| data science resume template: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| data science resume template: 50 Successful Harvard Medical School Essays Staff of the Harvard Crimson, 2020-05-05 Fifty all-new essays that got their authors into Harvard Medical School, including MCAT scores, showing what worked, what didn’t, and how you can do it too. Competition to get into the nation’s top medical schools has never been more intense. Harvard Medical School in particular draws thousands of elite applicants from around the world. As admissions departments become increasingly selective, even the best and brightest need an edge. Writing a personal statement is a daunting part of the application process. In less than 5,300 characters, applicants must weave together experiences and passions into a memorable narrative to set them apart from thousands of other applicants. While there is no magic formula for writing the perfect essay, picking up this book will put them on the right track. 50 Successful Harvard Medical School Essays is the first in a new line of books published by the Staff of the Harvard Crimson. It includes fifty standout essays from students who successfully secured a spot at Harvard Medical School. Each student has a unique set of experiences that led them to medicine. Each essay includes analysis by Crimson editors on essay qualities and techniques that worked, so readers can apply them to their own writing. This book will aid applicants in composing essays that reveal their passion for medicine and the discipline they will bring to this demanding program and profession. It will give them the extra help they need to get into the best medical school programs in the world. |
| data science resume template: 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 template: Applying Data Science and Learning Analytics Throughout a Learner’s Lifespan Trajkovski, Goran, Demeter, Marylee, Hayes, Heather, 2022-05-06 Research in the domains of learning analytics and educational data mining has prototyped an approach where methodologies from data science and machine learning are used to gain insights into the learning process by using large amounts of data. As many training and academic institutions are maturing in their data-driven decision making, useful, scalable, and interesting trends are emerging. Organizations can benefit from sharing information on those efforts. Applying Data Science and Learning Analytics Throughout a Learner’s Lifespan examines novel and emerging applications of data science and sister disciplines for gaining insights from data to inform interventions into learners’ journeys and interactions with academic institutions. Data is collected at various times and places throughout a learner’s lifecycle, and the learners and the institution should benefit from the insights and knowledge gained from this data. Covering topics such as learning analytics dashboards, text network analysis, and employment recruitment, this book is an indispensable resource for educators, computer scientists, faculty of higher education, government officials, educational administration, students of higher education, pre-service teachers, business professionals, researchers, and academicians. |
| data science resume template: 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 template: 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 template: Data Conscience Brandeis Hill Marshall, 2022-08-19 DATA CONSCIENCE ALGORITHMIC S1EGE ON OUR HUM4N1TY EXPLORE HOW D4TA STRUCTURES C4N HELP OR H1NDER SOC1AL EQU1TY Data has enjoyed ‘bystander’ status as we’ve attempted to digitize responsibility and morality in tech. In fact, data’s importance should earn it a spot at the center of our thinking and strategy around building a better, more ethical world. It’s use—and misuse—lies at the heart of many of the racist, gendered, classist, and otherwise oppressive practices of modern tech. In Data Conscience: Algorithmic Siege on our Humanity, computer science and data inclusivity thought leader Dr. Brandeis Hill Marshall delivers a call to action for rebel tech leaders, who acknowledge and are prepared to address the current limitations of software development. In the book, Dr. Brandeis Hill Marshall discusses how the philosophy of “move fast and break things” is, itself, broken, and requires change. You’ll learn about the ways that discrimination rears its ugly head in the digital data space and how to address them with several known algorithms, including social network analysis, and linear regression A can’t-miss resource for junior-level to senior-level software developers who have gotten their hands dirty with at least a handful of significant software development projects, Data Conscience also provides readers with: Discussions of the importance of transparency Explorations of computational thinking in practice Strategies for encouraging accountability in tech Ways to avoid double-edged data visualization Schemes for governing data structures with law and algorithms |
| data science resume template: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
| data science resume template: 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 template: Machine Learning Interviews Susan Shu Chang, 2023-11-29 As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process. Having served as principal data scientist in several companies, Chang has considerable experience as both ML interviewer and interviewee. She'll take you through the highly selective recruitment process by sharing hard-won lessons she learned along the way. You'll quickly understand how to successfully navigate your way through typical ML interviews. This guide shows you how to: Explore various machine learning roles, including ML engineer, applied scientist, data scientist, and other positions Assess your interests and skills before deciding which ML role(s) to pursue Evaluate your current skills and close any gaps that may prevent you from succeeding in the interview process Acquire the skill set necessary for each machine learning role Ace ML interview topics, including coding assessments, statistics and machine learning theory, and behavioral questions Prepare for interviews in statistics and machine learning theory by studying common interview questions |
| data science resume template: Theoretical Statistics Robert W. Keener, 2010-09-08 Intended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential. The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis. The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix. |
| data science resume template: The Advertising Solution Craig Simpson, 2016-10-17 Love it or hate it, advertising remains a key component in acquiring customers and nurturing brand engagement. Distilling the wisdom of the world’s greatest advertisers, direct marketing expert Craig Simpson delivers an education on how to create best-in-class direct marketing and advertising copy that creates brand awareness, sells products, and keeps customers engaged. He takes readers through basic principles and time-tested methods of creating effective ad copy that increases profits. Dissecting the principles of legendary marketers like Robert Collier, Claude Hopkins, John Caples, and David Ogilvy, the reader will find applications to modern digital marketing, direct marketing, and inspiration for headline writing and beyond. |
| data science resume template: Big Data Integration Xin Luna Dong, Divesh Srivastava, 2015-02-01 The big data era is upon us: data are being generated, analyzed, and used at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Since the value of data explodes when it can be linked and fused with other data, addressing the big data integration (BDI) challenge is critical to realizing the promise of big data. BDI differs from traditional data integration along the dimensions of volume, velocity, variety, and veracity. First, not only can data sources contain a huge volume of data, but also the number of data sources is now in the millions. Second, because of the rate at which newly collected data are made available, many of the data sources are very dynamic, and the number of data sources is also rapidly exploding. Third, data sources are extremely heterogeneous in their structure and content, exhibiting considerable variety even for substantially similar entities. Fourth, the data sources are of widely differing qualities, with significant differences in the coverage, accuracy and timeliness of data provided. This book explores the progress that has been made by the data integration community on the topics of schema alignment, record linkage and data fusion in addressing these novel challenges faced by big data integration. Each of these topics is covered in a systematic way: first starting with a quick tour of the topic in the context of traditional data integration, followed by a detailed, example-driven exposition of recent innovative techniques that have been proposed to address the BDI challenges of volume, velocity, variety, and veracity. Finally, it presents merging topics and opportunities that are specific to BDI, identifying promising directions for the data integration community. |
| data science resume template: 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 template: Dear Data Giorgia Lupi, Stefanie Posavec, 2016-09-13 Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates the infinitesimal, incomplete, imperfect, yet exquisitely human details of life, in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere. |
| data science resume template: Design Patterns Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, 1995 Software -- Software Engineering. |
| data science resume template: 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 template: 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 template: Congressional Record United States. Congress, 1968 |
| data science resume template: The Analytics Edge Dimitris Bertsimas, Allison K. O'Hair, William R. Pulleyblank, 2016 Provides a unified, insightful, modern, and entertaining treatment of analytics. The book covers the science of using data to build models, improve decisions, and ultimately add value to institutions and individuals--Back cover. |
| data science resume template: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
| data science resume template: Grease Your Job Finding Skids • Resume (PMT) Dennis Buckmaster, 2014-12-14 No more lame, Sucky, boring, predictable resumes. Gone are inane job title laundry lists, chronological formats, ridiculous job objectives. It is time to become Bold! Important: The wordresume projects images of poor souls going from one office to another, resume in hand, asking pitifully, Ya got any jobs? Can I drop off my resume? Better is the new Personal Marketing Tool (PMT) with connotations of confidence people representing a valuable product, seeking the best match. With all the Johnny-come-latelies career counselor wannabes in the field - Who do you trust? Dennis Buckmaster, with 30+ years, experience guiding 30K+ clients in greasing the skids to their job finding through creative, impactful and readable PMTs. In a balanced plan, grabbing employers' attention, setting the stage for your follow-up, interview and offer. Dennis shares proven step-by-step method and PMT examples to spark your inner creativity. |
| data science resume template: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| data science resume template: CVs, Resumes, and LinkedIn Adrian Wallwork, 2014-06-26 Are you a graduate, postgraduate or PhD student? Are you simply looking for a new job in the private or public sector, in research or industry? If your aim is to produce a professional CV or resume, then this book is for you. Based on interviews with recruiters and HR managers, and an analysis of hundreds of CVs from around 40 different countries, the book is structured as a series of FAQs. Topics covered include: how recruiters and HR people analyse a CV whether using a template is a good idea the difference between a CV and a resume how to present your personal details and whether to include a photo how to write an Executive Summary what to write in each section (Education, Work Experience, Skills, Personal Interests) how to write dates how to highlight your language, communication and team skills how to get and write references You will also learn some hints and strategies for writing a: cover letter LinkedIn profile reference letter bio The last chapter of the book contains a simple template to help you get the job of your dreams! |
| data science resume template: 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 template: The New Rules of Work Alexandra Cavoulacos, Kathryn Minshew, 2017 In this definitive guide to the ever-changing modern workplace, Kathryn Minshew and Alexandra Cavoulacos, the co-founders of popular career website TheMuse.com, show how to play the game by the New Rules. The Muse is known for sharp, relevant, and get-to-the-point advice on how to figure out exactly what your values and your skills are and how they best play out in the marketplace. Now Kathryn and Alex have gathered all of that advice and more in The New Rules of Work. Through quick exercises and structured tips, the authors will guide you as you sort through your countless options; communicate who you are and why you are valuable; and stand out from the crowd. The New Rules of Work shows how to choose a perfect career path, land the best job, and wake up feeling excited to go to work every day-- whether you are starting out in your career, looking to move ahead, navigating a mid-career shift, or anywhere in between-- |
| data science resume template: Suggestions to Medical Authors and A.M.A. Style Book American Medical Association, 1919 |
| data science resume template: Purely Functional Data Structures Chris Okasaki, 1999-06-13 This book describes data structures and data structure design techniques for functional languages. |
| data science resume template: Building Analytics Teams John K. Thompson, Douglas B. Laney, 2020-06-30 Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AI Key FeaturesLearn to create an operationally effective advanced analytics team in a corporate environmentSelect and undertake projects that have a high probability of success and deliver the improved top and bottom-line resultsUnderstand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your teamBook Description In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success. The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs. The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects. By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization. What you will learnAvoid organizational and technological pitfalls of moving from a defined project to a production environmentEnable team members to focus on higher-value work and tasksBuild Advanced Analytics and Artificial Intelligence (AA&AI) functions in an organizationOutsource certain projects to competent and capable third partiesSupport the operational areas that intend to invest in business intelligence, descriptive statistics, and small-scale predictive analyticsAnalyze the operational area, the processes, the data, and the organizational resistanceWho this book is for This book is for senior executives, senior and junior managers, and those who are working as part of a team that is accountable for designing, building, delivering and ensuring business success through advanced analytics and artificial intelligence systems and applications. At least 5 to 10 years of experience in driving your organization to a higher level of efficiency will be helpful. |
| data science resume template: Inventory of Projects , 1986 |
| data science resume template: 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 template: Law and Policy for the Quantum Age Chris Jay Hoofnagle, Simson L. Garfinkel, 2022-01-06 The Quantum Age cuts through the hype to demystify quantum technologies, their development paths, and the policy issues they raise. |
| data science resume template: The Product Manager Interview Lewis C. Lin, 2017-11-06 NOTE: This is the NEWER 3rd edition for the book formerly titled PM Interview Questions. -- 164 Actual PM Interview Questions From the creator of the CIRCLES Method(TM), The Product Manager Interview is a resource you don't want to miss. The world's expert in product management interviews, Lewis C. Lin, gives readers 164 practice questions to gain product management (PM) proficiency and master the PM interview including: Google Facebook Amazon Uber Dropbox Microsoft Fully Solved Solutions The book contains fully solved solutions so readers can learn, improve and do their best at the PM interview. Here are questions and sample answers you'll find in the book: Product Design How would you design an ATM for elderly people? Should Google build a Comcast-like TV cable service? Instagram currently supports 3 to 15 second videos. We're considering supporting videos of unlimited length. How would you modify the UX to accommodate this? Pricing How would you go about pricing UberX or any other new Uber product? Let's say Google created a teleporting device: which market segments would you go after? How would you price it? Metrics Imagine you are the Amazon Web Services (AWS) PM in Sydney. What are the top three metrics you'd look at? Facebook users have declined 20 percent week over week. Diagnose the problem. How would you fix the issue? Ideal Complement to Decode and Conquer Many of you have read the PM interview frameworks revealed in Decode and Conquer, including the CIRCLES(TM), AARM(TM) and DIGS(TM) Methods. The Product Manager Interview is the perfect complement to Decode and Conquer. With over 160 practice questions, you'll see what the best PM interview responses look and feel like. Brand New Third Edition Many of the sample answers have been re-written from scratch. The sample answers are now stronger and easier to follow. In total, thousands of changes have made in this brand new third edition of the book. Preferred by the World's Top Universities Here's what students and staff have to say about the Lewis C. Lin: DUKE UNIVERSITY I was so touched by your presentation this morning. It was really helpful. UNIVERSITY OF MICHIGAN I can say your class is the best that I have ever attended. I will definitely use knowledge I learned today for future interviews. COLUMBIA UNIVERSITY I'd like to let you know that your workshop today is super awesome! It's the best workshop I have been to since I came to Columbia Business School. Thank you very much for the tips, frameworks, and the very clear and well-structured instruction! UNIVERSITY OF TEXAS AT AUSTIN I wanted to reiterate how much I enjoyed your workshops today. Thank you so much for taking time out and teaching us about these much-needed principles and frameworks. I actually plan to print out a few slides and paste them on my walls! CARNEGIE MELLON UNIVERSITY I'm a very big admirer of your work. We, at Tepper, follow your books like the Bible. As a former associate product manager, I was able to connect your concepts back to my work experience back and Pragmatic Marketing training. I'm really looking forward to apply your teachings. |
| data science resume template: Principles Ray Dalio, 2018-08-07 #1 New York Times Bestseller “Significant...The book is both instructive and surprisingly moving.” —The New York Times Ray Dalio, one of the world’s most successful investors and entrepreneurs, shares the unconventional principles that he’s developed, refined, and used over the past forty years to create unique results in both life and business—and which any person or organization can adopt to help achieve their goals. In 1975, Ray Dalio founded an investment firm, Bridgewater Associates, out of his two-bedroom apartment in New York City. Forty years later, Bridgewater has made more money for its clients than any other hedge fund in history and grown into the fifth most important private company in the United States, according to Fortune magazine. Dalio himself has been named to Time magazine’s list of the 100 most influential people in the world. Along the way, Dalio discovered a set of unique principles that have led to Bridgewater’s exceptionally effective culture, which he describes as “an idea meritocracy that strives to achieve meaningful work and meaningful relationships through radical transparency.” It is these principles, and not anything special about Dalio—who grew up an ordinary kid in a middle-class Long Island neighborhood—that he believes are the reason behind his success. In Principles, Dalio shares what he’s learned over the course of his remarkable career. He argues that life, management, economics, and investing can all be systemized into rules and understood like machines. The book’s hundreds of practical lessons, which are built around his cornerstones of “radical truth” and “radical transparency,” include Dalio laying out the most effective ways for individuals and organizations to make decisions, approach challenges, and build strong teams. He also describes the innovative tools the firm uses to bring an idea meritocracy to life, such as creating “baseball cards” for all employees that distill their strengths and weaknesses, and employing computerized decision-making systems to make believability-weighted decisions. While the book brims with novel ideas for organizations and institutions, Principles also offers a clear, straightforward approach to decision-making that Dalio believes anyone can apply, no matter what they’re seeking to achieve. Here, from a man who has been called both “the Steve Jobs of investing” and “the philosopher king of the financial universe” (CIO magazine), is a rare opportunity to gain proven advice unlike anything you’ll find in the conventional business press. |
| data science resume template: Elements of Large-Sample Theory E.L. Lehmann, 2006-04-18 Written by one of the main figures in twentieth century statistics, this book provides a unified treatment of first-order large-sample theory. It discusses a broad range of applications including introductions to density estimation, the bootstrap, and the asymptotics of survey methodology. The book is written at an elementary level making it accessible to most readers. |
| data science resume template: 50 Ways to Get a Job Dev Aujla, 2018-04-03 A new personalized way to find the perfect job—while staying calm during the process. You are so much more than a resume or job application, but how can you communicate that to your potential employer? You need to learn to ask the right questions, stop using job sites, and start doing the work that actually counts. Based on information gained from over 400,000 individuals who have used these exercises, this book reveals career expert Dev Aujla’s tried-and-tested method for job seekers at every stage of their career. Filled with anecdotes and advice from professionals ranging from a wilderness guide to an architect, it includes quick-step exercises that help you avoid the common pitfalls of navigating a modern career. Whether you've just decided to start the hunt or you're gearing up for a big interview, 50 Ways to Get a Job will keep you poised, on-track, and motivated right up to landing your dream career. |
| data science resume template: The Complete Idiot's Guide to the Perfect Resume Susan Ireland, 2010-01 Provides over one hundred samples of successful resumes and cover letters, as well as examples of key words and phrases to get job applications noticed. |
| data science resume template: Cracking the Coding Interview Gayle Laakmann McDowell, 2011 Now in the 5th edition, Cracking the Coding Interview gives you the interview preparation you need to get the top software developer jobs. This book provides: 150 Programming Interview Questions and Solutions: From binary trees to binary search, this list of 150 questions includes the most common and most useful questions in data structures, algorithms, and knowledge based questions. 5 Algorithm Approaches: Stop being blind-sided by tough algorithm questions, and learn these five approaches to tackle the trickiest problems. Behind the Scenes of the interview processes at Google, Amazon, Microsoft, Facebook, Yahoo, and Apple: Learn what really goes on during your interview day and how decisions get made. Ten Mistakes Candidates Make -- And How to Avoid Them: Don't lose your dream job by making these common mistakes. Learn what many candidates do wrong, and how to avoid these issues. Steps to Prepare for Behavioral and Technical Questions: Stop meandering through an endless set of questions, while missing some of the most important preparation techniques. Follow these steps to more thoroughly prepare in less time. |
| data science resume template: 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 template: The Federal Resume Guide The National The National Archives and Records Administration, 2015-02-14 Knowing how to write a winning resume is a valuable skill to have in today's job market. Your resume is the first impression you give to a potential employer - it is your marketing piece. A well-written resume can help you land an interview or may lead to new job opportunities. Whether you are actively seeking a job at the National Archives and Records Administration (NARA), another Federal agency, or just want to update your existing resume, this guide will assist you in making your resume, and thereby you, stand out from the crowd. |
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