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| data science free lance: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data science free lance: The Freelance Manifesto Joey Korenman, 2017-05-31 Designing beautiful boards and making smooth animation come naturally to us Motion Designers. It's what we're good at. However, designing the career we want, with the freedom, flexibility, and pay we crave, that's more difficult. All of the above is within your grasp if you're willing to take the plunge into freelancing. School of Motion founder Joey Korenman worked in every kind of Motion Design role before discovering that freelancing offered him not only more autonomy but also higher pay, less stress, and more creativity. Since then, he's taught hundreds of School of Motion students his playbook for becoming a six-figure freelancer. Now he shares his experience and advice on breaking out of the nine-to-five mold in this comprehensive and tactical handbook. The Freelance Manifesto offers a field guide for Motion Design professionals looking to make the leap to freelance in two clear and concise parts. The first examines the goals, benefits, myths, and realities of the freelance lifestyle, while the second provides future freelancers with a five-step guide to launching and maintaining a solo business, including making contact, selling yourself, closing the deal, being indispensable, and becoming a lucrative enterprise. If you're feeling stifled by long hours, low-paying gigs, and an unfulfilling career, make the choice to redesign yourself as a freelancer-and, with the help of this book and some hard work, reclaim your time, independence, and inspiration for yourself. |
| data science free lance: Python for Data Science For Dummies John Paul Mueller, Luca Massaron, 2015-06-23 Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover. |
| data science free lance: JavaScript for Data Science Maya Gans, Toby Hodges, Greg Wilson, 2020 JavaScript is the language of the web. Originally developed for making browser-based interfaces more dynamic, it is now used for large-scale software projects of all kinds, including scientific visualization tools and data services. However, most researchers and data scientists have little or no experience with it. This book is designed to fill that void. It introduces readers to JavaScript's power and idiosyncrasies, and guides them through the key features of the modern version 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-- |
| data science free lance: Data Science Programming All-in-One For Dummies John Paul Mueller, Luca Massaron, 2020-01-09 Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s! |
| data science free lance: The Money Book for Freelancers, Part-Timers, and the Self-Employed Joseph D'Agnese, Denise Kiernan, 2010-03-02 This is a book for people like us, and we all know who we are. We make our own hours, keep our own profits, chart our own way. We have things like gigs, contracts, clients, and assignments. All of us are working toward our dreams: doing our own work, on our own time, on our own terms. We have no real boss, no corporate nameplate, no cubicle of our very own. Unfortunately, we also have no 401(k)s and no one matching them, no benefits package, and no one collecting our taxes until April 15th. It’s time to take stock of where you are and where you want to be. Ask yourself: Who is planning for your retirement? Who covers your expenses when clients flake out and checks are late? Who is setting money aside for your taxes? Who is responsible for your health insurance? Take a good look in the mirror: You are. The Money Book for Freelancers, Part-Timers, and the Self-Employed describes a completely new, comprehensive system for earning, spending, saving, and surviving as an independent worker. From interviews with financial experts to anecdotes from real-life freelancers, plus handy charts and graphs to help you visualize key concepts, you’ll learn about topics including: • Managing Cash Flow When the Cash Isn’t Flowing Your Way • Getting Real About What You’re Really Earning • Tools for Getting Out of Debt and Into Financial Security • Saving Consistently When You Earn Irregularly • What To Do When a Client’s Check Doesn’t Come In • Health Savings Accounts and How To Use Them • Planning for Retirement, Taxes and Dreams—All On Your Own |
| data science free lance: Grokking Deep Reinforcement Learning Miguel Morales, 2020-11-10 Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback. Summary We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology We learn by interacting with our environment, and the rewards or punishments we experience guide our future behavior. Deep reinforcement learning brings that same natural process to artificial intelligence, analyzing results to uncover the most efficient ways forward. DRL agents can improve marketing campaigns, predict stock performance, and beat grand masters in Go and chess. About the book Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback. What's inside An introduction to reinforcement learning DRL agents with human-like behaviors Applying DRL to complex situations About the reader For developers with basic deep learning experience. About the author Miguel Morales works on reinforcement learning at Lockheed Martin and is an instructor for the Georgia Institute of Technology’s Reinforcement Learning and Decision Making course. Table of Contents 1 Introduction to deep reinforcement learning 2 Mathematical foundations of reinforcement learning 3 Balancing immediate and long-term goals 4 Balancing the gathering and use of information 5 Evaluating agents’ behaviors 6 Improving agents’ behaviors 7 Achieving goals more effectively and efficiently 8 Introduction to value-based deep reinforcement learning 9 More stable value-based methods 10 Sample-efficient value-based methods 11 Policy-gradient and actor-critic methods 12 Advanced actor-critic methods 13 Toward artificial general intelligence |
| data science free lance: SQL Pocket Guide Alice Zhao, 2021-08-26 If you use SQL in your day-to-day work as a data analyst, data scientist, or data engineer, this popular pocket guide is your ideal on-the-job reference. You'll find many examples that address the language's complexities, along with key aspects of SQL used in Microsoft SQL Server, MySQL, Oracle Database, PostgreSQL, and SQLite. In this updated edition, author Alice Zhao describes how these database management systems implement SQL syntax for both querying and making changes to a database. You'll find details on data types and conversions, regular expression syntax, window functions, pivoting and unpivoting, and more. Quickly look up how to perform specific tasks using SQL Apply the book's syntax examples to your own queries Update SQL queries to work in five different database management systems NEW: Connect Python and R to a relational database NEW: Look up frequently asked SQL questions in the How Do I? chapter |
| data science free lance: Python and R for the Modern Data Scientist Rick J. Scavetta, Boyan Angelov, 2021-06-22 Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together |
| data science free lance: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-06 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 free lance: 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. |
| data science free lance: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
| data science free lance: Data Sketches Nadieh Bremer, Shirley Wu, 2021-02-09 In Data Sketches, Nadieh Bremer and Shirley Wu document the deeply creative process behind 24 unique data visualization projects, and they combine this with powerful technical insights which reveal the mindset behind coding creatively. Exploring 12 different themes – from the Olympics to Presidents & Royals and from Movies to Myths & Legends – each pair of visualizations explores different technologies and forms, blurring the boundary between visualization as an exploratory tool and an artform in its own right. This beautiful book provides an intimate, behind-the-scenes account of all 24 projects and shares the authors’ personal notes and drafts every step of the way. The book features: Detailed information on data gathering, sketching, and coding data visualizations for the web, with screenshots of works-in-progress and reproductions from the authors’ notebooks Never-before-published technical write-ups, with beginner-friendly explanations of core data visualization concepts Practical lessons based on the data and design challenges overcome during each project Full-color pages, showcasing all 24 final data visualizations This book is perfect for anyone interested or working in data visualization and information design, and especially those who want to take their work to the next level and are inspired by unique and compelling data-driven storytelling. |
| data science free lance: Financial Data Analytics Sinem Derindere Köseoğlu, 2022-04-25 This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. |
| data science free lance: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| data science free lance: Python for Data Science Yuli Vasiliev, 2022-08-02 A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples. Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You’ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. You will discover Python’s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices. |
| data science free lance: Data Pipelines Pocket Reference James Densmore, 2021-02-10 Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting |
| data science free lance: Engineering Production-grade Shiny Apps Colin Fay, Vincent Guyader, Sebastien Rochette, Girard Cervan, 2021 Presented in full color, Engineering Production-Grade Shiny Apps helps people build production-grade shiny applications, by providing advice, tools, and a methodology to work on web applications with R. This book starts with an overview of the challenges which arise from any big web application project: organizing work, thinking about the user interface, challenges of teamwork & production environment. Then, it moves to a step by step methodology that goes from the idea to the end application. Each part of this process will cover in detail a series of tools and methods to use while building production-ready shiny applications. Finally, the book will end with a series of approaches and advice about optimizations for production-- |
| data science free lance: Cracking the Data Science Interview Maverick Lin, 2019-12-17 Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. Topics include: - Necessary Prerequisites (statistics, probability, linear algebra, and computer science) - 18 Big Ideas in Data Science (such as Occam's Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality) - Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization) - Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more) - Reinforcement Learning (Q-Learning and Deep Q-Learning) - Non-Machine Learning Tools (graph theory, ARIMA, linear programming) - Case Studies (a look at what data science means at companies like Amazon and Uber) Maverick holds a bachelor's degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics. |
| data science free lance: The Human Cloud Matthew Mottola, Matthew Douglas Coatney, 2021-01-26 Empower yourself with the knowledge to keep up with the rapidly changing technical world of work, as two workforce productivity and technology experts lay out a clear picture of the?coming?revolution?in how work is done and how jobs are shaped. If you listen to the news, robots are coming for your job. Full-time employment will soon be a thing of the past as organizations opt more to hire employees on a contract basis.?With technological advances across email, video, project management, and instant messaging platforms, being tied to a desk working full time for one company is becoming obsolete. So, where does that leave you? The Human Cloud may be the most important book you read to prepare for how work is done in the future. In these pages, human cloud technologist Matthew Mottola and AI expert Matthew Coatney help you not only clearly understand the transition you see happening around you, but they will also help you take advantage of it. In The Human Cloud, Mottola and Coatney inform you about topics including: How employees and employers will be able to take advantage of the new automated and freelance-based workplace. How they will be able to take advantage of the new technology disruptions the machine cloud will create. Why the changes employees and employers are seeing aren’t the projection of doom that many are predicting. How to navigate the coming job marketplace. By replacing fear with knowledge, you will better understand how this shift in employment is a good thing, be equipped to embrace the positive?advantages new technology brings, and further secure how your own job is shaped so you are never left behind. |
| data science free lance: The Naked Beggar Zeeshan-ul-hassan Usmani, 2016-08-12 Ever since man started to create stories, there has existed a seemingly invisible yet eternal bond between fictional tales woven out of words and the actual truth. It is undeniable that the truth always reigns with magnificence and glory within any culture and its people. It is this very truth, seemingly shrouded in lies, that a writer attempts to capture and jail forever within intricate cages of letters and words. Doing this is an attempt, on his part, to relieve the heavy hearts of society from the burden of these lies. Although the need for guile exists as the requirement of the times, it is nonetheless preferred to be kept anonymous and unidentifiable. Consequently, the writer too has to alter the identity of these lies. Hence, borrowing unknown shrouds and cloaking these fibs with torn, soiled, and beleaguered words, he is forced to present them as being true. The Naked Beggar and Other Stories is also a similar attempt of a writer to go within the heart of truth and weave out tales that, though born of honesty, cannot be presented as anything else but falsehood. That is the need of the time, and it is the only way these truths will ever be accepted. These stories are strewn all about us but are visible only to the discerning eye and a sensitive heart. Mans intellect can only attempt to capture the essence of these tales. It is ultimately up to the human heart to inject meaning and life into them. For this reason, this collection is not just stories but living beings that have the potential to touch our lives as potently as mortals do. Should the circumambulation of the world seem tedious and wearisome, and should you feel the need to slow down and look inside your heart for peace rather than search for it in the meaningless rowdiness around you, then the stories in this collection will not disappoint you. |
| data science free lance: Breath from Salt Bijal P. Trivedi, 2020-09-08 Recommended by Bill Gates and included in GatesNotes Elaborating on the science as well as the business behind the fight against cystic fibrosis, Trivedi captures the emotions of the families, doctors, and scientists involved in the clinical trials and their 'weeping with joy' as new drugs are approved, and shows how cystic fibrosis, once a 'death sentence,' became, for many, a manageable condition. This is a rewarding and challenging work. —Publishers Weekly Cystic fibrosis was once a mysterious disease that killed infants and children. Now it could be the key to healing millions with genetic diseases of every type—from Alzheimer's and Parkinson's to diabetes and sickle cell anemia. In 1974, Joey O'Donnell was born with strange symptoms. His insatiable appetite, incessant vomiting, and a relentless cough—which shook his tiny, fragile body and made it difficult to draw breath—confounded doctors and caused his parents agonizing, sleepless nights. After six sickly months, his salty skin provided the critical clue: he was one of thousands of Americans with cystic fibrosis, an inherited lung disorder that would most likely kill him before his first birthday. The gene and mutation responsible for CF were found in 1989—discoveries that promised to lead to a cure for kids like Joey. But treatments unexpectedly failed and CF was deemed incurable. It was only after the Cystic Fibrosis Foundation, a grassroots organization founded by parents, formed an unprecedented partnership with a fledgling biotech company that transformative leaps in drug development were harnessed to produce groundbreaking new treatments: pills that could fix the crippled protein at the root of this deadly disease. From science writer Bijal P. Trivedi, Breath from Salt chronicles the riveting saga of cystic fibrosis, from its ancient origins to its identification in the dank autopsy room of a hospital basement, and from the CF gene's celebrated status as one of the first human disease genes ever discovered to the groundbreaking targeted genetic therapies that now promise to cure it. Told from the perspectives of the patients, families, physicians, scientists, and philanthropists fighting on the front lines, Breath from Salt is a remarkable story of unlikely scientific and medical firsts, of setbacks and successes, and of people who refused to give up hope—and a fascinating peek into the future of genetics and medicine. |
| data science free lance: Bayesian Inference in Statistical Analysis George E. P. Box, George C. Tiao, 2011-01-25 Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach. |
| data science free lance: The Science Writers' Handbook Writers of SciLance, 2013-04-30 Popular science writing has exploded in the past decade, both in print and online. Who better to guide writers striving to succeed in the profession than a group of award-winning independent journalists with a combined total of 225 years of experience? From Thomas Hayden's chapter on the perfect pitch to Emma Maris's advice on book proposals to Mark Schrope's essential information on contracts, the members of SciLance give writers of all experience levels the practical information they need to succeed, as either a staffer or a freelancer. Going beyond craft, The Science Writer's Handbook also tackles issues such as creating productive office space, balancing work and family, and finding lasting career satisfaction. It is the ultimate guide for anyone looking to prosper as a science writer in the new era of publishing. |
| data science free lance: Essential Math for Data Science Thomas Nield, 2022-05-26 Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market |
| data science free lance: Python One-Liners Christian Mayer, 2020-05-12 Python programmers will improve their computer science skills with these useful one-liners. Python One-Liners will teach you how to read and write one-liners: concise statements of useful functionality packed into a single line of code. You'll learn how to systematically unpack and understand any line of Python code, and write eloquent, powerfully compressed Python like an expert. The book's five chapters cover tips and tricks, regular expressions, machine learning, core data science topics, and useful algorithms. Detailed explanations of one-liners introduce key computer science concepts and boost your coding and analytical skills. You'll learn about advanced Python features such as list comprehension, slicing, lambda functions, regular expressions, map and reduce functions, and slice assignments. You'll also learn how to: • Leverage data structures to solve real-world problems, like using Boolean indexing to find cities with above-average pollution • Use NumPy basics such as array, shape, axis, type, broadcasting, advanced indexing, slicing, sorting, searching, aggregating, and statistics • Calculate basic statistics of multidimensional data arrays and the K-Means algorithms for unsupervised learning • Create more advanced regular expressions using grouping and named groups, negative lookaheads, escaped characters, whitespaces, character sets (and negative characters sets), and greedy/nongreedy operators • Understand a wide range of computer science topics, including anagrams, palindromes, supersets, permutations, factorials, prime numbers, Fibonacci numbers, obfuscation, searching, and algorithmic sorting By the end of the book, you'll know how to write Python at its most refined, and create concise, beautiful pieces of Python art in merely a single line. |
| data science free lance: Leaving the Rat Race with Python Christian Mayer (Computer Scientist), Lukas Rieger, 2020 |
| data science free lance: Data Analytics for Absolute Beginners: a Deconstructed Guide to Data Literacy Oliver Theobald, 2019-07-21 While exposure to data has become more or less a daily ritual for the rank-and-file knowledge worker, true understanding-treated in this book as data literacy-resides in knowing what lies behind the data. Everything from the data's source to the specific choice of input variables, algorithmic transformations, and visual representation shape the accuracy, relevance, and value of the data and mark its journey from raw data to business insight. It's also important to grasp the terminology and basic concepts of data analytics as much as it is to have the financial literacy to be successful as a decisionmaker in the business world. In this book, we make sense of data analytics without the assumption that you understand specific data science terminology or advanced programming languages to set you on your path. Topics covered in this book: Data Mining Big Data Machine Learning Alternative Data Data Management Web Scraping Regression Analysis Clustering Analysis Association Analysis Data Visualization Business Intelligence |
| data science free lance: JavaScript and jQuery for Data Analysis and Visualization Jon Raasch, Graham Murray, Vadim Ogievetsky, Joseph Lowery, 2014-12-03 Go beyond design concepts—build dynamic data visualizations using JavaScript JavaScript and jQuery for Data Analysis and Visualization goes beyond design concepts to show readers how to build dynamic, best-of-breed visualizations using JavaScript—the most popular language for web programming. The authors show data analysts, developers, and web designers how they can put the power and flexibility of modern JavaScript libraries to work to analyze data and then present it using best-of-breed visualizations. They also demonstrate the use of each technique with real-world use cases, showing how to apply the appropriate JavaScript and jQuery libraries to achieve the desired visualization. All of the key techniques and tools are explained in this full-color, step-by-step guide. The companion website includes all sample codes used to generate the visualizations in the book, data sets, and links to the libraries and other resources covered. Go beyond basic design concepts and get a firm grasp of visualization approaches and techniques using JavaScript and jQuery Discover detailed, step-by-step directions for building specific types of data visualizations in this full-color guide Learn more about the core JavaScript and jQuery libraries that enable analysis and visualization Find compelling stories in complex data, and create amazing visualizations cost-effectively Let JavaScript and jQuery for Data Analysis and Visualization be the resource that guides you through the myriad strategies and solutions for combining analysis and visualization with stunning results. |
| data science free lance: The Wealthy Freelancer Pete Savage, Steve Slaunwhite, 2010-03-02 Being your own boss can lead to incredible profts - here's how... Whether you call yourself a freelancer, consultant, independent contractor or solo professional of any kind, 'The Wealthy Freelancer: 12 Secrets to a Great Income and an Enviable Lifestyle', shows you how to get the clients, income, and lifestyle you deserve. So you can put more money in the bank, enjoy more time with your family and make a great living doing what you truly love to do, free from the burden of employment... Filled with proven ideas and real-world examples from dozens of successful freelancers, 'The Wealthy Freelancer' is essential reading for any solo professional who wants to enjoy a lifestyle that's 'wealthy' in every sense of the word. Here's a glimpse of what's waiting for you inside this book: * Why the typical one-size-fits-all marketing advice rarely works, and a fool-proof system for determining the optimal mix of marketing activities for your specific circumstances and goals. * How to get more prospects to say Yes! to the fees that you propose. * Why striving to be the best in your field almost never works, and what to do instead. * How to charge more - and earn more - by creating new income streams closely related to your core business. *How to have more time for the life you want and still have a great income. *How to test the waters and land freelance work now, even if you're already employed. * Why freelancing has moved beyond creative fields and into mainstream careers such as Engineering, Software Development, Bookkeeping, and more than 160 other professions. * Stories of real-life freelancers who destroy the myth that freelancers barely scrape by. * Dozens more proven tips and strategies to build a more profitable and fulfilling solo business. |
| data science free lance: Machine Learning with Python Cookbook Chris Albon, 2018-03-09 This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models |
| data science free lance: The Freelance Way Robert Vlach, 2022-03-02 The most comprehensive book for freelancers ever written - Packed with proven freelance know-how, including advice from world-class experts like David Allen (Getting Things Done), Adam Grant (Give and Take), Austin Kleon (Show Your Work), and David H. Hansson (Remote: Office Not Required). The Freelance Way is THE business book for independent professionals. It presents the best available and fully up-to-date freelance know-how, compiled from hundreds of quality sources, including surveys, the latest market data, advice from world-class experts, as well as real-life experiences and stories from hundreds of professionals in different fields and countries, which makes the book highly relevant to freelancers worldwide. The contents of this volume cover all the basics and best practices for beginning freelancers, as well as advanced career strategies and tools for freelance veterans. There are practical tips for greater productivity, successful teamwork, smart pricing, powerful business negotiations, bulletproof personal finance, effective marketing, and much more. |
| data science free lance: Data Science: The Hard Parts Daniel Vaughan, 2023-11 This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the big themes of the discipline—machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries. With this book, you will: Understand how data science creates value Deliver compelling narratives to sell your data science project Build a business case using unit economics principles Create new features for a ML model using storytelling Learn how to decompose KPIs Perform growth decompositions to find root causes for changes in a metric Daniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly). |
| data science free lance: Bring Your Human to Work: 10 Surefire Ways to Design a Workplace That Is Good for People, Great for Business, and Just Might Change the World Erica Keswin, 2018-09-28 WALL STREET JOURNAL BESTSELLER The secret to business success? Get REAL and be HUMAN! As human beings, we are built to connect and form relationships. So, it should be no surprise that relationships must also translate into the workplace, where we spend most of our time! Companies that recognize this will retain the most productive, creative, and loyal employees, and invariably seize the competitive edge. The most successful leaders are those who actively form quality relationships with their employees, who honor fundamental human qualities—authenticity, openness, and basic politeness—and apply them day in and day out. Paying attention and genuinely caring about the effects people have on one another other is key to developing a winning culture where people perform at the top of their game and want to work. As a workplace strategist and business coach, Erica Keswin has spent over 20 years working with top business leaders and executives to build successful organizations that honor relationships. Featuring case studies from top brands such as, Lyft, Starbucks, Mogul, and SoulCycle, to name a few, Bring Your Human to Work distills the key practices of the most human companies into applicable advice that any business leader can use to build a “human workplace.” These building blocks include: • Understanding your company’s role in the world, beyond financial profit • Encouraging employees to be healthy in body and spirit • Running your meetings with clear purpose • Making space for face-to-face interaction • Building professional development into company culture • Inspiring your workforce to give back to the community • Simply saying “thank you” A human company is real, genuine, aligned, and true to itself. A real company flaunts its humanity, instead of hiding it. It’s what the most successful, sustainable companies are doing today, and there’s no reason yours can’t be the same. Keswin’s leadership lessons foster fairness, devotion, and joy in the workplace—all critical elements of a successful business. By bringing your human to work, you can design a workplace that is good for people, great for business, and just might change the world. |
| data science free lance: 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 free lance: Learning Data Science Sam Lau, Joseph Gonzalez, Deborah Nolan, 2023-09-15 As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the technical/nontechnical divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas. Refine a question of interest to one that can be studied with data Pursue data collection that may involve text processing, web scraping, etc. Glean valuable insights about data through data cleaning, exploration, and visualization Learn how to use modeling to describe the data Generalize findings beyond the data |
| data science free lance: Learn Data Science Using Python Engy Fouda, |
| data science free lance: Python and R for the Modern Data Scientist Rick J. Scavetta, Boyan Angelov, 2021-06-22 Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together |
| data science free lance: Establishing a Freelance Interpretation Business Tammera J Richards, 2019-06-06 This book is practical business guidance for sign language interpreters looking to establish a freelance interpreting practice. Interpreter training programs often lack basic business-related coursework, and this book is designed to fill that gap. |
| data science free lance: Make Art with Python Kirk Kaiser, 2019-03-21 |
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 and open science. This will enable a …
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 data by default, and made available with minimum time …
Belmont Forum Adopts Open Data Principles for Environmental …
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 Management for Global Change Research, released in …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process from …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical barriers …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be collected, …
