| boston data science bootcamp: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| boston data science bootcamp: Powerful Python Aaron Maxwell, 2024-11-08 Once you've mastered the basics of Python, how do you skill up to the top 1%? How do you focus your learning time on topics that yield the most benefit for production engineering and data teams—without getting distracted by info of little real-world use? This book answers these questions and more. Based on author Aaron Maxwell's software engineering career in Silicon Valley, this unique book focuses on the Python first principles that act to accelerate everything else: the 5% of programming knowledge that makes the remaining 95% fall like dominos. It's also this knowledge that helps you become an exceptional Python programmer, fast. Learn how to think like a Pythonista: explore advanced Pythonic thinking Create lists, dicts, and other data structures using a high-level, readable, and maintainable syntax Explore higher-order function abstractions that form the basis of Python libraries Examine Python's metaprogramming tool for priceless patterns of code reuse Master Python's error model and learn how to leverage it in your own code Learn the more potent and advanced tools of Python's object system Take a deep dive into Python's automated testing and TDD Learn how Python logging helps you troubleshoot and debug more quickly |
| boston data science bootcamp: A New U Ryan Craig, 2018-09-11 Every year, the cost of a four-year degree goes up, and the value goes down. But for many students, there's a better answer. So many things are getting faster and cheaper. Movies stream into your living room, without ticket or concession-stand costs. The world's libraries are at your fingertips instantly, and for free. So why is a college education the only thing that seems immune to change? Colleges and universities operate much as they did 40 years ago, with one major exception: tuition expenses have risen dramatically. What's more, earning a degree takes longer than ever before, with the average time to graduate now over five years. As a result, graduates often struggle with enormous debt burdens. Even worse, they often find that degrees did not prepare them to obtain and succeed at good jobs in growing sectors of the economy. While many learners today would thrive with an efficient and affordable postsecondary education, the slow and pricey road to a bachelor's degree is starkly the opposite. In A New U: Faster + Cheaper Alternatives to College, Ryan Craig documents the early days of a revolution that will transform—or make obsolete—many colleges and universities. Alternative routes to great first jobs that do not involve a bachelor's degree are sprouting up all over the place. Bootcamps, income-share programs, apprenticeships, and staffing models are attractive alternatives to great jobs in numerous growing sectors of the economy: coding, healthcare, sales, digital marketing, finance and accounting, insurance, and data analytics. A New U is the first roadmap to these groundbreaking programs, which will lead to more student choice, better matches with employers, higher return on investment of cost and time, and stronger economic growth. |
| boston data science bootcamp: Innovating Luis Perez-Breva, 2018-08-28 Discover the MIT-developed, “doer’s approach” to innovation with this guide that reveals you don’t need an earth-shattering idea to create a standout product, service, or business—just a hunch that you can scale up to impact. Innovation is the subject of countless books and courses, but there’s very little out there about how you actually innovate. Innovation and entrepreneurship are not one and the same, although aspiring innovators often think of them that way. They are told to get an idea and a team and to build a show-and-tell for potential investors. In Innovating, Luis Perez-Breva describes another approach—a doer’s approach developed over a decade at MIT and internationally in workshops, classes, and companies. He shows that innovating doesn’t require an earth-shattering idea; all it takes is a hunch. Anyone can do it. By prototyping a problem and learning by being wrong, innovating can be scaled up to make an impact. As Perez-Breva demonstrates, “nothing is new” at the outset of what we only later celebrate as innovation. In Innovating, the process—illustrated by unique and dynamic artwork—is shown to be empirical, experimental, nonlinear, and incremental. You give your hunch the structure of a problem. Anything can be a part. Your innovating accrues other people’s knowledge and skills. Perez-Breva describes how to create a kit for innovating, and outlines questions that will help you think in new ways. Finally, he shows how to systematize what you’ve learned: to advocate, communicate, scale up, manage innovating continuously, and document—“you need a notebook to converse with yourself,” he advises. Everyone interested in innovating also needs to read this book. |
| boston data science bootcamp: Deep Learning Illustrated Jon Krohn, Grant Beyleveld, Aglaé Bassens, 2019-08-05 The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come. – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| boston data science bootcamp: The Mathematics of Data Michael W. Mahoney, John C. Duchi, Anna C. Gilbert, 2018-11-15 Nothing provided |
| boston data science bootcamp: Applied Data Science with Python and Jupyter Alex Galea, 2018-10-31 Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts such as SVM, KNN classifiers, and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook Description Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learnGet up and running with the Jupyter ecosystemIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findingsWho this book is for Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start. |
| boston data science bootcamp: Interpretable Machine Learning with Python Serg Masís, 2021-03-26 A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along. |
| boston data science bootcamp: Meeting Regional Stemm Workforce Needs in the Wake of Covid-19 National Academies of Sciences Engineering and Medicine, Policy and Global Affairs, Board on Higher Education and Workforce, 2021-07-23 The COVID-19 pandemic is transforming the global economy and significantly shifting workforce demand, requiring quick, adaptive responses. The pandemic has revealed the vulnerabilities of many organizations and regional economies, and it has accelerated trends that could lead to significant improvements in productivity, performance, and resilience, which will enable organizations and regions to thrive in the next normal. To explore how communities around the United States are addressing workforce issues laid bare by the COVID-19 pandemic and how they are taking advantage of local opportunities to expand their science, technology, engineering, mathematics, and medicine (STEMM) workforces to position them for success going forward, the Board of Higher Education and Workforce of the National Academies of Sciences, Engineering, and Medicine convened a series of workshops to identify immediate and near-term regional STEMM workforce needs in the wake of the COVID-19 pandemic. The workshop planning committee identified five U.S. cities and their associated metropolitan areas - Birmingham, Alabama; Boston, Massachusetts; Richmond, Virginia; Riverside, California; and Wichita, Kansas - to host workshops highlighting promising practices that communities can use to respond urgently and appropriately to their STEMM workforce needs. A sixth workshop discussed how the lessons learned during the five region-focused workshops could be applied in other communities to meet STEMM workforce needs. This proceedings of a virtual workshop series summarizes the presentations and discussions from the six public workshops that made up the virtual workshop series and highlights the key points raised during the presentations, moderated panel discussions and deliberations, and open discussions among the workshop participants. |
| boston data science bootcamp: Foundations of Machine Learning, second edition Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, 2018-12-25 A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. |
| boston data science bootcamp: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| boston data science bootcamp: Hands-On Data Analysis with Pandas Stefanie Molin, 2019-07-26 Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial. |
| boston data science bootcamp: The Ethical Algorithm Michael Kearns, Aaron Roth, 2020 Algorithms have made our lives more efficient and entertaining--but not without a significant cost. Can we design a better future, one in which societial gains brought about by technology are balanced with the rights of citizens? The Ethical Algorithm offers a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. |
| boston data science bootcamp: Data Mining and Predictive Analytics Daniel T. Larose, 2015-02-19 Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives. |
| boston data science bootcamp: Democratizing Innovation Eric Von Hippel, 2006-02-17 The process of user-centered innovation: how it can benefit both users and manufacturers and how its emergence will bring changes in business models and in public policy. Innovation is rapidly becoming democratized. Users, aided by improvements in computer and communications technology, increasingly can develop their own new products and services. These innovating users—both individuals and firms—often freely share their innovations with others, creating user-innovation communities and a rich intellectual commons. In Democratizing Innovation, Eric von Hippel looks closely at this emerging system of user-centered innovation. He explains why and when users find it profitable to develop new products and services for themselves, and why it often pays users to reveal their innovations freely for the use of all.The trend toward democratized innovation can be seen in software and information products—most notably in the free and open-source software movement—but also in physical products. Von Hippel's many examples of user innovation in action range from surgical equipment to surfboards to software security features. He shows that product and service development is concentrated among lead users, who are ahead on marketplace trends and whose innovations are often commercially attractive. Von Hippel argues that manufacturers should redesign their innovation processes and that they should systematically seek out innovations developed by users. He points to businesses—the custom semiconductor industry is one example—that have learned to assist user-innovators by providing them with toolkits for developing new products. User innovation has a positive impact on social welfare, and von Hippel proposes that government policies, including R&D subsidies and tax credits, should be realigned to eliminate biases against it. The goal of a democratized user-centered innovation system, says von Hippel, is well worth striving for. An electronic version of this book is available under a Creative Commons license. |
| boston data science bootcamp: Disciplined Entrepreneurship Bill Aulet, 2013-08-12 24 Steps to Success! Disciplined Entrepreneurship will change the way you think about starting a company. Many believe that entrepreneurship cannot be taught, but great entrepreneurs aren’t born with something special – they simply make great products. This book will show you how to create a successful startup through developing an innovative product. It breaks down the necessary processes into an integrated, comprehensive, and proven 24-step framework that any industrious person can learn and apply. You will learn: Why the “F” word – focus – is crucial to a startup’s success Common obstacles that entrepreneurs face – and how to overcome them How to use innovation to stand out in the crowd – it’s not just about technology Whether you’re a first-time or repeat entrepreneur, Disciplined Entrepreneurship gives you the tools you need to improve your odds of making a product people want. Author Bill Aulet is the managing director of the Martin Trust Center for MIT Entrepreneurship as well as a senior lecturer at the MIT Sloan School of Management. For more please visit http://disciplinedentrepreneurship.com/ |
| boston data science bootcamp: Learning Deep Learning Magnus Ekman, 2021-07-19 NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals. -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us. -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| boston data science bootcamp: Feature Engineering for Machine Learning Alice Zheng, Amanda Casari, 2018-03-23 Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques |
| boston data science bootcamp: Building Machine Learning Pipelines Hannes Hapke, Catherine Nelson, 2020-07-13 Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques |
| boston data science bootcamp: 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. |
| boston data science bootcamp: Artificial Intelligence and the Legal Profession Michael Legg, Felicity Bell, 2020-11-26 How are new technologies changing the practice of law? With examples and explanations drawn from the UK, US, Canada, Australia and other common law countries, as well as from China and Europe, this book considers the opportunities and implications for lawyers as artificial intelligence systems become commonplace in legal service delivery. It examines what lawyers do in the practice of law and where AI will impact this work. It also explains the important continuing role of the lawyer in an AI world. This book is divided into three parts: Part A provides an accessible explanation of AI, including diagrams, and contrasts this with the role and work of lawyers. Part B focuses on six different aspects of legal work (litigation, transactional, dispute resolution, regulation and compliance, criminal law and legal advice and strategy) where AI is making a considerable impact and looks at how this is occurring. Part C discusses how lawyers and law firms can best utilise the promise of AI, while also acknowledging its limitations. It also discusses ethical and regulatory issues, including the lawyer's role in upholding the rule of law. |
| boston data science bootcamp: Effective Data Science Infrastructure Ville Tuulos, 2022-08-30 Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. About the technology Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. About the book Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. What's inside Handle compute and orchestration in the cloud Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem Architect complex applications that require large datasets and models, and a team of data scientists About the reader For infrastructure engineers and engineering-minded data scientists who are familiar with Python. About the author At Netflix, Ville Tuulos designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. Table of Contents 1 Introducing data science infrastructure 2 The toolchain of data science 3 Introducing Metaflow 4 Scaling with the compute layer 5 Practicing scalability and performance 6 Going to production 7 Processing data 8 Using and operating models 9 Machine learning with the full stack |
| boston data science bootcamp: Barbarians at the Gate Bryan Burrough, John Helyar, 2009-10-13 #1 New York Times bestseller and arguably the best business narrative ever written, Barbarians at the Gate is the classic account of the fall of RJR Nabisco at the hands of a buyout from investment firm KKR. A book that stormed both the bestseller list and the public imagination, a book that created a genre of its own, and a book that gets at the heart of Wall Street and the '80s culture it helped define, Barbarians at the Gate is a modern classic—a masterpiece of investigatory journalism and a rollicking book of corporate derring-do and financial swordsmanship. The fight to control RJR Nabisco during October and November of 1988 was more than just the largest takeover in Wall Street history. Marked by brazen displays of ego not seen in American business for decades, it became the high point of a new gilded age and its repercussions are still being felt. The tale remains the ultimate story of greed and glory—a story and a cast of characters that determined the course of global business and redefined how deals would be done and fortunes made in the decades to come. Barbarians at the Gate is the gripping account of these two frenzied months, of deal makers and publicity flaks, of an old-line industrial powerhouse (home of such familiar products a Oreos and Camels) that became the victim of the ruthless and rapacious style of finance in the 1980s. As reporters for The Wall Street Journal, Burrough and Helyar had extensive access to all the characters in this drama. They take the reader behind the scenes at strategy meetings and society dinners, into boardrooms and bedrooms, providing an unprecedentedly detailed look at how financial operations at the highest levels are conducted but also a richly textured social history of wealth at the twilight of the Reagan era. At the center of the huge power struggle is RJR Nabisco's president, the high-living Ross Johnson. It's his secret plan to buy out the company that sets the frenzy in motion, attracting the country's leading takeover players: Henry Kravis, the legendary leveraged-buyout king of investment firm KKR, whose entry into the fray sets off an acquisitive commotion; Peter Cohen, CEO of Shearson Lehman Hutton and Johnson's partner, who needs a victory to propel his company to an unchallenged leadership in the lucrative mergers and acquisitions field; the fiercely independent Ted Forstmann, motivated as much by honor as by his rage at the corruption he sees taking over the business he cherishes; Jim Maher and his ragtag team, struggling to regain credibility for the decimated ranks at First Boston; and an army of desperate bankers, lawyers, and accountants, all drawn inexorably to the greatest prize of their careers—and one of the greatest prizes in the history of American business. Written with the bravado of a novel and researched with the diligence of a sweeping cultural history, Barbarians at the Gate is present at the front line of every battle of the campaign. Here is the unforgettable story of that takeover in all its brutality. In a new afterword specially commissioned for the story's 20th anniversary, Burrough and Helyar return to visit the heroes and villains of this epic story, tracing the fallout of the deal, charting the subsequent success and failure of those involved, and addressing the incredible impact this story—and the book itself—made on the world. |
| boston data science bootcamp: Foundations for Analytics with Python Clinton W. Brownley, 2016-08-16 If you’re like many of Excel’s 750 million users, you want to do more with your data—like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats—by using Python. After author Clinton Brownley takes you through Python basics, you’ll be able to write simple scripts for processing data in spreadsheets as well as databases. You’ll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary. Create and run your own Python scripts by learning basic syntax Use Python’s csv module to read and parse CSV files Read multiple Excel worksheets and workbooks with the xlrd module Perform database operations in MySQL or with the mysqlclient module Create Python applications to find specific records, group data, and parse text files Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn Produce summary statistics, and estimate regression and classification models Schedule your scripts to run automatically in both Windows and Mac environments |
| boston data science bootcamp: An Introduction to Data Francesco Corea, 2018-11-27 This book reflects the author’s years of hands-on experience as an academic and practitioner. It is primarily intended for executives, managers and practitioners who want to redefine the way they think about artificial intelligence (AI) and other exponential technologies. Accordingly the book, which is structured as a collection of largely self-contained articles, includes both general strategic reflections and detailed sector-specific information. More concretely, it shares insights into what it means to work with AI and how to do it more efficiently; what it means to hire a data scientist and what new roles there are in the field; how to use AI in specific industries such as finance or insurance; how AI interacts with other technologies such as blockchain; and, in closing, a review of the use of AI in venture capital, as well as a snapshot of acceleration programs for AI companies. |
| boston data science bootcamp: The Sales Acceleration Formula Mark Roberge, 2015-02-24 Use data, technology, and inbound selling to build a remarkable team and accelerate sales The Sales Acceleration Formula provides a scalable, predictable approach to growing revenue and building a winning sales team. Everyone wants to build the next $100 million business and author Mark Roberge has actually done it using a unique methodology that he shares with his readers. As an MIT alum with an engineering background, Roberge challenged the conventional methods of scaling sales utilizing the metrics-driven, process-oriented lens through which he was trained to see the world. In this book, he reveals his formulas for success. Readers will learn how to apply data, technology, and inbound selling to every aspect of accelerating sales, including hiring, training, managing, and generating demand. As SVP of Worldwide Sales and Services for software company HubSpot, Mark led hundreds of his employees to the acquisition and retention of the company's first 10,000 customers across more than 60 countries. This book outlines his approach and provides an action plan for others to replicate his success, including the following key elements: Hire the same successful salesperson every time — The Sales Hiring Formula Train every salesperson in the same manner — The Sales Training Formula Hold salespeople accountable to the same sales process — The Sales Management Formula Provide salespeople with the same quality and quantity of leads every month — The Demand Generation Formula Leverage technology to enable better buying for customers and faster selling for salespeople Business owners, sales executives, and investors are all looking to turn their brilliant ideas into the next $100 million revenue business. Often, the biggest challenge they face is the task of scaling sales. They crave a blueprint for success, but fail to find it because sales has traditionally been referred to as an art form, rather than a science. You can't major in sales in college. Many people question whether sales can even be taught. Executives and entrepreneurs are often left feeling helpless and hopeless. The Sales Acceleration Formula completely alters this paradigm. In today's digital world, in which every action is logged and masses of data sit at our fingertips, building a sales team no longer needs to be an art form. There is a process. Sales can be predictable. A formula does exist. |
| boston data science bootcamp: Smartups Rob Ryan, 2002 Ryan focuses on methods he has developed over the years for building a sustainable business that makes money. He shows how to turn an idea into real product. |
| boston data science bootcamp: Brave, Not Perfect Reshma Saujani, 2019-02-05 INTERNATIONAL BESTSELLER • Inspired by her popular TED Talk, the founder and CEO of Girls Who Code urges women to embrace imperfection and live a bolder, more authentic life. “A timely message for women of all ages: Perfection isn’t just impossible but, worse, insidious.”—Angela Duckworth, bestselling author of Grit Imagine if you lived without the fear of not being good enough. If you didn’t care how your life looked on Instagram. If you could let go of the guilt and stop beating yourself up for making human mistakes. Imagine if, in every decision you faced, you took the bolder path? As women, too many of us feel crushed under the weight of our own expectations. We run ourselves ragged trying to please everyone, pass up opportunities that scare us, and avoid rejection at all costs. There’s a reason we act this way, Saujani says. As girls, we were taught to play it safe. Well-meaning parents and teachers praised us for being quiet and polite, urged us to be careful so we didn’t get hurt, and steered us to activities at which we could shine. As a result, we grew up to be women who are afraid to fail. It’s time to stop letting our fears drown out our dreams and narrow our world, along with our chance at happiness. By choosing bravery over perfection, we can find the power to claim our voice, to leave behind what makes us unhappy, and to go for the things we genuinely, passionately want. Perfection may set us on a path that feels safe, but bravery leads us to the one we’re authentically meant to follow. In Brave, Not Perfect,Saujani shares powerful insights and practices to help us let go of our need for perfection and make bravery a lifelong habit. By being brave, not perfect, we can all become the authors of our best and most joyful life. |
| boston data science bootcamp: Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020-04-23 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. |
| boston data science bootcamp: Optimization for Machine Learning Suvrit Sra, Sebastian Nowozin, Stephen J. Wright, 2012 An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community. |
| boston data science bootcamp: Blindsight Matt Johnson, Prince Ghuman, 2020-05-19 Ever notice that all watch ads show 10:10 as the time? Or that all fast-food restaurants use red or yellow in their logos? Or that certain stores are always having a sale? You may not be aware of these details, yet they've been influencing you all along. Every time you purchase, swipe, or click, marketers are able to more accurately predict your behavior. These days, brands know more about you than you know about yourself. Blindsight is here to change that. With eye-opening science, engaging stories, and fascinating real-world examples, neuroscientist Matt Johnson and marketer Prince Ghuman dive deep into the surprising relationship between brains and brands. In Blindsight, they showcase how marketing taps every aspect of our mental lives, covering the neuroscience of pain and pleasure, emotion and logic, fear and safety, attention and addiction, and much more. We like to think of ourselves as independent actors in control of our decisions, but the truth is far more complicated. Blindsight will give you the ability to see the unseeable when it comes to marketing, so that you can consume on your own terms. On the surface, you will learn how the brain works and how brands design for it. But peel back a layer, and you'll find a sharper image of your psychology, reflected in your consumer behavior. This book will change the way you view not just branding, but yourself, too. |
| boston data science bootcamp: MTEL , 2011 If you are preparing for a teaching career in Massachusetts, passing the Massachusetts Tests for Educator Licensure (MTEL) Communication and Literacy Skills (01) test is an essential part of the certification process. This easy-to-use e-book helps you develop and practice the skills needed to achieve success on the MTEL. It provides a fully updated, comprehensive review of all areas tested on the official Communication and Literacy Skills (01) assessment, helpful information on the Massachusetts teacher certification and licensing process, and the LearningExpress Test Preparation System, with proven techniques for overcoming test anxiety, planning study time, and improving your results. |
| boston data science bootcamp: Proceedings of the Second Seattle Symposium in Biostatistics Danyu Lin, Patrick J. Heagerty, 2012-12-06 This volume contains a selection of papers presented at the Second Seattle Symposium in Biostatistics: Analysis of Correlated Data. The symposium was held in 2000 to celebrate the 30th anniversary of the University of Washington School of Public Health and Community Medicine. It featured keynote lectures by Norman Breslow, David Cox and Ross Prentice and 16 invited presentations by other prominent researchers. The papers contained in this volume encompass recent methodological advances in several important areas, such as longitudinal data, multivariate failure time data and genetic data, as well as innovative applications of the existing theory and methods. This volume is a valuable reference for researchers and practitioners in the field of correlated data analysis. |
| boston data science bootcamp: Python Natural Language Processing Cookbook Zhenya Antić, 2021-03-19 Get to grips with solving real-world NLP problems, such as dependency parsing, information extraction, topic modeling, and text data visualization Key Features Analyze varying complexities of text using popular Python packages such as NLTK, spaCy, sklearn, and gensim Implement common and not-so-common linguistic processing tasks using Python libraries Overcome the common challenges faced while implementing NLP pipelines Book DescriptionPython is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization. Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You’ll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you’ll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data. By the end of this NLP book, you’ll have developed the skills to use a powerful set of tools for text processing.What you will learn Become well-versed with basic and advanced NLP techniques in Python Represent grammatical information in text using spaCy, and semantic information using bag-of-words, TF-IDF, and word embeddings Perform text classification using different methods, including SVMs and LSTMs Explore different techniques for topic modeling such as K-means, LDA, NMF, and BERT Work with visualization techniques such as NER and word clouds for different NLP tools Build a basic chatbot using NLTK and Rasa Extract information from text using regular expression techniques and statistical and deep learning tools Who this book is for This book is for data scientists and professionals who want to learn how to work with text. Intermediate knowledge of Python will help you to make the most out of this book. If you are an NLP practitioner, this book will serve as a code reference when working on your projects. |
| boston data science bootcamp: Engineering Software as a Service Armando Fox, David A. Patterson, 2016 (NOTE: this Beta Edition may contain errors. See http://saasbook.info for details.) A one-semester college course in software engineering focusing on cloud computing, software as a service (SaaS), and Agile development using Extreme Programming (XP). This book is neither a step-by-step tutorial nor a reference book. Instead, our goal is to bring a diverse set of software engineering topics together into a single narrative, help readers understand the most important ideas through concrete examples and a learn-by-doing approach, and teach readers enough about each topic to get them started in the field. Courseware for doing the work in the book is available as a virtual machine image that can be downloaded or deployed in the cloud. A free MOOC (massively open online course) at saas-class.org follows the book's content and adds programming assignments and quizzes. See http://saasbook.info for details.(NOTE: this Beta Edition may contain errors. See http://saasbook.info for details.) A one-semester college course in software engineering focusing on cloud computing, software as a service (SaaS), and Agile development using Extreme Programming (XP). This book is neither a step-by-step tutorial nor a reference book. Instead, our goal is to bring a diverse set of software engineering topics together into a single narrative, help readers understand the most important ideas through concrete examples and a learn-by-doing approach, and teach readers enough about each topic to get them started in the field. Courseware for doing the work in the book is available as a virtual machine image that can be downloaded or deployed in the cloud. A free MOOC (massively open online course) at saas-class.org follows the book's content and adds programming assignments and quizzes. See http://saasbook.info for details. |
| boston data science bootcamp: Learning SQL Alan Beaulieu, 2009-04-11 Updated for the latest database management systems -- including MySQL 6.0, Oracle 11g, and Microsoft's SQL Server 2008 -- this introductory guide will get you up and running with SQL quickly. Whether you need to write database applications, perform administrative tasks, or generate reports, Learning SQL, Second Edition, will help you easily master all the SQL fundamentals. Each chapter presents a self-contained lesson on a key SQL concept or technique, with numerous illustrations and annotated examples. Exercises at the end of each chapter let you practice the skills you learn. With this book, you will: Move quickly through SQL basics and learn several advanced features Use SQL data statements to generate, manipulate, and retrieve data Create database objects, such as tables, indexes, and constraints, using SQL schema statements Learn how data sets interact with queries, and understand the importance of subqueries Convert and manipulate data with SQL's built-in functions, and use conditional logic in data statements Knowledge of SQL is a must for interacting with data. With Learning SQL, you'll quickly learn how to put the power and flexibility of this language to work. |
| boston data science bootcamp: Applied Econometrics with R Christian Kleiber, Achim Zeileis, 2008-12-10 R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research. |
| boston data science bootcamp: Artificial Intelligence Stuart Russell, Peter Norvig, 2016-09-10 Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. |
| boston data science bootcamp: The Analytics Edge Dimitris Bertsimas, Allison K. O'Hair, William R. Pulleyblank, 2016 Provides a unified, insightful, modern, and entertaining treatment of analytics. The book covers the science of using data to build models, improve decisions, and ultimately add value to institutions and individuals--Back cover. |
| boston data science bootcamp: Java Programming Ralph Bravaco, Shai Simonson, 2009-02-01 Java Programming, From The Ground Up, with its flexible organization, teaches Java in a way that is refreshing, fun, interesting and still has all the appropriate programming pieces for students to learn. The motivation behind this writing is to bring a logical, readable, entertaining approach to keep your students involved. Each chapter has a Bigger Picture section at the end of the chapter to provide a variety of interesting related topics in computer science. The writing style is conversational and not overly technical so it addresses programming concepts appropriately. Because of the flexibile organization of the text, it can be used for a one or two semester introductory Java programming class, as well as using Java as a second language. The text contains a large variety of carefully designed exercises that are more effective than the competition. |
Boston.com: Local breaking news, sports, weather, and things to do
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Boston - Wikipedia
Boston [a] is the capital and most populous city in the Commonwealth of Massachusetts in the United States. The city serves as the cultural and financial center of New England, a region of …
30 Top-Rated Things to Do in Boston | U.S. News Travel
Jun 6, 2025 · As Massachusetts' capital and the birthplace of the American Revolution, there's no shortage of historical sites for travelers to explore within Boston's city limits (and beyond). …
Visiting Boston | Boston.gov
May 10, 2024 · There are a variety of free walks and trails throughout the City of Boston. The City has a wealth of museums, with everything from the Museum of Fine Arts to the Old State …
Boston | History, Population, Map, Climate, & Facts | Britannica
6 days ago · Boston, city, capital of the commonwealth of Massachusetts, and seat of Suffolk county, in the northeastern United States. It lies on Massachusetts Bay, an arm of the Atlantic …
Meet Boston | Your Official Guide to Boston
Explore the city for history buffs, sports fanatics, music lovers, foodies, cultural travelers, and, truthfully, anyone. Whether you're visiting by air, by land, or by sea, find everything you need …
Boston Bucket List: 30 Best Things To Do in Boston - Earth …
Aug 22, 2017 · Here's a list of the best things to do in Boston, including the Freedom Trail, Fenway Park, the North End, whale watching, and more.
THE 15 BEST Things to Do in Boston (2025) - Tripadvisor
Things to Do in Boston, Massachusetts: See Tripadvisor's 743,229 traveler reviews and photos of Boston tourist attractions. Find what to do today, this weekend, or in June. We have reviews of …
Boston - Explore Culture & Historical Sites in Boston ... - Visit The …
Discover the Freedom Trail’s landmarks, trendy restaurants and new high-tech campuses of the USA’s most prestigious universities. Check out top things to do in Boston, Massachusetts.
Boston, Massachusetts - WorldAtlas
Apr 9, 2022 · Boston is a city in the northeastern United States that serves as the capital of the Commonwealth of Massachusetts and the seat of Suffolk County. It has an area of 46 square …
Boston.com: Local breaking news, sports, weather, and things to do
What Boston cares about right now: Get breaking updates on news, sports, and weather. Local alerts, things to do, and more on Boston.com.
Boston - Wikipedia
Boston [a] is the capital and most populous city in the Commonwealth of Massachusetts in the United States. The city serves as the cultural and financial center of New England, a region of …
30 Top-Rated Things to Do in Boston | U.S. News Travel
Jun 6, 2025 · As Massachusetts' capital and the birthplace of the American Revolution, there's no shortage of historical sites for travelers to explore within Boston's city limits (and beyond). …
Visiting Boston | Boston.gov
May 10, 2024 · There are a variety of free walks and trails throughout the City of Boston. The City has a wealth of museums, with everything from the Museum of Fine Arts to the Old State …
Boston | History, Population, Map, Climate, & Facts | Britannica
6 days ago · Boston, city, capital of the commonwealth of Massachusetts, and seat of Suffolk county, in the northeastern United States. It lies on Massachusetts Bay, an arm of the Atlantic …
Meet Boston | Your Official Guide to Boston
Explore the city for history buffs, sports fanatics, music lovers, foodies, cultural travelers, and, truthfully, anyone. Whether you're visiting by air, by land, or by sea, find everything you need …
Boston Bucket List: 30 Best Things To Do in Boston - Earth …
Aug 22, 2017 · Here's a list of the best things to do in Boston, including the Freedom Trail, Fenway Park, the North End, whale watching, and more.
THE 15 BEST Things to Do in Boston (2025) - Tripadvisor
Things to Do in Boston, Massachusetts: See Tripadvisor's 743,229 traveler reviews and photos of Boston tourist attractions. Find what to do today, this weekend, or in June. We have reviews of …
Boston - Explore Culture & Historical Sites in Boston ... - Visit The …
Discover the Freedom Trail’s landmarks, trendy restaurants and new high-tech campuses of the USA’s most prestigious universities. Check out top things to do in Boston, Massachusetts.
Boston, Massachusetts - WorldAtlas
Apr 9, 2022 · Boston is a city in the northeastern United States that serves as the capital of the Commonwealth of Massachusetts and the seat of Suffolk County. It has an area of 46 square …
