Advertisement
| freelance data science 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. |
| freelance data science 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. |
| freelance data science work: 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 |
| freelance data science 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. |
| freelance data science work: 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. |
| freelance data science 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 |
| freelance data science 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 |
| freelance data science work: 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 |
| freelance data science work: 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. |
| freelance data science work: 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! |
| freelance data science work: 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. |
| freelance data science 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) |
| freelance data science work: 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. |
| freelance data science work: 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 |
| freelance data science work: 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 |
| freelance data science work: 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-- |
| freelance data science work: 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-- |
| freelance data science work: Data Mining and Predictive Analytics Daniel T. Larose, 2015-02-19 Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives. |
| freelance data science work: 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. |
| freelance data science work: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| freelance data science work: 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. |
| freelance data science work: Cracking the Data Science Interview Leondra R. Gonzalez, Aaren Stubberfield, 2024-02-29 Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much more Key Features Acquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learning Gain the confidence to explain complex statistical, machine learning, and deep learning theory Extend your expertise beyond model development with version control, shell scripting, and model deployment fundamentals Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.What you will learn Explore data science trends, job demands, and potential career paths Secure interviews with industry-standard resume and portfolio tips Practice data manipulation with Python and SQL Learn about supervised and unsupervised machine learning models Master deep learning components such as backpropagation and activation functions Enhance your productivity by implementing code versioning through Git Streamline workflows using shell scripting for increased efficiency Who this book is for Whether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews. |
| freelance data science work: Work Disrupted Jeff Schwartz, 2021-01-07 If you only read one book on the future of work, Work Disrupted: Opportunity, Resilience, and Growth in the Accelerated Future of Work should be that book. The future of work swept in sooner than expected, accelerated by Covid-19, creating an urgent need for new maps, new mindsets, new strategies-- and most importantly, a trusted guide to take us on this journey. That guide is Jeff Schwartz. A founding partner of Deloitte Consulting’s Future of Work practice, Schwartz brings clarity, humor, wisdom, and practical advice to the future of work, a topic surrounded by misinformation, fear, and confusion. With a fundamental belief in the power of human innovation and creativity, Schwartz presents the key issues, critical choices, and potential pitfalls that must be on everyone’s radar. If you're anxious about robots taking away your job in the future, you will take comfort in the realistic perspective, fact-based insights, and practical steps Schwartz offers. If you're not sure where to even begin to prepare, follow his level-headed advice and easy-to-follow action plans. If you're a business leader caught between keeping up, while also being thoughtful about the next moves, you will appreciate the playbook directed at you. If you're wondering how Covid-19 will change how and where you will work, Work Disrupted has you covered. Written in a conversational style by Schwartz, with Suzanne Riss, an award-winning journalist and book author, Work Disrupted offers a welcome alternative to books on the topic that lack a broad perspective or dwell on the problems rather than offer solutions. Timely and insightful, the book includes the impact of Covid-19 on our present and future work. Interviews with leading thinkers on the future of work offer additional perspectives and guidance.Cartoons created for the book by leading business illustrator Tom Fishburne bring to life the reader’s journey and the complex issues surrounding the topic. Told from the perspective of an economist, management advisor, and social commentator, Work Disrupted offers hope--and practical advice--exploring such topics as: How we frame what lies ahead is a critical navigational tool. Discover the signposts that can serve as practical guides for individuals who have families to support, mortgages to pay, and want to stay gainfully employed no matter what the future holds. The importance of recognizing the rapidly evolving opportunities in front of us. Learn how to build resilience—in careers, organizations, and leaders—for what lies ahead. Why exploring new mental models helps us discover the steps we need to take to thrive. Individuals can decide how to protect their livelihood while businesses and public institutions can consider how they can lead and support workforces to thrive in twenty-first-century careers and work. Jeff's marvelous book is a roadmap for the new world of work with clear signposts. His insights will help readers discover opportunities, take action, and find hope in uncertain times. The ideas are fresh, beautifully crafted, and immediately applicable. This is not only a book to be read, but savored and used. —Dave Ulrich, Rensis Likert Professor, Ross School of Business, University of Michigan; Partner, the RBL Group; Co-author Reinventing the Organization |
| freelance data science work: 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. |
| freelance data science work: 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. |
| freelance data science work: Software Engineering for Data Scientists Catherine Nelson, 2024-04-16 Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more |
| freelance data science 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. |
| freelance data science work: 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 |
| freelance data science work: Ethnographies of Work Rick Delbridge, Markus Helfen, Andreas Pekarek, Gretchen Purser, 2023-12-12 Presenting cutting-edge ethnographic research on contemporary worlds of work and the experiences of workers from a range of contexts, this volume offers fine-grained, exploratory ethnographic data to provide insights unmatched by other research methods. |
| freelance data science work: 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. |
| freelance data science work: Making Big Data Work for Your Business Sudhi Sinha, 2014-10-28 If your are interested in the power of Big Data to drive improvement in your business, then this book will help you build and initiate a project for positive change. |
| freelance data science work: Content Inc.: How Entrepreneurs Use Content to Build Massive Audiences and Create Radically Successful Businesses Joe Pulizzi, 2015-09-04 “Instead of throwing money away and sucking up to A-listers, now there is a better way to promote your business. It’s called content marketing, and this book is a great way to master this new technique.” -Guy Kawasaki, Chief evangelist of Canva and author of The Art of the Start 2.0 How do you take the maximum amount of risk out of starting a business? Joe Pulizzi shows us. Fascinate your audience, then turn them into loyal fans. Content Inc. shows you how. Use it as your roadmap to startup success.” -Sally Hogshead, New York Times and Wall Street Journal bestselling author, How the World Sees You If you're serious about turning content into a business, this is the most detailed, honest, and useful book ever written. -Jay Baer, New York Times bestselling author of Youtility The approach to business taught all over the world is to create a product and then spend a bunch of money to market and sell it. Joe outlines a radically new way to succeed in business: Develop your audience first by creating content that draws people in and then watch your business sell themselves! -David Meerman Scott bestselling author of ten books including The New Rules of Sales and Service The digital age has fundamentally reshaped the cost curve for entrepreneurs. Joe describes the formula for developing a purpose-driven business that connects with an engaged and loyal audience around content. With brand, voice and audience, building and monetizing a business is easy. -Julie Fleischer, Sr. Director, Data + Content + Media, Kraft Foods What if you launched a business with nothing to sell, and instead focused first on serving the needs of an audience, trusting that the 'selling' part would come later? Crazy? Or crazy-brilliant? I'd say the latter. Because in today's world, you should serve before selling. -Ann Handley, author of the Wall Street Journal bestseller Everybody Writes and Content Rules Today, anyone, anywhere with a passion and a focus on a content niche can build a multi-million dollar platform and business. I did it and so can you. Just follow Joe's plan and hisContent Inc. model. -John Lee Dumas, Founder, EntrepreneurOnFire The Internet doesn't need more content. It needs amazing content. Content Inc is the business blueprint on how to achieve that. If you're in business and are tired of hearing about the need for content marketing, but want the how and the proof, Content Inc is your blueprint. -Scott Stratten, bestselling author and President of UnMarketing Inc. Content marketing is by far the best marketing strategy for every company and Joe is by far the best guru on the topic. I wish this book was available when we started our content marketing initiative. It would have saved us a huge amount of time and effort! -Scott Maxwell, Managing Partner/Founder OpenView Venture Partners |
| freelance data science work: Data Science Bookcamp Leonard Apeltsin, 2021-12-07 Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution |
| freelance data science work: 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. |
| freelance data science work: Data Science for Web3 Gabriela Castillo Areco, 2023-12-29 Be part of the future of Web3, decoding blockchain data to build trust in the next-generation internet Key Features Build a deep understanding of the fundamentals of blockchain analytics Extract actionable business insights by modeling blockchain data Showcase your work and gain valuable experience to seize opportunities in the Web3 ecosystem Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3. You’ll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You’ll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data. The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you’ll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients. By the end of this book, you’ll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.What you will learn Understand the core components of blockchain transactions and blocks Identify reliable sources of on-chain and off-chain data to build robust datasets Understand key Web3 business questions and how data science can offer solutions Build your skills to create and query NFT- and DeFi-specific datasets Implement a machine learning toolbox with real-world use cases in the Web3 space Who this book is for This book is designed for data professionals—data analysts, data scientists, or data engineers— and business professionals, aiming to acquire the skills for extracting data from the Web3 ecosystem, as it demonstrates how to effectively leverage data tools for in-depth analysis of blockchain transactional data. If you seek hands-on experience, you'll find value in the shared repository, enabling you to experiment with the provided solutions. While not mandatory, a basic understanding of statistics, machine learning, and Python will enhance your learning experience. |
| freelance data science work: Analyzing the Analyzers Harlan Harris, Sean Murphy, Marck Vaisman, 2013-06-10 Despite the excitement around data science, big data, and analytics, the ambiguity of these terms has led to poor communication between data scientists and organizations seeking their help. In this report, authors Harlan Harris, Sean Murphy, and Marck Vaisman examine their survey of several hundred data science practitioners in mid-2012, when they asked respondents how they viewed their skills, careers, and experiences with prospective employers. The results are striking. Based on the survey data, the authors found that data scientists today can be clustered into four subgroups, each with a different mix of skillsets. Their purpose is to identify a new, more precise vocabulary for data science roles, teams, and career paths. This report describes: Four data scientist clusters: Data Businesspeople, Data Creatives, Data Developers, and Data Researchers Cases in miscommunication between data scientists and organizations looking to hire Why T-shaped data scientists have an advantage in breadth and depth of skills How organizations can apply the survey results to identify, train, integrate, team up, and promote data scientists |
| freelance data science work: Developing Analytic Talent Vincent Granville, 2014-03-24 Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. |
| freelance data science work: Open Talent John Winsor, Jin H. Paik, 2024-01-16 In the new world of hybrid work and AI, one thing is clear: the war for talent is over—and talent won. With sparsely populated offices and people working from wherever they are, and with AI emerging everywhere in business and dominating headlines, our work lives have undergone a remarkable transformation, seemingly overnight. But the reality is that for years the ever-growing digital wave has been breaking down organizational boundaries and increasing the adoption of open innovation, including the use of crowdsourcing platforms as a talent solution. Now the imperative is clear: adapt to and leverage this new, digitally enabled world of open talent—or get left behind. In this eye-opening, essential guidebook, John Winsor and Jin Paik, with their work at the Laboratory for Innovation Science at Harvard, show how the massive reset of the pandemic allowed talented workers everywhere to exit their jobs without leaving the workforce. Now many are freelancing for multiple companies or are starting small businesses, challenging hiring managers as never before amidst a transformed workforce. What's more, talent has more power than ever using platforms such as Freelancer.com, Fiverr, and Upwork, setting their own terms for work: what, where, when, and at what price. How can companies adapt? The key, the authors argue, is shifting to a more distributed idea and structure of collaborative work. The authors call this a networked organization, where talent is culled from both inside and outside the organization and viewed through a single lens—as a global ecosystem that can be tapped as needed. With rich stories, keen insights, and an abundance of practical advice, Winsor and Paik provide a new framework and operating model for transforming your organization into a talent-orchestrating, problem-solving machine. |
| freelance data science work: The Year Without Pants Scott Berkun, 2013-09-10 A behind-the-scenes look at the firm behind WordPress.com and the unique work culture that contributes to its phenomenal success 50 million websites, or twenty percent of the entire web, use WordPress software. The force behind WordPress.com is a convention-defying company called Automattic, Inc., whose 120 employees work from anywhere in the world they wish, barely use email, and launch improvements to their products dozens of times a day. With a fraction of the resources of Google, Amazon, or Facebook, they have a similar impact on the future of the Internet. How is this possible? What's different about how they work, and what can other companies learn from their methods? To find out, former Microsoft veteran Scott Berkun worked as a manager at WordPress.com, leading a team of young programmers developing new ideas. The Year Without Pants shares the secrets of WordPress.com's phenomenal success from the inside. Berkun's story reveals insights on creativity, productivity, and leadership from the kind of workplace that might be in everyone's future. Offers a fast-paced and entertaining insider's account of how an amazing, powerful organization achieves impressive results Includes vital lessons about work culture and managing creativity Written by author and popular blogger Scott Berkun (scottberkun.com) The Year Without Pants shares what every organization can learn from the world-changing ideas for the future of work at the heart of Automattic's success. |
| freelance data science work: Learn Python 3 the Hard Way Zed A. Shaw, 2017-06-26 You Will Learn Python 3! Zed Shaw has perfected the world’s best system for learning Python 3. Follow it and you will succeed—just like the millions of beginners Zed has taught to date! You bring the discipline, commitment, and persistence; the author supplies everything else. In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Variables Strings and text Interact with users Work with files Looping and logic Data structures using lists and dictionaries Program design Object-oriented programming Inheritance and composition Modules, classes, and objects Python packaging Automated testing Basic game development Basic web development It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer. This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven’t written code in years Seasoned professionals looking for a fast, simple, crash course in Python 3 |
如何开始 Freelancer 生涯？ - 知乎
下面是13个寻找和发布自由职业（包括兼职和全职）的freelance网站。 其中，国 …
upwork怎样入门呢太难了这玩意。? - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月 …
freelanceforum.org
freelanceforum.org
如何开始 Freelancer 生涯？ - 知乎
下面是13个寻找和发布自由职业（包括兼职和全职）的freelance网站。 其中，国外网站的比例明显超过国内。 这一是说明国外对远程式工作方式的接受度远远大于我国；另一个 …
upwork怎样入门呢太难了这玩意。? - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭 …
freelanceforum.org
freelanceforum.org
