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| data science gig work: Data Science Jobs Ann Rajaram, Want a high-paying $$$ career in the exciting field of DataScience? This is the ONLY book that will help you land a lucrative Analytics job in 90 days or less! This book is the perfect guide for you, if you fall into any of these categories: * You recently completed a masters degree (or online course or bootcamp) and want to get hired quickly as a Data Scientist, Data Analyst, Data Engineer, Machine learning engineer or BI developer. * Looking to start a career in data science, but unsure where to start. * You are an experienced tech professional, but looking to pivot into analytics to boost your salary potential. * Tired of applying to dozens of jobs without getting a positive response and/or final job offer . * F1 visa, STEM OPT/ CPT students will also find this book helpful to land a job in this lucrative field. The book will teach you proven successful strategies on: * Winning Profiles Turbocharge your resume and LinkedIn profile and start receiving interview calls from hiring managers. Let JOBS CHASE YOU, instead of the other way around! * LinkedIn - A dedicated chapter on LinkedIn that teaches you some creative (and SECRET) ways to leverage the site and identify high-paying jobs with low competition. * Niche sites - A full list of niche job boards that other candidates have overlooked. These sites have high-$ jobs but lesser competition than the popular job search sites. Upwork - Contrary to popular opinion, Upwork can help you make $$$ in data science jobs. Learn proven techniques to help you bag contracts and start earning, as quickly as next week. * 100+ interview questions asked in real-life data scientist interviews. * Other learner resources and much more... Author is a practicing analytics professional who has worked in Fortune500 Firms like NASDAQ , BlackRock, etc. Unlike most job search books that are written by recruiters or professors, this book is written by a senior professional, who rose quickly from analyst to managerial roles. She has attended interviews of her own, and knows clearly the frustrations (and at times, hopelessness) of the job search process. The systems in this book have successfully helped dozens of job seekers and will work effectively for you too! Read on to launch your dream career! Note, this book is deliberately kept short and precise, so you can quickly read through and start applying these principles, instead of sifting through 500 pages of fluff. This book includes: Data Science interview questions and answers; Help preparing for Machine Learning Interviews; Top 25 Interview Questions for Data Analyst/Scientist roles; An in-depth overview of Data Science Interview Process; How to ace your interview even if you are an Entry level Data Analyst / Data Scientist; Data Science Interview questions for freshers; How and Where to look for jobs; and much more! |
| data science gig work: 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 gig work: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| data science gig work: Getting Started with Streamlit for Data Science Tyler Richards, 2021-08-20 Create, deploy, and test your Python applications, analyses, and models with ease using Streamlit Key Features Learn how to showcase machine learning models in a Streamlit application effectively and efficiently Become an expert Streamlit creator by getting hands-on with complex application creation Discover how Streamlit enables you to create and deploy apps effortlessly Book DescriptionStreamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.What you will learn Set up your first development environment and create a basic Streamlit app from scratch Explore methods for uploading, downloading, and manipulating data in Streamlit apps Create dynamic visualizations in Streamlit using built-in and imported Python libraries Discover strategies for creating and deploying machine learning models in Streamlit Use Streamlit sharing for one-click deployment Beautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebar Implement best practices for prototyping your data science work with Streamlit Who this book is for This book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you’re a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered. |
| data science gig work: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
| data science gig work: 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 gig work: 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 gig work: Hands-On Data Science for Marketing Yoon Hyup Hwang, 2019-03-29 Optimize your marketing strategies through analytics and machine learning Key FeaturesUnderstand how data science drives successful marketing campaignsUse machine learning for better customer engagement, retention, and product recommendationsExtract insights from your data to optimize marketing strategies and increase profitabilityBook Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learnLearn how to compute and visualize marketing KPIs in Python and RMaster what drives successful marketing campaigns with data scienceUse machine learning to predict customer engagement and lifetime valueMake product recommendations that customers are most likely to buyLearn how to use A/B testing for better marketing decision makingImplement machine learning to understand different customer segmentsWho this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples. |
| data science gig work: A First Course in Machine Learning Simon Rogers, Mark Girolami, 2016-10-14 Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/ |
| data science gig work: 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 gig work: Linear Algebra and Learning from Data Gilbert Strang, 2019-01-31 Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation. |
| data science gig work: Discovering Statistics Using R Andy Field, Jeremy Miles, Zoë Field, 2012-03-07 Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field′s books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you′re doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more. Given this book′s accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software. |
| data science gig work: Fifty Challenging Problems in Probability with Solutions Frederick Mosteller, 2012-04-26 Remarkable puzzlers, graded in difficulty, illustrate elementary and advanced aspects of probability. These problems were selected for originality, general interest, or because they demonstrate valuable techniques. Also includes detailed solutions. |
| data science gig work: Business Intelligence Demystified Anoop Kumar V K, 2021-09-25 Clear your doubts about Business Intelligence and start your new journey KEY FEATURES ● Includes successful methods and innovative ideas to achieve success with BI. ● Vendor-neutral, unbiased, and based on experience. ● Highlights practical challenges in BI journeys. ● Covers financial aspects along with technical aspects. ● Showcases multiple BI organization models and the structure of BI teams. DESCRIPTION The book demystifies misconceptions and misinformation about BI. It provides clarity to almost everything related to BI in a simplified and unbiased way. It covers topics right from the definition of BI, terms used in the BI definition, coinage of BI, details of the different main uses of BI, processes that support the main uses, side benefits, and the level of importance of BI, various types of BI based on various parameters, main phases in the BI journey and the challenges faced in each of the phases in the BI journey. It clarifies myths about self-service BI and real-time BI. The book covers the structure of a typical internal BI team, BI organizational models, and the main roles in BI. It also clarifies the doubts around roles in BI. It explores the different components that add to the cost of BI and explains how to calculate the total cost of the ownership of BI and ROI for BI. It covers several ideas, including unconventional ideas to achieve BI success and also learn about IBI. It explains the different types of BI architectures, commonly used technologies, tools, and concepts in BI and provides clarity about the boundary of BI w.r.t technologies, tools, and concepts. The book helps you lay a very strong foundation and provides the right perspective about BI. It enables you to start or restart your journey with BI. WHAT YOU WILL LEARN ● Builds a strong conceptual foundation in BI. ● Gives the right perspective and clarity on BI uses, challenges, and architectures. ● Enables you to make the right decisions on the BI structure, organization model, and budget. ● Explains which type of BI solution is required for your business. ● Applies successful BI ideas. WHO THIS BOOK IS FOR This book is a must-read for business managers, BI aspirants, CxOs, and all those who want to drive the business value with data-driven insights. TABLE OF CONTENTS 1. What is Business Intelligence? 2. Why do Businesses need BI? 3. Types of Business Intelligence 4. Challenges in Business Intelligence 5. Roles in Business Intelligence 6. Financials of Business Intelligence 7. Ideas for Success with BI 8. Introduction to IBI 9. BI Architectures 10. Demystify Tech, Tools, and Concepts in BI |
| data science gig work: 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 gig work: Gig John Bowe, Marisa Bowe, Sabin Streeter, 2001-08-21 “An engaging, humorous, revealing, and refreshingly human look at the bizarre, life-threatening, and delightfully humdrum exploits of everyone from sports heroes to sex workers.” -- Douglas Rushkoff, author of Coercion, Ecstasy Club, and Media Virus This wide-ranging survey of the American economy at the turn of the millennium is stunning, surprising, and always entertaining. It gives us an unflinching view of the fabric of this country from the point of view of the people who keep it all moving. The more than 120 roughly textured monologues that make up Gig beautifully capture the voices of our fast-paced and diverse economy. The selections demonstrate how much our world has changed--and stayed the same--in the three decades prior to the turn of the millennium. If you think things have speeded up, become more complicated and more technological, you're right. But people's attitudes about their jobs, their hopes and goals and disappointments, endure. Gig's soul isn't sociological--it's emotional. The wholehearted diligence that people bring to their work is deeply, inexplicably moving. People speak in these pages of the constant and complex stresses nearly all of them confront on the job, but, nearly universally, they throw themselves without reservation into coping with them. Instead of resisting work, we seem to adapt to it. Some of us love our jobs, some of us don't, but almost all of us are not quite sure what we would do without one. With all the hallmarks of another classic on this subject, Gig is a fabulous read, filled with indelible voices from coast to coast. After hearing them, you'll never again feel quite the same about how we work. |
| data science gig work: Humans and Machines at Work Phoebe V. Moore, Martin Upchurch, Xanthe Whittaker, 2017-10-06 This edited collection provides a series of accounts of workers’ local experiences that reflect the ubiquity of work’s digitalisation. Precarious gig economy workers ride bikes and drive taxis in China and Britain; call centre workers in India experience invasive tracking; warehouse workers discover that hidden data has been used for layoffs; and academic researchers see their labour obscured by a ‘data foam’ that does not benefit them. These cases are couched in historical accounts of identity and selfhood experiments seen in the Hawthorne experiments and the lineage of automation. This book will appeal to scholars in the Sociology of Work and Digital Labour Studies and anyone interested in learning about monitoring and surveillance, automation, the gig economy and the quantified self in the workplace. |
| data science gig work: The Gig Academy Adrianna Kezar, Tom DePaola, Daniel T. Scott, 2019-10-29 Why the Gig Academy is the dominant organizational form within the higher education economy—and its troubling implications for faculty, students, and the future of college education. Over the past two decades, higher education employment has undergone a radical transformation with faculty becoming contingent, staff being outsourced, and postdocs and graduate students becoming a larger share of the workforce. For example, the faculty has shifted from one composed mostly of tenure-track, full-time employees to one made up of contingent, part-time teachers. Non-tenure-track instructors now make up 70 percent of college faculty. Their pay for teaching eight courses averages $22,400 a year—less than the annual salary of most fast-food workers. In The Gig Academy, Adrianna Kezar, Tom DePaola, and Daniel T. Scott assess the impact of this disturbing workforce development. Providing an overarching framework that takes the concept of the gig economy and applies it to the university workforce, this book scrutinizes labor restructuring across both academic and nonacademic spheres. By synthesizing these employment trends, the book reveals the magnitude of the problem for individual workers across all institutional types and job categories while illustrating the damaging effects of these changes on student outcomes, campus community, and institutional effectiveness. A pointed critique of contemporary neoliberalism, the book also includes an analysis of the growing divide between employees and administrators. The authors conclude by examining the strengthening state of unionization among university workers. Advocating a collectivist, action-oriented vision for reversing the tide of exploitation, Kezar, DePaola, and Scott urge readers to use the book as a tool to interrogate the state of working relations on their own campuses and fight for a system that is run democratically for the benefit of all. Ultimately, The Gig Academy is a call to arms, one that encourages non-tenure-track faculty, staff, postdocs, graduate students, and administrative and tenure-track allies to unite in a common struggle against the neoliberal Gig Academy. |
| data science gig work: Business Statistics for Contemporary Decision Making Ignacio Castillo, Ken Black, Tiffany Bayley, 2023-05-08 Show students why business statistics is an increasingly important business skill through a student-friendly pedagogy. In this fourth Canadian edition of Business Statistics For Contemporary Decision Making authors Ken Black, Tiffany Bayley, and Ignacio Castillo uses current real-world data to equip students with the business analytics techniques and quantitative decision-making skills required to make smart decisions in today's workplace. |
| data science gig work: Career Pathways Jerry W. Hedge, Gary W. Carter, 2020 Major changes have occurred in the workplace during the last several decades that have transformed the nature of work, and our preparation for work. In recent years, we have seen the globalization of thousands of companies and most industries, organizational downsizing and restructuring, greater use of information technology at work, changes in work contracts, and the growth of various alternative education and work strategies and schedules-- |
| data science gig work: Can Science Make Sense of Life? Sheila Jasanoff, 2019-03-05 Since the discovery of the structure of DNA and the birth of the genetic age, a powerful vocabulary has emerged to express science’s growing command over the matter of life. Armed with knowledge of the code that governs all living things, biology and biotechnology are poised to edit, even rewrite, the texts of life to correct nature’s mistakes. Yet, how far should the capacity to manipulate what life is at the molecular level authorize science to define what life is for? This book looks at flash points in law, politics, ethics, and culture to argue that science’s promises of perfectibility have gone too far. Science may have editorial control over the material elements of life, but it does not supersede the languages of sense-making that have helped define human values across millennia: the meanings of autonomy, integrity, and privacy; the bonds of kinship, family, and society; and the place of humans in nature. |
| data science gig work: The Quantified Self in Precarity Phoebe V. Moore, 2017-09-11 Humans are accustomed to being tool bearers, but what happens when machines become tool bearers, calculating human labour via the use of big data and people analytics by metrics? The Quantified Self in Precarity highlights how, whether it be in insecure ‘gig’ work or office work, such digitalisation is not an inevitable process – nor is it one that necessarily improves working conditions. Indeed, through unique research and empirical data, Moore demonstrates how workplace quantification leads to high turnover rates, workplace rationalisation and worker stress and anxiety, with these issues linked to increased rates of subjective and objective precarity. Scientific management asked us to be efficient. Now, we are asked to be agile. But what does this mean for the everyday lives we lead? With a fresh perspective on how technology and the use of technology for management and self-management changes the ‘quantified’, precarious workplace today, The Quantified Self in Precarity will appeal to undergraduate and postgraduate students interested in fields such as Science and Technology, Organisation Management, Sociology and Politics. |
| data science gig work: Hire Purpose Deanna Mulligan, Greg Shaw, 2020-10-13 A WALL STREET JOURNAL BUSINESS BESTSELLER The future of work is already here, and what this future looks like must be a pressing concern for the current generation of leaders in both the private and public sectors. In the next ten to fifteen years, rapid change in a post-pandemic world and emerging technology will revolutionize nearly every job, eliminate some, and create new forms of work that we have yet to imagine. How can we survive and thrive in the face of such drastic change? Deanna Mulligan offers a practical, broad-minded look at the effects of workplace evolution and automation and why the private sector needs to lead the charge in shaping a values-based response. With a focus on the power of education, Mulligan proposes that the solutions to workforce upheaval lie in reskilling and retraining for individuals and companies adapting to rapid change. By creating lifelong learning opportunities that break down boundaries between the classroom and the workplace, businesses can foster personal and career well-being and growth for their employees. Drawing on her own experiences, historical examples, and reports from the frontiers where these issues are unfolding, Mulligan details how business leaders can prepare for and respond to technological disruption. Providing a framework for concrete and meaningful action, Hire Purpose is an essential read about the transformations that will shape the next decade and beyond. |
| data science gig work: Waste Kate O'Neill, 2019-09-04 Waste is one of the planet’s last great resource frontiers. From furniture made from up-cycled wood to gold extracted from computer circuit boards, artisans and multinational corporations alike are finding ways to profit from waste while diverting materials from overcrowded landfills. Yet beyond these benefits, this “new” resource still poses serious risks to human health and the environment. In this unique book, Kate O’Neill traces the emergence of the global political economy of wastes over the past two decades. She explains how the emergence of waste governance initiatives and mechanisms can help us deal with both the risks and the opportunities associated with the hundreds of millions – possibly billions – of tons of waste we generate each year. Drawing on a range of fascinating case studies to develop her arguments, including China’s role as the primary recipient of recyclable plastics and scrap paper from the Western world, “Zero-Waste” initiatives, the emergence of transnational waste-pickers’ alliances, and alternatives for managing growing volumes of electronic and food wastes, O’Neill shows how waste can be a risk, a resource, and even a livelihood, with implications for governance at local, national, and global levels. |
| data science gig work: Data Science Job: How to become a Data Scientist Przemek Chojecki, 2020-01-31 We’re living in a digital world. Most of our global economy is digital and the sheer volume of data is stupendous. It’s 2020 and we’re living in the future. Data Scientist is one of the hottest job on the market right now. Demand for data science is huge and will only grow, and it seems like it will grow much faster than the actual number of data scientists. So if you want to make a career change and become a data scientist, now is the time. This book will guide you through the process. From my experience of working with multiple companies as a project manager, a data science consultant or a CTO, I was able to see the process of hiring data scientists and building data science teams. I know what’s important to land your first job as a data scientist, what skills you should acquire, what you should show during a job interview. |
| data science gig work: Data Science for Entrepreneurship Werner Liebregts, Willem-Jan van den Heuvel, Arjan van den Born, 2023-03-23 The fast-paced technological development and the plethora of data create numerous opportunities waiting to be exploited by entrepreneurs. This book provides a detailed, yet practical, introduction to the fundamental principles of data science and how entrepreneurs and would-be entrepreneurs can take advantage of it. It walks the reader through sections on data engineering, and data analytics as well as sections on data entrepreneurship and data use in relation to society. The book also offers ways to close the research and practice gaps between data science and entrepreneurship. By having read this book, students of entrepreneurship courses will be better able to commercialize data-driven ideas that may be solutions to real-life problems. Chapters contain detailed examples and cases for a better understanding. Discussion points or questions at the end of each chapter help to deeply reflect on the learning material. |
| data science gig work: Ghost Work Mary L. Gray, Siddharth Suri, 2019 A startling exposé of the invisible human workforce that powers the web--and how to bring it out of the shadows. Hidden beneath the surface of the internet, a new, stark reality is looming--one that cuts to the very heart of our endless debates about the impact of AI. Anthropologist Mary L. Gray and computer scientist Siddharth Suri unveil how the services we use from companies like Amazon, Google, Microsoft, and Uber can only function smoothly thanks to the judgment and experience of a vast human labor force that is kept deliberately concealed. The people who do 'ghost work' make the internet seem smart. They perform high-tech, on-demand piecework: flagging X-rated content, proofreading, transcribing audio, confirming identities, captioning video, and much more. The shameful truth is that no labor laws protect them or even acknowledge their existence. They often earn less than legal minimums for traditional work, they have no health benefits, and they can be fired at any time for any reason, or for no reason at all. An estimated 8 percent of Americans have worked in this 'ghost economy,' and that number is growing every day. In this unprecedented investigation, Gray and Suri make the case that robots will never completely eliminate 'ghost work' and the unchecked quest for artificial intelligence could spark catastrophic work conditions if not stopped in its tracks. Ultimately, they show how this essential type of work can create opportunity--rather than misery--for those who do it.--Dust jacket. |
| data science gig work: Surveillance After Snowden David Lyon, 2015-10-19 In 2013, Edward Snowden revealed that the NSA and its partners had been engaging in warrantless mass surveillance, using the internet and cellphone data, and driven by fear of terrorism under the sign of ’security’. In this compelling account, surveillance expert David Lyon guides the reader through Snowden’s ongoing disclosures: the technological shifts involved, the steady rise of invisible monitoring of innocent citizens, the collusion of government agencies and for-profit companies and the implications for how we conceive of privacy in a democratic society infused by the lure of big data. Lyon discusses the distinct global reactions to Snowden and shows why some basic issues must be faced: how we frame surveillance, and the place of the human in a digital world. Surveillance after Snowden is crucial reading for anyone interested in politics, technology and society. |
| data science gig work: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| data science gig work: Data Analytics James Smith, 2016-07-05 Are You Actively Analyzing the Data Surrounding Your Business? Keep Reading to Learn Why You Should Be.. You may be the owner of a business, or someone who actively participates in the day to day operations of a business. We will go ahead and assume that your business is operating at a profit and you are happy with the direction it is going. As someone in this situation you might ask yourself, Why do I need Data Analysis anyways?. I'll tell you why, one simple reason. You are leaving money on the table. Let's put it this way.. you are doing good, but wouldn't you rather be doing great? Wouldn't you rather have the ability to predict how the consumers in your target market are going to be behaving a year from now? Five years from now? This is where Data Analysis comes in. Many people realize the need to pay attention to data in their business, but have no clue where to start. With the help of this book you will be better able to understand the importance of the data surrounding your business and exactly what to do with it. A Preview of What You Will Learn The Importance of Data in Business Exactly How to Handle and Manage Big Data Real World Examples of Data Science Benefiting Businesses Ways Data Can Be Used to Mitigate Risks The Entire Process of Data Analytics Much, much more! Take charge of your business today and buy this book! |
| data science gig work: AI-Driven Marketing Research and Data Analytics Masengu, Reason, Chiwaridzo, Option Takunda, Dube, Mercy, Ruzive, Benson, 2024-04-22 The surge in technological advancements, coupled with the exponential growth of data, has left marketers grappling with the need for a paradigm shift. The once-established methods of consumer engagement are now overshadowed by the complexities of the digital age, demanding a profound understanding of artificial intelligence (AI) and data analytics. The gap between academic knowledge and practical applications in the field of marketing has widened, leaving industry professionals, educators, and students seeking a comprehensive resource to navigate the intricacies of this transformative era. AI-Driven Marketing Research and Data Analytics is a groundbreaking book that serves as a beacon for marketers, educators, and industry leaders alike. With a keen focus on the symbiotic relationship between AI, data analytics, and marketing research, this book bridges the gap between theory and practice. It not only explores the historical evolution of marketing but also provides an innovative examination of how AI and data analytics are reshaping the landscape. Through real-time case studies, ethical considerations, and in-depth insights, the book offers a holistic solution to the challenges faced by marketing professionals in the digital age. |
| data science gig work: Artificial Intelligence and the Future of Defense Stephan De Spiegeleire, Matthijs Maas, Tim Sweijs, 2017-05-17 Artificial intelligence (AI) is on everybody’s minds these days. Most of the world’s leading companies are making massive investments in it. Governments are scrambling to catch up. Every single one of us who uses Google Search or any of the new digital assistants on our smartphones has witnessed first-hand how quickly these developments now go. Many analysts foresee truly disruptive changes in education, employment, health, knowledge generation, mobility, etc. But what will AI mean for defense and security? In a new study HCSS offers a unique perspective on this question. Most studies to date quickly jump from AI to autonomous (mostly weapon) systems. They anticipate future armed forces that mostly resemble today’s armed forces, engaging in fairly similar types of activities with a still primarily industrial-kinetic capability bundle that would increasingly be AI-augmented. The authors of this study argue that AI may have a far more transformational impact on defense and security whereby new incarnations of ‘armed force’ start doing different things in novel ways. The report sketches a much broader option space within which defense and security organizations (DSOs) may wish to invest in successive generations of AI technologies. It suggests that some of the most promising investment opportunities to start generating the sustainable security effects that our polities, societies and economies expect may lie in in the realms of prevention and resilience. Also in those areas any large-scale application of AI will have to result from a preliminary open-minded (on all sides) public debate on its legal, ethical and privacy implications. The authors submit, however, that such a debate would be more fruitful than the current heated discussions about ‘killer drones’ or robots. Finally, the study suggests that the advent of artificial super-intelligence (i.e. AI that is superior across the board to human intelligence), which many experts now put firmly within the longer-term planning horizons of our DSOs, presents us with unprecedented risks but also opportunities that we have to start to explore. The report contains an overview of the role that ‘intelligence’ - the computational part of the ability to achieve goals in the world - has played in defense and security throughout human history; a primer on AI (what it is, where it comes from and where it stands today - in both civilian and military contexts); a discussion of the broad option space for DSOs it opens up; 12 illustrative use cases across that option space; and a set of recommendations for - especially - small- and medium sized defense and security organizations. |
| data science gig work: Fixing Broken Windows George L. Kelling, Catherine M. Coles, 1997 Cites successful examples of community-based policing. |
| data science gig work: Race After Technology Ruha Benjamin, 2019-07-09 From everyday apps to complex algorithms, Ruha Benjamin cuts through tech-industry hype to understand how emerging technologies can reinforce White supremacy and deepen social inequity. Benjamin argues that automation, far from being a sinister story of racist programmers scheming on the dark web, has the potential to hide, speed up, and deepen discrimination while appearing neutral and even benevolent when compared to the racism of a previous era. Presenting the concept of the “New Jim Code,” she shows how a range of discriminatory designs encode inequity by explicitly amplifying racial hierarchies; by ignoring but thereby replicating social divisions; or by aiming to fix racial bias but ultimately doing quite the opposite. Moreover, she makes a compelling case for race itself as a kind of technology, designed to stratify and sanctify social injustice in the architecture of everyday life. This illuminating guide provides conceptual tools for decoding tech promises with sociologically informed skepticism. In doing so, it challenges us to question not only the technologies we are sold but also the ones we ourselves manufacture. Visit the book's free Discussion Guide: www.dropbox.com |
| data science gig work: 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 gig work: Multivariable Calculus James Stewart, 2011-09-27 Success in your calculus course starts here! James Stewart's CALCULUS, 7e, International Metric texts are world-wide best-sellers for a reason: they are clear, accurate, and filled with relevant, real-world examples. With MULTIVARIABLE CALCULUS, 7e, International Metric Edition Stewart conveys not only the utility of calculus to help you develop technical competence, but also gives you an appreciation for the intrinsic beauty of the subject. His patient examples and built-in learning aids will help you build your mathematical confidence and achieve your goals in the course! |
| data science gig work: Platform Economy Puzzles Meijerink, Jeroen, Jansen, Giedo, Daskalova, Victoria, 2021-08-27 Searching for paid tasks via digital labour platforms, such as Uber, Deliveroo and Fiverr, has become a global phenomenon and the regular source of income for millions of people. In the advent of digital labour platforms, this insightful book sheds new light on familiar questions about tensions between competition and cooperation, short-term gains and long-term success, and private benefits and public costs. Drawing on a wealth of knowledge from a range of disciplines, including law, management, psychology, economics, sociology and geography, it pieces together a nuanced picture of the societal challenges posed by the platform economy. |
| data science gig work: The Economics of World War I Stephen Broadberry, Mark Harrison, 2005-09-29 This unique volume offers a definitive new history of European economies at war from 1914 to 1918. It studies how European economies mobilised for war, how existing economic institutions stood up under the strain, how economic development influenced outcomes and how wartime experience influenced post-war economic growth. Leading international experts provide the first systematic comparison of economies at war between 1914 and 1918 based on the best available data for Britain, Germany, France, Russia, the USA, Italy, Turkey, Austria-Hungary and the Netherlands. The editors' overview draws some stark lessons about the role of economic development, the importance of markets and the damage done by nationalism and protectionism. A companion volume to the acclaimed The Economics of World War II, this is a major contribution to our understanding of total war. |
| data science gig work: Canguilhem Stuart Elden, 2019-07-12 Georges Canguilhem (1904–95) was an influential historian and philosopher of science, as renowned for his teaching as for his writings. He is best known for his book The Normal and the Pathological, originally his doctoral thesis in medicine, but he also wrote a thesis in philosophy on the concept of the reflex, supervised by Gaston Bachelard. He was the sponsor of Michel Foucault’s doctoral thesis on madness. However, his work extends far beyond what is suggested by his association with these thinkers. Canguilhem also produced a series of important works on the natural sciences, including studies of evolution, psychology, vitalism and mechanism, experimentation, monstrosity and disease. Stuart Elden discusses the whole of this important thinker’s complex work, including recently rediscovered texts and archival materials. Canguilhem always approached questions historically, examining how it was that we came to a significant moment in time, outlining tensions, detours and paths not taken. The first comprehensive study in English, this book is a crucial guide for those coming to terms with Canguilhem’s important contributions, and will appeal to researchers and students from a range of fields. |
| data science gig work: Augmented Exploitation Phoebe V. Moore, Jamie Woodcock, 2021 Artificial intelligence should be changing society, not reinforcing capitalist notions of work. |
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, …
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 …
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 …
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, …
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 …
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 …
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 …
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 …
