers, corporate executives and small business owners who have used the techniques described in this book to achieve massive success in their own lives. About the Author: Dr. Hankel is an internationally recognized expert in the biotechnology industry and prolific public speaker. He's given over 250 seminars in 22 different countries while working with many of the world's most respected companies and institutions, including Harvard University, Oxford University, Roche Pharmaceuticals, Eli Lilly & Company, Baxter International and Pfizer. Dr. Hankel uses the science of purpose and the principles of entrepreneurship to help people achieve their biggest goals. |
| comp sci vs data science: ICT for GCSE Tim Roderick, Geoff Rushbrook, 2002-04-04 This is an illustrated summary book in full colour for classroom use and for examination revision, covering the requirements of GCSE Specifications from September 2001 in information technology/computing. |
| comp sci vs data science: The Mathematics of Data Michael W. Mahoney, John C. Duchi, Anna C. Gilbert, 2018-11-15 Nothing provided |
Sample 4-Year Plan: Computer Sciences and Data Science
Jul 8, 2022 · Below is a sample 4-year plan that shows how a student might navigate both the Computer Sciences major and Data Science major. This plan indicates both potential areas of …
Master's Programs Overview and Comparison Data
Sep 15, 2020 · data science systems, with emphasis on providing experimental evidence for design decisions; design, implement and evaluate a user experience prototype for a user need.
A Comparison of Key Concepts in Data Analytics and Data …
In this study, we compare the topics and tools that are presented in Data Analytics and Data Science programs. Our research is relevant to potential students who need to evaluate the …
Data Analytics vs. Data Science: A Study of Similarities and
Our investigation clarifies and illustrates the similarities and differences between undergraduate data analytics and data science programs. 1. INTRODUCTION. Inexpensive data storage and …
Computer science guide - IB CompSci Hub
Computer science requires an understanding of the fundamental concepts of computational thinking as well as knowledge of how computers and other digital devices operate. The …
Clarification Guide COMPUTER SCIENCE - OCR
Candidates need to understand the differences between the CISC and RISC processors and the key features and benefits of each. They should be aware of the relative benefits of each …
Data Science Major - mps.rutgers.edu
Overall, the program provides students with a comprehensive curriculum that prepares them for a career in data science with a focus on economics or computer science. The Data Science …
COMP 543: Tools and Models for Data Science [Graduate Level]
data science models, including gradient descent, logistic regression, outlier detection, deep neural nets and more. Prerequisites: Mathematical sophistication (calculus, statistics) and …
Bachelor of Science, Computer Science program guide
The Bachelor of Science in Computer Science prepares students for a career in the high demand field of Computer Science. Upon program completion, students will apply their learned …
Computer Sciences (COMP SCI)
Provides a broad overview of computing at an introductory level, including topics such as security, robotics, and artificial intelligence. Increases understanding of how computers work and how …
The impact of OCR’s change from J276 to J277 specifications
Comparison of OCR GCSE Computer Science J276 and J277 Page 5 of 48 Provided courtesy of www.gcsecs.org Changes from pseudo-code in J276 to reference language in J277 Apart from …
COMP 250 - Introduction to Computer Science - McGill …
This course introduces you to two core topics in computer science: data structures and algorithms. You will learn basic data structures for lists (arrays, linked lists, stacks, queues), …
Computer Science Major, B.S. - University of North Carolina at …
The bachelor of science with a major in computer science is the preferred degree both for graduate study in computer science and for technical careers in software development, …
Computer Science Major - University of Maryland Catalog
Computer science is the study of computers and computational systems: their application, design, development and theory.
Computational thinking, problem-solving and programming
Linear search or sequential search is an algorithm to find an item in a list. It starts at the first element and compares each element to the one it’s looking for until it finds it. Commonly used …
Computer Science Paper 2 NEA Computer Science: NEA - Mr …
https://msdn.microsoft.com/en-us/library/ms379572(v=vs.80).aspx Features you will have to use to maximise the complexity. • Data structures • Objects • Recursion • File operations • sorting
Quick Guide for CC, GS, and Barnard CS Students - Columbia …
Take Intro to Computer Science and Programming in Java (W1004) or Honors Intro to Computer Science (W1007) in your 1st year if possible. These courses introduce you to basic computer …
DIFFERENCES BETWEEN THE COMPUTER SCIENCE AND …
and avionics software, computer games, data engineering, and web services and applications. Computer Science (90 credits) is a three-year program leading to the degree Bachelor of …
Syllabus Cambridge International AS & A Level Computer …
Cambridge International A Level Computer Science provides a foundation for the study of Computer Science or related courses in higher education. Equally it is suitable as part of a …
Sample 4-Year Plan: Computer Sciences and Data Science
Jul 8, 2022 · Below is a sample 4-year plan that shows how a student might navigate both the Computer Sciences major and Data Science major. This plan indicates both potential areas of …
Master's Programs Overview and Comparison Data
Sep 15, 2020 · data science systems, with emphasis on providing experimental evidence for design decisions; design, implement and evaluate a user experience prototype for a user need.
world of possibilities. All undergraduate students at the …
Computer science is a rich, collaborative, and varied field that you will find challenging, no matter where your individual interests lie. And there is more to computer science than programming.
A Comparison of Key Concepts in Data Analytics and Data …
In this study, we compare the topics and tools that are presented in Data Analytics and Data Science programs. Our research is relevant to potential students who need to evaluate the …
Data Analytics vs. Data Science: A Study of Similarities and
Our investigation clarifies and illustrates the similarities and differences between undergraduate data analytics and data science programs. 1. INTRODUCTION. Inexpensive data storage and …
Computer science guide - IB CompSci Hub
Computer science requires an understanding of the fundamental concepts of computational thinking as well as knowledge of how computers and other digital devices operate. The …
Clarification Guide COMPUTER SCIENCE - OCR
Candidates need to understand the differences between the CISC and RISC processors and the key features and benefits of each. They should be aware of the relative benefits of each …
Data Science Major - mps.rutgers.edu
Overall, the program provides students with a comprehensive curriculum that prepares them for a career in data science with a focus on economics or computer science. The Data Science …
COMP 543: Tools and Models for Data Science [Graduate Level]
data science models, including gradient descent, logistic regression, outlier detection, deep neural nets and more. Prerequisites: Mathematical sophistication (calculus, statistics) and …
Bachelor of Science, Computer Science program guide
The Bachelor of Science in Computer Science prepares students for a career in the high demand field of Computer Science. Upon program completion, students will apply their learned …
Computer Sciences (COMP SCI)
Provides a broad overview of computing at an introductory level, including topics such as security, robotics, and artificial intelligence. Increases understanding of how computers work and how …
The impact of OCR’s change from J276 to J277 specifications
Comparison of OCR GCSE Computer Science J276 and J277 Page 5 of 48 Provided courtesy of www.gcsecs.org Changes from pseudo-code in J276 to reference language in J277 Apart from …
COMP 250 - Introduction to Computer Science - McGill …
This course introduces you to two core topics in computer science: data structures and algorithms. You will learn basic data structures for lists (arrays, linked lists, stacks, queues), …
Computer Science Major, B.S. - University of North Carolina at …
The bachelor of science with a major in computer science is the preferred degree both for graduate study in computer science and for technical careers in software development, …
Computer Science Major - University of Maryland Catalog
Computer science is the study of computers and computational systems: their application, design, development and theory.
Computational thinking, problem-solving and …
Linear search or sequential search is an algorithm to find an item in a list. It starts at the first element and compares each element to the one it’s looking for until it finds it. Commonly used …
Computer Science Paper 2 NEA Computer Science: NEA
https://msdn.microsoft.com/en-us/library/ms379572(v=vs.80).aspx Features you will have to use to maximise the complexity. • Data structures • Objects • Recursion • File operations • sorting
Quick Guide for CC, GS, and Barnard CS Students - Columbia …
Take Intro to Computer Science and Programming in Java (W1004) or Honors Intro to Computer Science (W1007) in your 1st year if possible. These courses introduce you to basic computer …
DIFFERENCES BETWEEN THE COMPUTER SCIENCE AND …
and avionics software, computer games, data engineering, and web services and applications. Computer Science (90 credits) is a three-year program leading to the degree Bachelor of …
Syllabus Cambridge International AS & A Level Computer …
Cambridge International A Level Computer Science provides a foundation for the study of Computer Science or related courses in higher education. Equally it is suitable as part of a …
