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| data science capstone project ideas: Recommendation Engines Michael Schrage, 2020-09-01 How companies like Amazon, Netflix, and Spotify know what you might also like: the history, technology, business, and societal impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press's Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences you might also like. |
| data science capstone project ideas: Law and Policy for the Quantum Age Chris Jay Hoofnagle, Simson L. Garfinkel, 2022-01-06 The Quantum Age cuts through the hype to demystify quantum technologies, their development paths, and the policy issues they raise. |
| data science capstone project ideas: Why Data Science Projects Fail Douglas Gray, Evan Shellshear, 2024-09-05 The field of artificial intelligence, data science, and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects, and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven. This book aims to fix this by countering the AI hype with a dose of realism. Written by two experts in the field, the authors firmly believe in the power of mathematics, computing, and analytics, but if false expectations are set and practitioners and leaders don’t fully understand everything that really goes into data science projects, then a stunning 80% (or more) of analytics projects will continue to fail, costing enterprises and society hundreds of billions of dollars, and leading to non-experts abandoning one of the most important data-driven decision-making capabilities altogether. For the first time, business leaders, practitioners, students, and interested laypeople will learn what really makes a data science project successful. By illustrating with many personal stories, the authors reveal the harsh realities of implementing AI and analytics. |
| data science capstone project ideas: Handbook of Research on Foundations and Applications of Intelligent Business Analytics Zhaohao Sun, Zhiyou Wu, 2021 This book addresses research issues by investigating into foundations, technologies, and applications of intelligent business analytics, offering theoretical foundations, technologies, methodologies, and applications of intelligent business analytics in an integrated way-- |
| data science capstone project ideas: Real-World Software Projects for Computer Science and Engineering Students Varun Gupta, Anh Nguyen-Duc, 2021-02-23 Developing projects outside of a classroom setting can be intimidating for students and is not always a seamless process. Real-World Software Projects for Computer Science and Engineering Students is a quick, easy source for tackling such issues. Filling a critical gap in the research literature, the book: Is ideal for academic project supervisors. Helps researchers conduct interdisciplinary research. Guides computer science students on undertaking and implementing research-based projects This book explains how to develop highly complex, industry-specific projects touching on real-world complexities of software developments. It shows how to develop projects for students who have not yet had the chance to gain real-world experience, providing opportunity to become familiar with the skills needed to implement projects using standard development methodologies. The book is also a great source for teachers of undergraduate students in software engineering and computer science as it can help students prepare for the risk and uncertainty that is typical of software development in industrial settings. |
| data science capstone project ideas: Python Machine Learning Sebastian Raschka, 2015-09-23 Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models. |
| data science capstone project ideas: Business Intelligence Demystified Anoop Kumar V K, 2021-09-25 Clear your doubts about Business Intelligence and start your new journey KEY FEATURES ● Includes successful methods and innovative ideas to achieve success with BI. ● Vendor-neutral, unbiased, and based on experience. ● Highlights practical challenges in BI journeys. ● Covers financial aspects along with technical aspects. ● Showcases multiple BI organization models and the structure of BI teams. DESCRIPTION The book demystifies misconceptions and misinformation about BI. It provides clarity to almost everything related to BI in a simplified and unbiased way. It covers topics right from the definition of BI, terms used in the BI definition, coinage of BI, details of the different main uses of BI, processes that support the main uses, side benefits, and the level of importance of BI, various types of BI based on various parameters, main phases in the BI journey and the challenges faced in each of the phases in the BI journey. It clarifies myths about self-service BI and real-time BI. The book covers the structure of a typical internal BI team, BI organizational models, and the main roles in BI. It also clarifies the doubts around roles in BI. It explores the different components that add to the cost of BI and explains how to calculate the total cost of the ownership of BI and ROI for BI. It covers several ideas, including unconventional ideas to achieve BI success and also learn about IBI. It explains the different types of BI architectures, commonly used technologies, tools, and concepts in BI and provides clarity about the boundary of BI w.r.t technologies, tools, and concepts. The book helps you lay a very strong foundation and provides the right perspective about BI. It enables you to start or restart your journey with BI. WHAT YOU WILL LEARN ● Builds a strong conceptual foundation in BI. ● Gives the right perspective and clarity on BI uses, challenges, and architectures. ● Enables you to make the right decisions on the BI structure, organization model, and budget. ● Explains which type of BI solution is required for your business. ● Applies successful BI ideas. WHO THIS BOOK IS FOR This book is a must-read for business managers, BI aspirants, CxOs, and all those who want to drive the business value with data-driven insights. TABLE OF CONTENTS 1. What is Business Intelligence? 2. Why do Businesses need BI? 3. Types of Business Intelligence 4. Challenges in Business Intelligence 5. Roles in Business Intelligence 6. Financials of Business Intelligence 7. Ideas for Success with BI 8. Introduction to IBI 9. BI Architectures 10. Demystify Tech, Tools, and Concepts in BI |
| data science capstone project ideas: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
| data science capstone project ideas: MICCAI 2012 Workshop on Multi-Atlas Labeling Bennett Landman, Annemie Ribbens, Blake Lucas, Christos, Christos Davatzikos,, Brian Avants, Christian Ledig, Da Ma, Daniel Rueckert, Dirk Vandermeulen, Frederik Maes, Guray Erus, Jiahui Wang, Holly Holmes, Hongzhi Wang, Jimit Doshi, Joe Kornegay, Jose Manjon, Alexander Hammers, Alireza Akhondi-Asl, Andrew Asman, 2012-08-26 Characterization of anatomical structure through segmentation has become essential for morphological assessment and localizing quantitative measures. Segmentation through registration and atlas label transfer has proven to be a flexible and fruitful approach as efficient, non-rigid image registration methods have become prevalent. Label transfer segmentation using multiple atlases has helped to bring statistical fusion, shape modeling, and meta-analysis techniques to the forefront of segmentation research. Numerous creative approaches have proposed to use atlas information to apply labels to brain anatomy. However, it is difficult to evaluate the relative advantages and limitations of these methods as they have been applied on very different datasets. This workshop provides a snapshot of the current progress in the field through extended discussions and provides researchers an opportunity to characterize their methods on standardized data in a grand challenge. |
| data science capstone project ideas: Computational Intelligence in Data Science Lekshmi Kalinathan, Priyadharsini R., Madheswari Kanmani, Manisha S., 2022-09-28 This book constitutes the refereed post-conference proceedings of the Fifth IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2022, held virtually, in March 2022. The 28 revised full papers presented were carefully reviewed and selected from 96 submissions. The papers cover topics such as computational intelligence for text analysis; computational intelligence for image and video analysis; blockchain and data science. |
| data science capstone project ideas: JavaScript for Data Science Maya Gans, Toby Hodges, Greg Wilson, 2020-02-03 JavaScript is the native language of the Internet. Originally created to make web pages more dynamic, it is now used for software projects of all kinds, including scientific visualization and data services. However, most data scientists have little or no experience with JavaScript, and most introductions to the language are written for people who want to build shopping carts rather than share maps of coral reefs. This book will introduce you to JavaScript's power and idiosyncrasies and guide you through the key features of the language and its tools and libraries. The book places equal focus on client- and server-side programming, and shows readers how to create interactive web content, build and test data services, and visualize data in the browser. Topics include: The core features of modern JavaScript Creating templated web pages Making those pages interactive using React Data visualization using Vega-Lite Using Data-Forge to wrangle tabular data Building a data service with Express Unit testing with Mocha All of the material is covered by the Creative Commons Attribution-Noncommercial 4.0 International license (CC-BY-NC-4.0) and is included in the book's companion website. . Maya Gans is a freelance data scientist and front-end developer by way of quantitative biology. Toby Hodges is a bioinformatician turned community coordinator who works at the European Molecular Biology Laboratory. Greg Wilson co-founded Software Carpentry, and is now part of the education team at RStudio |
| data science capstone project ideas: Social Work Capstone Projects John Poulin, PhD, MSW, Stephen Kauffman, PhD, Travis Sky Ingersoll, MED, MSW, PhD, 2021-05-29 The only practical guide for helping social work students create high-quality applied capstone research projects from start to finish This “mentor-in-a-book” provides social work students with invaluable information on designing, implementing, and presenting first-rate applied research projects focused on improving social work programs and services. Taking students step-by-step through the entire process, the book helps students plan their projects by providing descriptions of the various research methodologies that can be used to improve social work programs and services. It offers extensive instruction on how to write effectively by providing detailed information on all written components of capstone research projects, as well as the dos and don’ts of writing research reports. Covering data collection methods, program evaluation, organization and community needs assessments, practice-effectiveness studies, and quantitative and qualitative data analysis, this brand-new book also addresses best practices for presenting findings upon completion of the applied research project. Additional features include abundant case examples demonstrating the application of theory to practice and an examination of both qualitative and quantitative research approaches, while also helping students demonstrate social work practice competencies within their capstone projects. Practice activities in each chapter help students apply knowledge to their research projects; and technology exercises help students master important digital research techniques. A capstone project checklist and competency log help students monitor progress, and QR codes provide supplementary support and resources. Additional faculty resources include competency rubrics, detailed group exercises for each chapter, and a sample syllabus for faculty. Purchase of the book includes digital access for use on most mobile devices or computers. Key Features: Delivers step-by-step information on creating high-quality social work capstone projects from conception through presentation Includes a detailed summary of the major applied research approaches to improving social work programs and services Explains how to research literature and write a problem statement on a social service issue Contains extensive information on how to write effective capstone research papers along with abundant examples Helps students to demonstrate social work practice competencies Offers case examples throughout to demonstrate the application of theory to practice Presents practice activities and technology exercises in each chapter Provides a capstone project checklist and competency log Includes QR codes providing additional resources for each chapter |
| data science capstone project ideas: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data science capstone project ideas: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data science capstone project ideas: Impractical Python Projects Lee Vaughan, 2018-11-27 Impractical Python Projects is a collection of fun and educational projects designed to entertain programmers while enhancing their Python skills. It picks up where the complete beginner books leave off, expanding on existing concepts and introducing new tools that you'll use every day. And to keep things interesting, each project includes a zany twist featuring historical incidents, pop culture references, and literary allusions. You'll flex your problem-solving skills and employ Python's many useful libraries to do things like: - Help James Bond crack a high-tech safe with a hill-climbing algorithm - Write haiku poems using Markov Chain Analysis - Use genetic algorithms to breed a race of gigantic rats - Crack the world's most successful military cipher using cryptanalysis - Derive the anagram, I am Lord Voldemort using linguistical sieves - Plan your parents' secure retirement with Monte Carlo simulation - Save the sorceress Zatanna from a stabby death using palingrams - Model the Milky Way and calculate our odds of detecting alien civilizations - Help the world's smartest woman win the Monty Hall problem argument - Reveal Jupiter's Great Red Spot using optical stacking - Save the head of Mary, Queen of Scots with steganography - Foil corporate security with invisible electronic ink Simulate volcanoes, map Mars, and more, all while gaining valuable experience using free modules like Tkinter, matplotlib, Cprofile, Pylint, Pygame, Pillow, and Python-Docx. Whether you're looking to pick up some new Python skills or just need a pick-me-up, you'll find endless educational, geeky fun with Impractical Python Projects. |
| data science capstone project ideas: Deep Learning Applications, Volume 2 M. Arif Wani, Taghi Khoshgoftaar, Vasile Palade, 2020-12-14 This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers. |
| data science capstone project ideas: 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. |
| data science capstone project ideas: The DNP Degree & Capstone Project Mary Bemker, Barb Schreiner, 2016-02-23 Practical guide to understanding the DNP degree and to completing a successful capstone projectClinical, education, and policy exemplars of successful DNP Capstone projects illustrate the necessary components and approach. Provides guidance on publicizing results and conducting projects as a DNP This textbook focuses on enhancing understanding, and characterizing the Doctor of Nursing Practice degree, and its place in the current healthcare environment. The book offers guidelines for planning and conducting all phases of a DNP capstone project. Examples of successful projects from varied areas of nursing practice are included along with practical tips for publicizing capstone project results to the wider medical community. |
| data science capstone project ideas: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| data science capstone project ideas: Intelligence-Based Medicine Anthony C. Chang, 2020-06-27 Intelligence-Based Medicine: Data Science, Artificial Intelligence, and Human Cognition in Clinical Medicine and Healthcare provides a multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies with real life applications in healthcare and medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and the data science domains that is symmetric and balanced. The content consists of basic concepts of artificial intelligence and its real-life applications in a myriad of medical areas as well as medical and surgical subspecialties. It brings section summaries to emphasize key concepts delineated in each section; mini-topics authored by world-renowned experts in the respective key areas for their personal perspective; and a compendium of practical resources, such as glossary, references, best articles, and top companies. The goal of the book is to inspire clinicians to embrace the artificial intelligence methodologies as well as to educate data scientists about the medical ecosystem, in order to create a transformational paradigm for healthcare and medicine by using this emerging new technology. - Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything - Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists - Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future - Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare |
| data science capstone project ideas: Agile by Design Rachel Alt-Simmons, 2015-10-12 Achieve greater success by increasing the agility of analytics lifecycle management Agile by Design offers the insight you need to improve analytic lifecycle management while integrating the right analytics projects into different frameworks within your business. You will explore, in-depth, what analytics projects are and why they are set apart from traditional development initiatives. Beyond merely defining analytics projects, Agile by Design equips you with the information you need to apply agile methodologies in a way that tailors your approach to individual initiatives—and the needs of your projects and team. Lifecycle management is a complex subject area, and with the increasingly important integration of analytics into multiple facets of business models, understanding how to use agile tools while managing a product lifecycle is essential to maintaining a competitive edge in today's professional world. Gain an understanding of the principles, processes, and practices associated with effective analytic lifecycle management Discover techniques that will enable you to successfully initiate, plan, and execute analytic development projects with an eye for the opportunity to engage agile methodologies Understand agile development frameworks Identify which agile methodologies are best for different frameworks—and how to apply them throughout the analytic development lifecycle With analytics becoming increasingly important in today's business world, you need to understand and apply agile methodologies in order to meet rising standards of efficiency and effectiveness. Agile by Design is the perfect reference for project managers, CFOs, IT managers, and marketing managers who want to cultivate a relevant, forward-thinking lifecycle management style. |
| data science capstone project ideas: Mastering Java for Data Science Alexey Grigorev, 2017-04-27 Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book An overview of modern Data Science and Machine Learning libraries available in Java Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn Get a solid understanding of the data processing toolbox available in Java Explore the data science ecosystem available in Java Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images Create your own search engine Get state-of-the-art performance with XGBoost Learn how to build deep neural networks with DeepLearning4j Build applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples. |
| data science capstone project ideas: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science capstone project ideas: Mining the Social Web Matthew Russell, 2011-01-21 Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they’re talking about, or where they’re located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed. Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools. Get a straightforward synopsis of the social web landscape Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn Learn how to employ easy-to-use Python tools to slice and dice the data you collect Explore social connections in microformats with the XHTML Friends Network Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python. --Jeff Hammerbacher, Chief Scientist, Cloudera A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data. --Alex Martelli, Senior Staff Engineer, Google |
| data science capstone project ideas: Quantum Robotics Prateek Tandon, Stanley Lam, Ben Shih, Tanay Mehta, Alex Mitev, Zhiyang Ong, 2017-01-17 Quantum robotics is an emerging engineering and scientific research discipline that explores the application of quantum mechanics, quantum computing, quantum algorithms, and related fields to robotics. This work broadly surveys advances in our scientific understanding and engineering of quantum mechanisms and how these developments are expected to impact the technical capability for robots to sense, plan, learn, and act in a dynamic environment. It also discusses the new technological potential that quantum approaches may unlock for sensing and control, especially for exploring and manipulating quantum-scale environments. Finally, the work surveys the state of the art in current implementations, along with their benefits and limitations, and provides a roadmap for the future. |
| data science capstone project ideas: Creating Breakthrough Products Jonathan Cagan, Craig M. Vogel, 2002 Creating Breakthrough Products describes the new forces driving product development that companies must master if they want to lead and innovate. It is a step-by-step guide to the new ideal in product development. |
| data science capstone project ideas: Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies Murugan, Thangavel, E., Nirmala, 2023-09-21 Disruptive innovations are now propelling Industry 4.0 (I4.0) and presenting new opportunities for value generation in all major industry segments. I4.0 technologies' innovations in cybersecurity and data science provide smart apps and services with accurate real-time monitoring and control. Through enhanced access to real-time information, it also aims to increase overall effectiveness, lower costs, and increase the efficiency of people, processes, and technology. The Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies discusses the technological foundations of cybersecurity and data science within the scope of the I4.0 landscape and details the existing cybersecurity and data science innovations with I4.0 applications, as well as state-of-the-art solutions with regard to both academic research and practical implementations. Covering key topics such as data science, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, computer scientists, scholars, researchers, academicians, practitioners, instructors, and students. |
| data science capstone project ideas: Machine Learning for Hackers Drew Conway, John Myles White, 2012-02-13 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data |
| data science capstone project ideas: 375 Online Business Ideas Prabhu TL, 2024-04-03 In today's digital age, the opportunities for starting and growing a successful online business are abundant. From e-commerce stores and digital services to content creation and online coaching, the internet offers a vast landscape of possibilities for aspiring entrepreneurs to turn their ideas into profitable ventures. 375 Online Business Ideas serves as a comprehensive guide for individuals seeking inspiration, guidance, and practical advice on launching and managing their online businesses. This book presents a curated collection of 375 diverse and innovative online business ideas, spanning various industries, niches, and business models. Whether you're a seasoned entrepreneur looking to expand your online portfolio or a beginner exploring your entrepreneurial journey, this book provides a wealth of ideas to spark your creativity and guide your decision-making process. Each business idea is presented with detailed insights, including market analysis, potential target audience, revenue streams, startup costs, marketing strategies, and scalability opportunities. Readers will gain valuable insights into emerging trends, niche markets, and untapped opportunities within the digital landscape, empowering them to identify viable business ideas that align with their skills, interests, and resources. Furthermore, 375 Online Business Ideas goes beyond mere inspiration by offering practical guidance on how to turn these ideas into reality. The book explores essential aspects of starting and growing an online business, such as market research, business planning, branding, website development, digital marketing, customer acquisition, and monetization strategies. Additionally, readers will find tips, resources, and case studies from successful online entrepreneurs, providing real-world examples and actionable advice to navigate the challenges and capitalize on the opportunities in the online business ecosystem. Whether you aspire to launch an e-commerce store, start a freelance business, create digital products, or build an online community, 375 Online Business Ideas equips you with the knowledge, insights, and inspiration needed to kickstart your entrepreneurial journey and build a thriving online business in today's dynamic and competitive marketplace. With this comprehensive guide at your fingertips, you'll be well-positioned to explore, evaluate, and pursue the online business ideas that resonate with your passions and goals, ultimately paving the way for success and fulfillment in the digital realm. |
| data science capstone project ideas: Frontiers in Software Engineering Education Alfredo Capozucca, Sophie Ebersold, Jean-Michel Bruel, Bertrand Meyer, 2023-11-30 This book constitutes invited papers from the Second International Workshop on Frontiers in Software Engineering Education, FISEE 2023, which took place at the Château de Villebrumier, France, during January 23-25, 2023. The Editorial and the 8 papers included in this volume were considerably enhanced after the conference and during two different peer-review phases. The contributions cover the main topics of the workshop: education in technology and technology for education; new (and fearless) ideas on education; adjustments in teaching during pandemic: experience reports; models for class development; how to design learning objectives and outcomes; labs and practical sessions: how to conduct them; curriculum development; course design; quality course assessment; long-life studies in education; empirical research in SE education; experiences in starting-up new educational systems; blended education. FISEE 2023 is part of a series of scientific events held at the new LASER center in Villebrumier near Montauban and Toulouse, France. |
| data science capstone project ideas: The Discipline of Organizing: Professional Edition Robert J. Glushko, 2014-08-25 Note about this ebook: This ebook exploits many advanced capabilities with images, hypertext, and interactivity and is optimized for EPUB3-compliant book readers, especially Apple's iBooks and browser plugins. These features may not work on all ebook readers. We organize things. We organize information, information about things, and information about information. Organizing is a fundamental issue in many professional fields, but these fields have only limited agreement in how they approach problems of organizing and in what they seek as their solutions. The Discipline of Organizing synthesizes insights from library science, information science, computer science, cognitive science, systems analysis, business, and other disciplines to create an Organizing System for understanding organizing. This framework is robust and forward-looking, enabling effective sharing of insights and design patterns between disciplines that weren’t possible before. The Professional Edition includes new and revised content about the active resources of the Internet of Things, and how the field of Information Architecture can be viewed as a subset of the discipline of organizing. You’ll find: 600 tagged endnotes that connect to one or more of the contributing disciplines Nearly 60 new pictures and illustrations Links to cross-references and external citations Interactive study guides to test on key points The Professional Edition is ideal for practitioners and as a primary or supplemental text for graduate courses on information organization, content and knowledge management, and digital collections. FOR INSTRUCTORS: Supplemental materials (lecture notes, assignments, exams, etc.) are available at http://disciplineoforganizing.org. FOR STUDENTS: Make sure this is the edition you want to buy. There's a newer one and maybe your instructor has adopted that one instead. |
| data science capstone project ideas: Authoring a PhD Patrick Dunleavy, 2017-04-28 This engaging and highly regarded book takes readers through the key stages of their PhD research journey, from the initial ideas through to successful completion and publication. It gives helpful guidance on forming research questions, organising ideas, pulling together a final draft, handling the viva and getting published. Each chapter contains a wealth of practical suggestions and tips for readers to try out and adapt to their own research needs and disciplinary style. This text will be essential reading for PhD students and their supervisors in humanities, arts, social sciences, business, law, health and related disciplines. |
| data science capstone project ideas: The Entry Level Occupational Therapy Doctorate Capstone Elizabeth DeIuliis, Julie Bednarski, 2024-06-01 The purpose of The Entry Level Occupational Therapy Doctorate Capstone: A Framework for The Experience and Project is to provide a step-by-step guide for the development, planning, implementation and dissemination of the entry-level occupational therapy doctoral capstone experience and project. The first entry-level occupational therapy doctorate program was established in 1999, but even now there is a scarcity of occupational therapy resources to guide faculty, prepare students and to socialize mentors to the capstone experience and project. The Entry Level Occupational Therapy Doctorate Capstone by Drs. Elizabeth DeIuliis and Julie Bednarski is the first available resource in the field of occupational therapy devoted to the doctoral capstone. Each chapter provides sample resources and useful documents appropriate for use with occupational therapy doctoral students, faculty, capstone coordinators and site mentors. Included Inside: Templates to develop the MOU, individualized doctoral student objectives, and evaluations Examples of how to structure capstone project proposals Learning activities to guide the literature search and development of a problem statement Strategies of how to approach sustainability and program evaluation of the capstone project Recommendations for structure and formatting of the final written document Additional scholarly products derived from the project Other scholarly deliverables including formats for professional presentations and submissible papers The Entry Level Occupational Therapy Doctorate Capstone: A Framework for The Experience and Project will be the first of its kind to serve as a textbook to provide recommendations that will benefit various stakeholders among the capstone team. |
| data science capstone project ideas: Behavioral Competencies of Digital Professionals Sara Bonesso, Elena Bruni, Fabrizio Gerli, 2019-12-18 Shedding new light on the human side of big data through the lenses of emotional and social intelligence competencies, this book advances the understanding of the requirements of the different professions that deal with big data. It also illustrates the empirical evidence collected through the application of the competency-based methodology to a sample of data scientists and data analysts, the two most in-demand big data jobs in the labor market. The book provides recommendations for the higher education system to offer better designed curricula for entry-level big data professions. It also offers managerial insights in describing how organizations and specifically HR practitioners can benefit from the competency-based approach to overcome the skill shortage that characterizes the demand for big data professional roles and to increase the effectiveness of the selection and recruiting processes. |
| data science capstone project ideas: Java Projects Bpb, 2004-11 The java projects book enables you to develop java applications using an easy and simple approac.The book is designed for the readers,who are familiar with java programming.The book provides numerous listings and figures for an affective understanding of java concepts.The book consists of a CD that includes source code for all the java applications. Table of contents: Chapter 1 Creating a calculator applications Chapter 2 Creating analog clock applications Chapter 3 Creating a 9-box puzzle game Chapter 4 Student information management system Chapter 5 Creating a text editor applications Chapter 6 Creating an online test applications Chapter 7 Creating a shopping cart applications Chapter 8 Share trading application Chapter 9 Online banking applications |
| data science capstone project ideas: How to Become a Data Analyst Annie Nelson, 2023-11-23 Start a brand-new career in data analytics with no-nonsense advice from a self-taught data analytics consultant In How to Become a Data Analyst: My Low-Cost, No Code Roadmap for Breaking into Tech, data analyst and analytics consultant Annie Nelson walks you through how she took the reins and made a dramatic career change to unlock new levels of career fulfilment and enjoyment. In the book, she talks about the adaptability, curiosity, and persistence you’ll need to break free from the 9-5 grind and how data analytics—with its wide variety of skills, roles, and options—is the perfect field for people looking to refresh their careers. Annie offers practical and approachable data portfolio-building advice to help you create one that’s manageable for an entry-level professional but will still catch the eye of employers and clients. You’ll also find: Deep dives into the learning journey required to step into a data analytics role Ways to avoid getting lost in the maze of online courses and certifications you can find online—while still obtaining the skills you need to be competitive Explorations of the highs and lows of Annie’s career-change journey and job search—including what was hard, what was easy, what worked well, and what didn’t Strategies for using ChatGPT to help you in your job search A must-read roadmap to a brand-new and exciting career in data analytics, How to Become a Data Analyst is the hands-on tutorial that shows you exactly how to succeed. |
| data science capstone project ideas: Transformative Science Teaching Daniel Morales-Doyle, 2024-05-23 A call to action championing equity and social justice in K–12 science curriculum |
| data science capstone project ideas: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science capstone project ideas: Transportation and Public Health M. D. Meyer, O. A. Elrahman, 2019-06-15 Transportation and Public Health: An Integrated Approach to Policy, Planning, and Implementation helps current and future transportation professionals integrate public health considerations into their transportation planning, thus supporting sustainability and promoting societal health and well-being. The book defines key issues, describes potential solutions, and provides detailed examples of how solutions have been implemented worldwide. In addition, it demonstrates how to identify gaps in existing policy frameworks. Addressing a critical and emerging urgent need in transportation and public health research, the book creates a coherent, inclusive and interdisciplinary framework for understanding. By integrating principles from transportation planning and engineering, health management, economics, social and organizational psychology, the book deepens understanding of these multiple perspectives and tensions inherent in integrating public health and transportation planning and policy implementation. |
| data science capstone project ideas: Designing and Teaching Undergraduate Capstone Courses Robert C. Hauhart, Jon E. Grahe, 2015-01-12 Enrich your students and the institution with a high-impact practice Designing and Teaching Undergraduate Capstone Courses is a practical, research-backed guide to creating a course that is valuable for both the student and the school. The book covers the design, administration, and teaching of capstone courses throughout the undergraduate curriculum, guiding departments seeking to add a capstone course, and allowing those who have one to compare it to others in the discipline. The ideas presented in the book are supported by regional and national surveys that help the reader understand what's common, what's exceptional, what works, and what doesn't within capstone courses. The authors also provide additional information specific to different departments across the curriculum, including STEM, social sciences, humanities, fine arts, education, and professional programs. Identified as a high-impact practice by the National Survey of Student Engagement (NSSE) and the Association of American Colleges and Universities' LEAP initiative, capstone courses culminate a student's final college years in a project that integrates and applies what they've learned. The project takes the form of a research paper, a performance, a portfolio, or an exhibit, and is intended to showcase the student's very best work as a graduating senior. This book is a guide to creating for your school or department a capstone course that ties together undergraduate learning in a way that enriches the student and adds value to the college experience. Understand what makes capstone courses valuable for graduating students Discover the factors that make a capstone course effective, and compare existing programs, both within academic disciplines and across institutions Learn administrative and pedagogical techniques that increase the course's success Examine discipline-specific considerations for design, administration, and instruction Capstones are generally offered in departmental programs, but are becoming increasingly common in general education as well. Faculty and administrators looking to add a capstone course or revive an existing one need to understand what constitutes an effective program. Designing and Teaching Undergraduate Capstone Courses provides an easily digested summary of existing research, and offers expert guidance on making your capstone course successful. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
