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| data science resume writer: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data science resume writer: The Resume Writing Guide Lisa McGrimmon, 2014-11-03 Write a Winning Resume - Step-by-StepThe Resume Writing Guide, (updated for 2015) will show you, step-by-step, exactly how to create a resume that makes a great impression and gives you more power in the job search process. It breaks the big, complex job of resume writing into small, completely manageable tasks, so you easily can write a resume that is tailored to show you at your best.Learn From the Experience of 2000 Job SeekersIn this book, you'll find the kind of expertise that only comes from real experience.Lisa McGrimmon has written over 1000 resumes and helped over 2000 job seekers find work. She knows how to coach people through the process to create real resumes that give real results for real people.Guidance Through the ProcessThe Resume Writing Guide takes you through the actual process of creating a resume one step at a time, carefully guiding you through each decision. It isn't just a collection of rules and tips.The process is broken into small, simple steps. In each chapter, you will complete a few tasks, and, if you complete all of the tasks to the best of your ability, once you reach the end of the book, you will have written a resume that shows you in your best possible light.Learn How to Deal With Resume ChallengesLisa has used this process successfully with clients from all walks of life and with all kinds of work histories. She has written resumes for: career changers people returning to work after several years without paid employment new immigrants recent graduates older workers people in competitive and declining industries This book isn't just for the lucky few who have a perfect career history. It gets into the heart of how to write resumes that pop even if you're worried that you may have some challenges to overcome.New for 2015The second edition has been revised and expanded to include up-to-date information about trends in resume writing and job hunting including 2 new chapters about writing applicant tracking system (ATS) optimized resumes.Not a 20 Minute Resume Guide!This book is not for people who want to knock out a resume in 20 minutes. It is a meticulous, step-by-step guide through each phase in the resume writing process.It shows you how to structure each section, write compelling statements, and make smart decisions about content to highlight your individual strengths and minimize weaknesses.Thoughtfully completing the tasks in this book will take time, but it will be time well spent. In the end, you will have a winning resume that is perfect for your needs and structured to show you at your best.Partners in Resume WritingGood career coaches will not write a resume for you; they will write one with you, as partners. You and the career coach both bring important information to the table.Throughout this book, you and Lisa will be partners. Lisa will be the expert on how to create a resume. You will be the expert on your own career.Lisa will show you: how to decide what information you should include, how to write compelling content, how to structure sections to highlight your strengths, and how to minimize concerns you may have about your work history.Your task is to take that information and apply it to what you know about your own career.In this partnership, you can create an effective resume that gets you one big step closer to landing the job you want.Scroll up and click to buy! |
| data science resume writer: 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. |
| data science resume writer: 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 resume writer: The Google Resume Gayle Laakmann McDowell, 2011-01-25 The Google Resume is the only book available on how to win a coveted spot at Google, Microsoft, Apple, or other top tech firms. Gayle Laakmann McDowell worked in Google Engineering for three years, where she served on the hiring committee and interviewed over 120 candidates. She interned for Microsoft and Apple, and interviewed with and received offers from ten tech firms. If you’re a student, you’ll learn what to study and how to prepare while in school, as well as what career paths to consider. If you’re a job seeker, you’ll get an edge on your competition by learning about hiring procedures and making yourself stand out from other candidates. Covers key concerns like what to major in, which extra-curriculars and other experiences look good, how to apply, how to design and tailor your resume, how to prepare for and excel in the interview, and much more Author was on Google’s hiring committee; interned at Microsoft and Apple; has received job offers from more than 10 tech firms; and runs CareerCup.com, a site devoted to tech jobs Get the only comprehensive guide to working at some of America’s most dynamic, innovative, and well-paying tech companies with The Google Resume. |
| data science resume writer: Heard in Data Science Interviews Kal Mishra, 2018-10-03 A collection of over 650 actual Data Scientist/Machine Learning Engineer job interview questions along with their full answers, references, and useful tips |
| data science resume writer: 97 Things Every Data Engineer Should Know Tobias Macey, 2021-06-11 Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail |
| data science resume writer: The Damn Good Resume Guide Yana Parker, 1983 Yana Parker has helped hundreds of thousands of job seekers write and refine their resumes to damn near perfection. Her resume guides have been praised for their user-friendly style and savvy advice and, rightly so, have become staples in libraries, career centers, and employment offices nationwide. Now, in this fully revised and updated edition of the best-seller, you can quickly garner resume-writing wisdom by following 10 easy steps to a damn good resume. Also included are completely new sections on formatting resumes and submitting resumes over the Internet. Here is a resume guide you can count on to help you get that resume done fast and get it done right. |
| data science resume writer: Resume Writing Tips and Strategies for All , 2024-09-18 Today's job market is increasingly competitive, and sending out an unpolished resume can be risky. Without the right strategies, you might end up blending into the crowd or, worse, missing out on the opportunity altogether. Resume Writing: Tips and Strategies for All is your essential guide to mastering the art of resume writing, no matter your experience level or industry. This book delivers actionable insights in a clear, concise format, designed to help you create a resume that stands out from the crowd—quickly and effectively. Inside this smartphone-friendly eBook, you'll discover 69 proven tips and strategies, including: - Step-by-step guidance on writing a professional summary that grabs attention. - How to tailor your resume to any job description for maximum impact. - The best ways to quantify your achievements to showcase your value. - Expert advice on overcoming employment gaps, frequent job changes, and other challenges. - Resume templates and real-life examples to help you craft your own. - Insider secrets on optimizing your resume for Applicant Tracking Systems (ATS) to get past the initial screening. Don’t risk sending out a lackluster resume that gets lost in the pile. If you're serious about landing the job, this book will help you avoid the pitfalls that hold candidates back and show you how to present your best self on paper. Stop wondering, stop procrastinating, and start learning. This book like the other books in the Life Hacks Books collection gives you the tools to grow, take action, and get ahead. Whether you're applying for your first job, making a career change, or advancing in your field, this guide will empower you to create a powerful resume in less than 30 minutes. Make today the day you invest in your future—let your resume work for you! |
| data science resume writer: Modernize Your Resume Wendy Enelow, Louise Kursmark, 2019-01-15 Based on today's real-world job search trends, MODERNIZE YOUR RESUME shows you how to craft a winning resume to meet the complexities of today's highly competitive and technologically driven employment market. The 2nd edition has been updated with new resume samples, new designs, and new ATS and e-resume guidelines, along with new chapters for jobseekers with special circumstances - career change, military transition, and return to work. The entire job search process is evolving at a remarkably rapid pace, and your resume is at the foundation of it all. In this book, you'll learn to: Write tight, lean, clean, and laser-focused content that allows you to tell your whole career story in half the words to keep your reader engaged. Focus on information that is relevant to your target market and integrate keywords that are vital to being found online. Present important information quickly in a resume format that prominently displays your skills, experience, education, and other credentials as they support your current career objectives. Create a distinctive design to make your resume stand out and capture attention. Getting noticed is step #1, so make that happen and you're on your way. Adopt a new mindset and deeper understanding of how to use today's modern resume for both person-to-person job search as well as electronic, digital, and mobile search technologies. The 100+ resume samples demonstrate these strategies in action for real-life job seekers who've excelled in their search campaigns. The samples showcase the resume writing work of Enelow, Kursmark, and many of their colleagues who are also well respected for their expertise in resume strategy, writing, format, and design. Clear guidelines and easy-to-follow examples give you practical know-how for building your own powerful resume that will serve all of your job search needs. You'll learn what works, why it works, and how you can make it work for you. Authors and Master Resume Writers Wendy Enelow and Louise Kursmark have each spent more than 30 years writing resumes, training professional resume writers, speaking to job search audiences, and writing about evolving trends in job search. The latest in their library of more than 30 books is MODERNIZE YOUR RESUME, an up-to-the minute resource for every job seeker to guide you to career success. Follow the recommendations of this book and you'll find yourself a more competent and competitive job seeker! |
| data science resume writer: Cover Letter Magic Wendy S. Enelow, Louise Kursmark, 2004 Professional resume and cover letter writers reveal their inside secrets for creating phenomenal cover letters that get attention and land interviews. Features more than 150 sample cover letters written for all types of job seekers, including the Before-and-After transformations that can make boring letters fabulous. |
| data science resume writer: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data science resume writer: Data Science in Production Ben Weber, 2020 Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and deploy scalable model pipelines, data scientists can own more of the model production process and more rapidly deliver data products. This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production. From startups to trillion dollar companies, data science is playing an important role in helping organizations maximize the value of their data. This book helps data scientists to level up their careers by taking ownership of data products with applied examples that demonstrate how to: Translate models developed on a laptop to scalable deployments in the cloud Develop end-to-end systems that automate data science workflows Own a data product from conception to production The accompanying Jupyter notebooks provide examples of scalable pipelines across multiple cloud environments, tools, and libraries (github.com/bgweber/DS_Production). Book Contents Here are the topics covered by Data Science in Production: Chapter 1: Introduction - This chapter will motivate the use of Python and discuss the discipline of applied data science, present the data sets, models, and cloud environments used throughout the book, and provide an overview of automated feature engineering. Chapter 2: Models as Web Endpoints - This chapter shows how to use web endpoints for consuming data and hosting machine learning models as endpoints using the Flask and Gunicorn libraries. We'll start with scikit-learn models and also set up a deep learning endpoint with Keras. Chapter 3: Models as Serverless Functions - This chapter will build upon the previous chapter and show how to set up model endpoints as serverless functions using AWS Lambda and GCP Cloud Functions. Chapter 4: Containers for Reproducible Models - This chapter will show how to use containers for deploying models with Docker. We'll also explore scaling up with ECS and Kubernetes, and building web applications with Plotly Dash. Chapter 5: Workflow Tools for Model Pipelines - This chapter focuses on scheduling automated workflows using Apache Airflow. We'll set up a model that pulls data from BigQuery, applies a model, and saves the results. Chapter 6: PySpark for Batch Modeling - This chapter will introduce readers to PySpark using the community edition of Databricks. We'll build a batch model pipeline that pulls data from a data lake, generates features, applies a model, and stores the results to a No SQL database. Chapter 7: Cloud Dataflow for Batch Modeling - This chapter will introduce the core components of Cloud Dataflow and implement a batch model pipeline for reading data from BigQuery, applying an ML model, and saving the results to Cloud Datastore. Chapter 8: Streaming Model Workflows - This chapter will introduce readers to Kafka and PubSub for streaming messages in a cloud environment. After working through this material, readers will learn how to use these message brokers to create streaming model pipelines with PySpark and Dataflow that provide near real-time predictions. Excerpts of these chapters are available on Medium (@bgweber), and a book sample is available on Leanpub. |
| data science resume writer: Writing for Engineering and Science Students Gerald Rau, 2019-08-01 Writing for Engineering and Science Students is a clear and practical guide for anyone undertaking either academic or technical writing. Drawing on the author’s extensive experience of teaching students from different fields and cultures, and designed to be accessible to both international students and native speakers of English, this book: Employs analyses of hundreds of articles from engineering and science journals to explore all the distinctive characteristics of a research paper, including organization, length and naming of sections, and location and purpose of citations and graphics; Guides the student through university-level writing and beyond, covering lab reports, research proposals, dissertations, poster presentations, industry reports, emails, and job applications; Explains what to consider before and after undertaking academic or technical writing, including focusing on differences between genres in goal, audience, and criteria for acceptance and rewriting; Features tasks, hints, and tips for teachers and students at the end of each chapter, as well as accompanying eResources offering additional exercises and answer keys. With metaphors and anecdotes from the author’s personal experience, as well as quotes from famous writers to make the text engaging and accessible, this book is essential reading for all students of science and engineering who are taking a course in writing or seeking a resource to aid their writing assignments. |
| data science resume writer: Ask a Manager Alison Green, 2018-05-01 From the creator of the popular website Ask a Manager and New York’s work-advice columnist comes a witty, practical guide to 200 difficult professional conversations—featuring all-new advice! There’s a reason Alison Green has been called “the Dear Abby of the work world.” Ten years as a workplace-advice columnist have taught her that people avoid awkward conversations in the office because they simply don’t know what to say. Thankfully, Green does—and in this incredibly helpful book, she tackles the tough discussions you may need to have during your career. You’ll learn what to say when • coworkers push their work on you—then take credit for it • you accidentally trash-talk someone in an email then hit “reply all” • you’re being micromanaged—or not being managed at all • you catch a colleague in a lie • your boss seems unhappy with your work • your cubemate’s loud speakerphone is making you homicidal • you got drunk at the holiday party Praise for Ask a Manager “A must-read for anyone who works . . . [Alison Green’s] advice boils down to the idea that you should be professional (even when others are not) and that communicating in a straightforward manner with candor and kindness will get you far, no matter where you work.”—Booklist (starred review) “The author’s friendly, warm, no-nonsense writing is a pleasure to read, and her advice can be widely applied to relationships in all areas of readers’ lives. Ideal for anyone new to the job market or new to management, or anyone hoping to improve their work experience.”—Library Journal (starred review) “I am a huge fan of Alison Green’s Ask a Manager column. This book is even better. It teaches us how to deal with many of the most vexing big and little problems in our workplaces—and to do so with grace, confidence, and a sense of humor.”—Robert Sutton, Stanford professor and author of The No Asshole Rule and The Asshole Survival Guide “Ask a Manager is the ultimate playbook for navigating the traditional workforce in a diplomatic but firm way.”—Erin Lowry, author of Broke Millennial: Stop Scraping By and Get Your Financial Life Together |
| data science resume writer: Deep Learning Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016-11-10 An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. |
| data science resume writer: Job Coach-Life Coach-Executive Coach-Branding-Letter & Resume-Writing Service Anne Hart, 2005-10-07 Here's how to start your personal service business. Develop an icon, logo, and motto for your coaching clients. As a job or career coach, an executive coach, or a life coach, you will be presenting and classifying your client's competencies, writing resumes, cover letters, and creating a wide variety of business correspondence including sales letters, news releases, and direct mail copy. You will be planning events for your clients and their prospective employers. You'll need to really work a room to find clients as well as niches or jobs for clients when networking at professional associations and trade shows. Most frequently, you'll be asked to write, evaluate, and repackage resumes, cover letters, and other summaries of qualifications of your clients. A resume is a summary of qualifications. A career coach helps clients find success by taking step-by-step detailed, concrete strategies that solve specific problems, get results, and reach a defined goal. A resume writing business online can be combined with a career coaching enterprise. The steps are outlined here for you to follow in chronological order to open and operate a resume-writing service business and also a career coaching enterprise, online from your home, mobile location, or office. You can telecommute online and still help people find direction by offering information, training, or consulting services. Here's how to open an online business at home presenting and packaging your clients' competencies. Make your living writing resumes, business letters, and being a job coach. Help clients obtain appoints for interviews that may eventually lead to finding work. Write and repackage resumes and all types of business correspondence-from cover letters and follow-ups to direct mail or trade show sales letters. |
| data science resume writer: Cloud Computing and Services Science Ivan Ivanov, Marten Sinderen, Frank Leymann, Tony Shan, 2013-12-20 This book constitutes the thoroughly refereed proceedings of the Second International Conference on Cloud Computing and Services Science, CLOSER 2012, held in Porto, Portugal, in April 2012. The 15 papers were selected from 145 submissions and are presented together with one invited paper. The papers cover the following topics: cloud computing fundamentals, services science foundation for cloud computing, cloud computing platforms and applications, and cloud computing enabling technology. |
| data science resume writer: Get the Job Pamela Paterson, 2013-03-11 Provides techniques to transform a resume into an effective marketing tool that can outpace the competition in online job systems and spike the interest of hiring managers. -- P. [4] of cover. |
| data science resume writer: Résumé Writing Burdette E. Bostwick, 1980 How to look for a job is as much of a discipline as doing the job itself. This guide gives step-by-step techniques for writing an effective resume and shows how to use it to find the right job. This Fourth Edition reflects today's changing job market and covers the new directories designed to aid job-seekers. Also included are tips on interviewing and writing applications, advice on career changes, and a system for keeping track of the scores of replies received during the course of a job solicitation campaign. Copyright © Libri GmbH. All rights reserved. |
| data science resume writer: The Manager's Path Camille Fournier, 2017-03-13 Managing people is difficult wherever you work. But in the tech industry, where management is also a technical discipline, the learning curve can be brutal—especially when there are few tools, texts, and frameworks to help you. In this practical guide, author Camille Fournier (tech lead turned CTO) takes you through each stage in the journey from engineer to technical manager. From mentoring interns to working with senior staff, you’ll get actionable advice for approaching various obstacles in your path. This book is ideal whether you’re a new manager, a mentor, or a more experienced leader looking for fresh advice. Pick up this book and learn how to become a better manager and leader in your organization. Begin by exploring what you expect from a manager Understand what it takes to be a good mentor, and a good tech lead Learn how to manage individual members while remaining focused on the entire team Understand how to manage yourself and avoid common pitfalls that challenge many leaders Manage multiple teams and learn how to manage managers Learn how to build and bootstrap a unifying culture in teams |
| data science resume writer: The Perfect Resume Dan Quillen, 2016-10-04 HR Director and author of four job search books Dan Quillen shows readers how to craft an incredible resume that will get job-seekers in the door! Dan Quillen calls on his expertise as an HR professional (and as one who lost and found a job in the toughest economic environment since the Great Depression) to help his readers understand the New Economy, and how to find work when many cannot. The Perfect Resume second edition focuses on that most important tool in the job hunter's quiver -- the resume. Quillen walks his readers through the technical aspects of resume writing, but also helps them understand the tricks of the resume-writing trade. He teaches readers how to write resumes that end up in the hands of hiring managers, not screened by gatekeepers and deposited in the recycle bin. Topics covered: • The current economic environment • You lost your job -- what do you do now?! • The importance of a resume • The mechanics of resume writing • Three types of resumes and their uses (Chronological, Functional, Combination) • One size doesn't fit all (Targeting your resume) • Your resume template • How to handle resume difficulties (Gaps in employment, Short-term employment, Lack of experience) • Section-by-section resume reviews • New Appendix shows 10 different resume samples |
| data science resume writer: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data science resume writer: Modern Fortran Milan Curcic, 2020-10-07 Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you’ll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. Summary Using Fortran, early and accurate forecasts for hurricanes and other major storms have saved thousands of lives. Better designs for ships, planes, and automobiles have made travel safer, more efficient, and less expensive than ever before. Using Fortran, low-level machine learning and deep learning libraries provide incredibly easy, fast, and insightful analysis of massive data. Fortran is an amazingly powerful and flexible programming language that forms the foundation of high performance computing for research, science, and industry. And it's come a long, long way since starting life on IBM mainframes in 1956. Modern Fortran is natively parallel, so it's uniquely suited for efficiently handling problems like complex simulations, long-range predictions, and ultra-precise designs. If you're working on tasks where speed, accuracy, and efficiency matter, it's time to discover—or re-discover—Fortran.. About the technology For over 60 years Fortran has been powering mission-critical scientific applications, and it isn't slowing down yet! Rock-solid reliability and new support for parallel programming make Fortran an essential language for next-generation high-performance computing. Simply put, the future is in parallel, and Fortran is already there. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you'll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. What's inside Fortran's place in the modern world Working with variables, arrays, and functions Module development Parallelism with coarrays, teams, and events Interoperating Fortran with C About the reader For developers and computational scientists. No experience with Fortran required. About the author Milan Curcic is a meteorologist, oceanographer, and author of several general-purpose Fortran libraries and applications. Table of Contents PART 1 - GETTING STARTED WITH MODERN FORTRAN 1 Introducing Fortran 2 Getting started: Minimal working app PART 2 - CORE ELEMENTS OF FORTRAN 3 Writing reusable code with functions and subroutines 4 Organizing your Fortran code using modules 5 Analyzing time series data with arrays 6 Reading, writing, and formatting your data PART 3 - ADVANCED FORTRAN USE 7 Going parallel with Fortan coarrays 8 Working with abstract data using derived types 9 Generic procedures and operators for any data type 10 User-defined operators for derived types PART 4 - THE FINAL STRETCH 11 Interoperability with C: Exposing your app to the web 12 Advanced parallelism with teams, events, and collectives |
| data science resume writer: The Art and Science of Effective and Impactful Business Communication for Managers Karminder Ghuman, 2024-09-16 Though we all communicate, yet effective communication is not an innate skill for many people. It has to be learned and practiced. This book has been designed to meet postgraduate management students' requirements and equip them with the skills needed for effective workplace communication, emphasizing strategies for business interactions. It shall impart learning on core principles of business communication and shall provide practical guidelines regarding how to communicate effectively and impactfully in the complex and nuanced corporate world.The book shall provide an in-depth understanding of communication practices prevalent in business organisations with the aim of preparing students for their future roles in the corporate world. Every chapter has been designed in a manner to provide a tool, strategy, or approach that can further enhance the effectiveness of the communication of readers for contributing towards their success while working at a business organisation. It also covers the new-age digital communication competencies employees need in today's highly dynamic and hybrid working environment. |
| data science resume writer: Become an Effective Software Engineering Manager James Stanier, 2020-06-09 Software startups make global headlines every day. As technology companies succeed and grow, so do their engineering departments. In your career, you'll may suddenly get the opportunity to lead teams: to become a manager. But this is often uncharted territory. How can you decide whether this career move is right for you? And if you do, what do you need to learn to succeed? Where do you start? How do you know that you're doing it right? What does it even mean? And isn't management a dirty word? This book will share the secrets you need to know to manage engineers successfully. Going from engineer to manager doesn't have to be intimidating. Engineers can be managers, and fantastic ones at that. Cast aside the rhetoric and focus on practical, hands-on techniques and tools. You'll become an effective and supportive team leader that your staff will look up to. Start with your transition to being a manager and see how that compares to being an engineer. Learn how to better organize information, feel productive, and delegate, but not micromanage. Discover how to manage your own boss, hire and fire, do performance and salary reviews, and build a great team. You'll also learn the psychology: how to ship while keeping staff happy, coach and mentor, deal with deadline pressure, handle sensitive information, and navigate workplace politics. Consider your whole department. How can you work with other teams to ensure best practice? How do you help form guilds and committees and communicate effectively? How can you create career tracks for individual contributors and managers? How can you support flexible and remote working? How can you improve diversity in the industry through your own actions? This book will show you how. Great managers can make the world a better place. Join us. |
| data science resume writer: Handbook of Writing Effective Resume for Job Applications Parul Singh, 2009 This book helps you in getting the right job by putting your right resume in the right format. It offers you the tools you need to get your dream job, irrespective of the stage you have reached in your career. The goal of this book is to optimize your marketability by offering you a customized, individualized and targeted resume. The step by step mechanism of writing resume is illustrated with examples in the Indian context. |
| data science resume writer: Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science Raj, Pethuru, Raman, Anupama, 2018-05-18 Fog computing is quickly increasing its applications and uses to the next level. As it continues to grow, different types of virtualization technologies can thrust this branch of computing further into mainstream use. The Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science is a key reference volume on the latest research on the role of next-generation systems and devices that are capable of self-learning and how those devices will impact society. Featuring wide-ranging coverage across a variety of relevant views and themes such as cognitive analytics, data mining algorithms, and the internet of things, this publication is ideally designed for programmers, IT professionals, students, researchers, and engineers looking for innovative research on software-defined cloud infrastructures and domain-specific analytics. |
| data science resume writer: The Professor Is In Karen Kelsky, 2015-08-04 The definitive career guide for grad students, adjuncts, post-docs and anyone else eager to get tenure or turn their Ph.D. into their ideal job Each year tens of thousands of students will, after years of hard work and enormous amounts of money, earn their Ph.D. And each year only a small percentage of them will land a job that justifies and rewards their investment. For every comfortably tenured professor or well-paid former academic, there are countless underpaid and overworked adjuncts, and many more who simply give up in frustration. Those who do make it share an important asset that separates them from the pack: they have a plan. They understand exactly what they need to do to set themselves up for success. They know what really moves the needle in academic job searches, how to avoid the all-too-common mistakes that sink so many of their peers, and how to decide when to point their Ph.D. toward other, non-academic options. Karen Kelsky has made it her mission to help readers join the select few who get the most out of their Ph.D. As a former tenured professor and department head who oversaw numerous academic job searches, she knows from experience exactly what gets an academic applicant a job. And as the creator of the popular and widely respected advice site The Professor is In, she has helped countless Ph.D.’s turn themselves into stronger applicants and land their dream careers. Now, for the first time ever, Karen has poured all her best advice into a single handy guide that addresses the most important issues facing any Ph.D., including: -When, where, and what to publish -Writing a foolproof grant application -Cultivating references and crafting the perfect CV -Acing the job talk and campus interview -Avoiding the adjunct trap -Making the leap to nonacademic work, when the time is right The Professor Is In addresses all of these issues, and many more. |
| data science resume writer: Resume Writing Made Easy for High Tech Lola M. Coxford, Lola Brown, 1987 |
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| data science resume writer: The Art of Data Science Roger D. Peng, Elizabeth Matsui, 2016-06-08 This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.--Leanpub.com. |
| data science resume writer: 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. |
| data science resume writer: Suggestions to Medical Authors and A.M.A. Style Book American Medical Association, 1919 |
| data science resume writer: Purely Functional Data Structures Chris Okasaki, 1999-06-13 This book describes data structures and data structure design techniques for functional languages. |
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| data science resume writer: Learning How to Learn Barbara Oakley, PhD, Terrence Sejnowski, PhD, Alistair McConville, 2018-08-07 A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course Learning How to Learn have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid rut think in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun. |
| data science resume writer: Knock 'em Dead Resumes Martin Yate, 2016-11-04 Expert advice on building a resume to get the job you want! Your resume is the most important financial document you'll ever create. When it works, so do you. Drawing on more than thirty years of experience, bestselling author Martin Yate shows you how to write a dynamic, effective resume that: Stands out in a resume database Builds a strong personal brand employers will want on their team Passes recruiters' six-second scan test Speaks to exactly what employers are looking for The new edition of this classic guide includes dozens of sample resumes as well as cutting-edge advice on resume-writing tactics. With Knock 'em Dead Resumes, 12th Edition, you'll grab employers' attention--and score the job you want. |
| data science resume writer: Advanced Abstract Algebra , |
| data science resume writer: The Damn Good Resume Guide, Fifth Edition Yana Parker, Beth Brown, 2012-05-22 One of the best-selling resume books of all time and a trusted resource for job-seekers for nearly three decades, this edition of The Damn Good Resume Guide has been completely revised and updated for today’s marketplace. The Shortest Distance Between You and Your Next Job For hundreds of thousands of job seekers, The Damn Good Resume Guide has been the go-to resource for writing and refining their resumes to damn near perfection. Filled with savvy advice and written in a straightforward, user-friendly style, The Damn Good Resume Guide will help you zero in on that dream job, then craft a winning resume that gets your foot in the door. This tried-and-true best seller has been fully revised and updated for today’s job market, including: Contemporary sample resumes (all of which landed interviews!) with job objectives running the career gamut—from line cook to sales manager, school principal to software engineer. Tips on creating a functional, chronological, or hybrid resume—and advice on choosing which format is best for you. What to include and what to leave out of your resume, so you get the job you really want. Smart ways to deal with gaps in your work history and other less-than-ideal resume scenarios. Instructions for writing cover emails and submitting resumes electronically. How to set up (and excel at) an informational interview. Advice for formatting, polishing, and proofing your resume so that it stands out in the right way. And much more! Follow Parker and Brown’s ten easy steps, and you’ll be well on your way to a smart, effective, and thoroughly modern resume—a resume that makes you look good and produces results. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
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, …
