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| director of data science salary: Data Science Careers, Training, and Hiring Renata Rawlings-Goss, 2019-08-02 This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build. |
| director of data science salary: Leadership in Statistics and Data Science Amanda L. Golbeck, 2021-03-22 This edited collection brings together voices of the strongest thought leaders on diversity, equity and inclusion in the field of statistics and data science, with the goal of encouraging and steering the profession into the regular practice of inclusive and humanistic leadership. It provides futuristic ideas for promoting opportunities for equitable leadership, as well as tested approaches that have already been found to make a difference. It speaks to the challenges and opportunities of leading successful research collaborations and making strong connections within research teams. Curated with a vision that leadership takes a myriad of forms, and that diversity has many dimensions, this volume examines the nuances of leadership within a workplace environment and promotes storytelling and other competencies as critical elements of effective leadership. It makes the case for inclusive and humanistic leadership in statistics and data science, where there often remains a dearth of women and members of certain racial communities among the employees. Titled and non-titled leaders will benefit from the planning, evaluation, and structural tools offered within to contribute inclusive excellence in workplace climate, environment, and culture. |
| director of data science salary: Big Data Bernard Marr, 2015-01-09 Convert the promise of big data into real world results There is so much buzz around big data. We all need to know what it is and how it works - that much is obvious. But is a basic understanding of the theory enough to hold your own in strategy meetings? Probably. But what will set you apart from the rest is actually knowing how to USE big data to get solid, real-world business results - and putting that in place to improve performance. Big Data will give you a clear understanding, blueprint, and step-by-step approach to building your own big data strategy. This is a well-needed practical introduction to actually putting the topic into practice. Illustrated with numerous real-world examples from a cross section of companies and organisations, Big Data will take you through the five steps of the SMART model: Start with Strategy, Measure Metrics and Data, Apply Analytics, Report Results, Transform. Discusses how companies need to clearly define what it is they need to know Outlines how companies can collect relevant data and measure the metrics that will help them answer their most important business questions Addresses how the results of big data analytics can be visualised and communicated to ensure key decisions-makers understand them Includes many high-profile case studies from the author's work with some of the world's best known brands |
| director of data science salary: Management Today Terri A. Scandura, Kim Gower, 2019-12-04 Today’s ever-evolving workplace requires managers to hone new skills so they can make informed decisions, manage diverse teams, and lead change. Management Today: Best Practices for the Modern Workplace cuts through the noise by introducing students to evidence-based management theories, models, and strategies. Experiential activities, critical thinking questions, and self-assessments provide students with hands-on opportunities to practice essential management skills. Authors Terri A. Scandura and Kim Gower provide best practices and explore timely issues like emotional intelligence, cultural intelligence, and virtual teams. Real-world cases explore good and bad examples of management, including the college admissions scandal, Theranos, and Walmart. In-depth coverage of big data, data analytics, and technology ensures students are ready to thrive in today′s workplace. This title is accompanied by a complete teaching and learning package. |
| director of data science salary: The Budget of the United States Government United States. Bureau of the Budget, 1966 |
| director of data science salary: Data Science Thinking Longbing Cao, 2018-08-17 This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. |
| director of data science salary: Closing the Analytics Talent Gap Jennifer Priestley, Robert McGrath, 2021-05-03 How can we recruit out of your program? We have a project – how do we reach out to your students? If we do research together who owns it? We have employees who need to upskill in analytics – can you help me with that? How much does all of this cost? Managers and executives are increasingly asking university professors such questions as they deal with a critical shortage of skilled data analysts. At the same time, academics are asking such questions as: How can I bring a real analytical project in the classroom? How can I get real data to help my students develop the skills necessary to be a data scientist? Is what I am teaching in the classroom aligned with the demands of the market for analytical talent? After spending several years answering almost daily e-mails and telephone calls from business managers asking for staffing help and aiding fellow academics with their analytics teaching needs, Dr. Jennifer Priestley of Kennesaw State University and Dr. Robert McGrath of the University of New Hampshire wrote Closing the Analytics Talent Gap: An Executive’s Guide to Working with Universities. The book builds a bridge between university analytics programs and business organizations. It promotes a dialog that enables executives to learn how universities can help them find strategically important personnel and universities to learn how they can develop and educate this personnel. Organizations are facing previously unforeseen challenges related to the translation of massive amounts of data – structured and unstructured, static and in-motion, voice, text, and image – into information to solve current challenges and anticipate new ones. The advent of analytics and data science also presents universities with unforeseen challenges of providing learning through application. This book helps both organizations with finding data natives and universities with educating students to develop the facility to work in a multi-faceted and complex data environment. . |
| director of data science salary: Data Science and Machine Learning Interview Questions Using Python Vishwanathan Narayanan, 2020-05-08 ÊKnowÊ Data science with numpy, pandas, scipy, sklearn DESCRIPTION ÒData science and Machine learning interview questions using Python,Ó a book which is a true companion of people aspiring for data science and machine learning, and it provides answers to most asked questions in an easy to remember and presentable form. Book mainly intended to be used as last-minute revision, before the interview, as all the important concepts and various terminologies have been given in a very simple and understandable format. Many examples have been provided so that the same can be used while giving answers in an interview. The book is divided into six chapters, which starts with the Data Science Basic Questions and Terms then covers the questions related to Python Programming, Numpy, Pandas, Scipy, and its Applications, then at the last covers Matplotlib and Statistics with Excel Sheet. Ê KEY FEATURES - Questions related to core/basic Python, Excel, basic and advanced statistics are included - Book will prove to be a companion whenever you want to go for an interview - Simple to use words have been used in the answers for the questions to help ease of remembering Ê WHAT WILL YOU LEARN - You can learn the basic concept and terms related to Data Science, python programming - You will get to learn how to program in python, basics of Numpy - You will get familiarity with the questions asked in an interview related to Pandas and learn the concepts of Scipy, Matplotib, and Statistics with Excel Sheet Ê WHO THIS BOOK IS FOR The book is mainly intended to help people represent their answer in a sensible way to the interviewer. The answers have been carefully rendered in a way to make things quite simple and yet represent the seriousness and complexity of the matter. Since data science is incomplete without mathematics, we have also included a part of the book dedicated to statistics.Ê Ê Table of Contents 1. Data Science Basic Questions and Terms 2. Python Programming Questions 3. Numpy Interview Questions 4. Pandas Interview Questions 5. Scipy and its Applications 6. Matplotlib Samples to Remember 7. Statistics with Excel Sheet |
| director of data science salary: Machine Learning Methods in Systems Radek Silhavy, |
| director of data science salary: Data Science from Scratch Joel Grus, 2019-04-12 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. |
| director of data science salary: Data Science Without Makeup Mikhail Zhilkin, 2021-11-01 Mikhail Zhilkin, a data scientist who has worked on projects ranging from Candy Crush games to Premier League football players’ physical performance, shares his strong views on some of the best and, more importantly, worst practices in data analytics and business intelligence. Why data science is hard, what pitfalls analysts and decision-makers fall into, and what everyone involved can do to give themselves a fighting chance—the book examines these and other questions with the skepticism of someone who has seen the sausage being made. Honest and direct, full of examples from real life, Data Science Without Makeup: A Guidebook for End-Users, Analysts and Managers will be of great interest to people who aspire to work with data, people who already work with data, and people who work with people who work with data—from students to professional researchers and from early-career to seasoned professionals. Mikhail Zhilkin is a data scientist at Arsenal FC. He has previously worked on the popular Candy Crush mobile games and in sports betting. |
| director of data science salary: 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. |
| director of data science salary: Data Science for Business Professionals Probyto Data Science and Consulting Pvt. Ltd., 2020-05-06 Primer into the multidisciplinary world of Data Science KEY FEATURESÊÊ - Explore and use the key concepts of Statistics required to solve data science problems - Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app - Learn how to build Data Science solutions with GCP and AWS DESCRIPTIONÊ The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset.Ê WHAT WILL YOU LEARNÊÊ - Understand the multi-disciplinary nature of Data Science - Get familiar with the key concepts in Mathematics and Statistics - Explore a few key ML algorithms and their use cases - Learn how to implement the basics of Data Pipelines - Get an overview of Cloud Computing & DevOps - Learn how to create visualizations using Tableau WHO THIS BOOK IS FORÊ This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science.Ê TABLE OF CONTENTS 1. Data Science in Practice 2. Mathematics Essentials 3. Statistics Essentials 4. Exploratory Data Analysis 5. Data preprocessing 6. Feature Engineering 7. Machine learning algorithms 8. Productionizing ML models 9. Data Flows in Enterprises 10. Introduction to Databases 11. Introduction to Big Data 12. DevOps for Data Science 13. Introduction to Cloud Computing 14. Deploy Model to Cloud 15. Introduction to Business IntelligenceÊ 16. Data Visualization Tools 17. Industry Use Case 1 Ð FormAssist 18. Industry Use Case 2 Ð PeopleReporter 19. Data Science Learning Resources 20. Do It Your Self Challenges 21. MCQs for Assessments |
| director of data science salary: Getting Started with Data Science Murtaza Haider, 2015-12-14 Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon. |
| director of data science salary: Message of the President of the United States Transmitting the Budget for the Service of the Fiscal Year Ending ... United States, 1960 |
| director of data science salary: Data Science Field Cady, 2020-12-03 Tap into the power of data science with this comprehensive resource for non-technical professionals Data Science: The Executive Summary – A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the “business side” of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies. Data Science: The Executive Summary covers topics like: Assessing whether your organization needs data scientists, and what to look for when hiring them When Big Data is the best approach to use for a project, and when it actually ties analysts’ hands Cutting edge Artificial Intelligence, as well as classical approaches that work better for many problems How many techniques rely on dubious mathematical idealizations, and when you can work around them Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation. |
| director of data science salary: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-06 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| director of data science salary: Data Science in R Deborah Nolan, Duncan Temple Lang, 2015-04-21 Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and ComputationData Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts |
| director of data science salary: Computerworld , 1981-08-31 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
| director of data science salary: Innovations in Classification, Data Science, and Information Systems Daniel Baier, Klaus-Dieter Wernecke, 2006-06-06 The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques. |
| director of data science salary: Computerworld , 1979-12-10 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
| director of data science salary: Data Science and Machine Learning Interview Questions Using R Vishwanathan Narayanan, 2020-06-23 Get answers to frequently asked questions on Data Science and Machine Learning using R KEY FEATURESÊÊ - Understand the capabilities of the R programming language - Most of the machine learning algorithms and their R implementation covered in depth - Answers on conceptual data science concepts are also covered DESCRIPTIONÊÊ This book prepares you for the Data Scientist and Machine Learning Engineer interview w.r.t. R programming language.Ê The book is divided into various parts, making it easy for you to remember and associate with the questions asked in an interview. It covers multiple possible transformations and data filtering techniques in depth. You will be able to create visualizations like graphs and charts using your data. You will also see some examples of how to build complex charts with this data. This book covers the frequently asked interview questions and shares insights on the kind of answers that will help you get this job. By the end of this book, you will not only crack the interview but will also have a solid command of the concepts of Data Science as well as R programming. WHAT WILL YOU LEARNÊ - Get answers to the basics, intermediate and advanced questions on R programming - Understand the transformation and filtering capabilities of R - Know how to perform visualization using R WHO THIS BOOK IS FORÊ This book is a must for anyone interested in Data Science and Machine Learning. Anyone who wants to clear the interview can use it as a last-minute revision guide. TABLE OF CONTENTSÊÊ 1. Data Science basic questions and terms 2. R programming questions 3. GGPLOT Questions 4. Statistics with excel sheet |
| director of data science salary: Network World , 1988-02-15 For more than 20 years, Network World has been the premier provider of information, intelligence and insight for network and IT executives responsible for the digital nervous systems of large organizations. Readers are responsible for designing, implementing and managing the voice, data and video systems their companies use to support everything from business critical applications to employee collaboration and electronic commerce. |
| director of data science salary: Bulletin , 1979 |
| director of data science salary: Data Science Jobs Ann Rajaram, Want a high-paying $$$ career in the exciting field of DataScience? This is the ONLY book that will help you land a lucrative Analytics job in 90 days or less! This book is the perfect guide for you, if you fall into any of these categories: * You recently completed a masters degree (or online course or bootcamp) and want to get hired quickly as a Data Scientist, Data Analyst, Data Engineer, Machine learning engineer or BI developer. * Looking to start a career in data science, but unsure where to start. * You are an experienced tech professional, but looking to pivot into analytics to boost your salary potential. * Tired of applying to dozens of jobs without getting a positive response and/or final job offer . * F1 visa, STEM OPT/ CPT students will also find this book helpful to land a job in this lucrative field. The book will teach you proven successful strategies on: * Winning Profiles Turbocharge your resume and LinkedIn profile and start receiving interview calls from hiring managers. Let JOBS CHASE YOU, instead of the other way around! * LinkedIn - A dedicated chapter on LinkedIn that teaches you some creative (and SECRET) ways to leverage the site and identify high-paying jobs with low competition. * Niche sites - A full list of niche job boards that other candidates have overlooked. These sites have high-$ jobs but lesser competition than the popular job search sites. Upwork - Contrary to popular opinion, Upwork can help you make $$$ in data science jobs. Learn proven techniques to help you bag contracts and start earning, as quickly as next week. * 100+ interview questions asked in real-life data scientist interviews. * Other learner resources and much more... Author is a practicing analytics professional who has worked in Fortune500 Firms like NASDAQ , BlackRock, etc. Unlike most job search books that are written by recruiters or professors, this book is written by a senior professional, who rose quickly from analyst to managerial roles. She has attended interviews of her own, and knows clearly the frustrations (and at times, hopelessness) of the job search process. The systems in this book have successfully helped dozens of job seekers and will work effectively for you too! Read on to launch your dream career! Note, this book is deliberately kept short and precise, so you can quickly read through and start applying these principles, instead of sifting through 500 pages of fluff. This book includes: Data Science interview questions and answers; Help preparing for Machine Learning Interviews; Top 25 Interview Questions for Data Analyst/Scientist roles; An in-depth overview of Data Science Interview Process; How to ace your interview even if you are an Entry level Data Analyst / Data Scientist; Data Science Interview questions for freshers; How and Where to look for jobs; and much more! |
| director of data science salary: ESSENTIAL PYTHON: FROM DATA SCIENCE TO AUTOMATION Marcel Souza, Unlock the power of Python with Essential Python: From Data Science to Automation. Whether you're a beginner or an experienced coder, this comprehensive guide is your gateway to the exciting world of Python. Dive into the world of data science and learn how to manipulate, analyze, and visualize data with Python. Discover the versatility of this language as you explore various libraries and tools essential for data-driven decision-making. Take your coding skills to the next level and embrace the world of automation. With Python, you can automate repetitive tasks, streamline workflows, and boost your productivity like never before. From web development to machine learning, Python is at the heart of cutting-edge technologies. Unravel the mysteries of this versatile language and gain the skills to tackle real-world challenges. With Essential Python: From Data Science to Automation, you'll not only learn the fundamentals of Python but also dive into advanced topics that will make you a proficient Python developer. Don't miss this opportunity to master Python and tap into its immense potential. Get your hands on this book now and embark on a transformative journey in the world of programming. Your future as a skilled Python developer starts here! |
| director of data science salary: Computerworld , 1977-04-04 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
| director of data science salary: Behavioral Competencies of Digital Professionals Sara Bonesso, Elena Bruni, Fabrizio Gerli, 2019-12-18 Shedding new light on the human side of big data through the lenses of emotional and social intelligence competencies, this book advances the understanding of the requirements of the different professions that deal with big data. It also illustrates the empirical evidence collected through the application of the competency-based methodology to a sample of data scientists and data analysts, the two most in-demand big data jobs in the labor market. The book provides recommendations for the higher education system to offer better designed curricula for entry-level big data professions. It also offers managerial insights in describing how organizations and specifically HR practitioners can benefit from the competency-based approach to overcome the skill shortage that characterizes the demand for big data professional roles and to increase the effectiveness of the selection and recruiting processes. |
| director of data science salary: Computer Sciences and Data Systems , 1987 |
| director of data science salary: Computerworld , 1978-02-20 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
| director of data science salary: Report of the President's Biomedical Research Panel United States. President's Biomedical Research Panel, 1976 |
| director of data science salary: Data Science in the Public Interest: Improving Government Performance in the Workforce Joshua D. Hawley, 2020-07-22 This book is about how new and underutilized types of big data sources can inform public policy decisions related to workforce development. Hawley describes how government is currently using data to inform decisions about the workforce at the state and local levels. He then moves beyond standardized performance metrics designed to serve federal agency requirements and discusses how government can improve data gathering and analysis to provide better, up-to-date information for government decision making. |
| director of data science salary: Health Informatics Sixth Edition Supplement: Practical Guide for Healthcare and Information Technology Professionals Ann K. Yoshihashi, Robert E. Hoyt, 2016-11-15 Health Informatics: Practical Guide for Health and Information Technology Professionals Sixth Edition Supplement adds 3 new chapters. The supplement has learning objectives, case studies, recommended reading, future trends, key points, and references. Introduction to Data Science, provides a comprehensive overview with topics including databases, machine learning, big data and predictive analytics. Clinical Decision Support (CDS), covers current and salient aspects of CDS functionality, implementation, benefits, challenges and lessons learned. International Health Informatics, highlights the informatics initiatives of developed and developing countries on each continent. Available as a paperback and eBook. For more information about the textbook, visit www.informaticseducation.org. For instructors, an Instructor Manual, PDF version and PowerPoint slides are available under the Instructor's tab. |
| director of data science salary: Beginning Data Science with Python and Jupyter Alex Galea, 2018-06-05 Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. What you will learn Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests Plan a machine learning classification strategy and train classification, models Use validation curves and dimensionality reduction to tune and enhance your models Discover how you can use web scraping to gather and parse your own bespoke datasets Scrape tabular data from web pages and transform them into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start. |
| director of data science salary: It's All Analytics! Scott Burk, Gary D. Miner, 2020-05-25 It's All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690) Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, analytics, is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and terminology? This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series. |
| director of data science salary: Computerworld , 1985-04-22 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
| director of data science salary: United States Government, Policy and Supporting Position.....1968, Committee Print, 90-2 United States. Congress. House. Post Office and Civil Service, 1968 |
| director of data science salary: Usa University Guide Marlene Bell, 2017-09-01 According to the Institute of International Education, American higher education continues to be highly valued throughout the world. US campuses offer unparalleled opportunities for creativity, flexibility and cultural exchange. Students from all over the world contribute substantially to their host campuses and to the US economy With more than 4,000 colleges and universities, the United States has one of the finest systems of higher education in the world. US higher education is unparalleled in its vitality, quality, and diversity, which help propel its leading role in the fields of business, engineering, science and education. In this book , you will find your perfect program with our university guide – covering general information, entry requirement, top 10 university, specialization, best direction to get a job easily in the future and many more... |
| director of data science salary: Available Pay Survey Reports Steven Langer, 1980 |
| director of data science salary: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
外企中Managing Director和General Manager有什么区别？
外企特别是金融类外企经常有vice president，director, managing director之类，头衔很大，其实很多只是职级而已，大多不是字面表达的意思，更多是类似国内的职称。比如VP其实就是外企 …
名片中职务的对应英文翻译及欧美企业职务含义及级别？ - 知乎
1. 如果怕误解为中干，建议翻为CEO，这个老外一听就了解。至于Managing Director要看总经理是否董事会成员，如果是，CEO和Managing Director取其一（听你们领导的），不要重复用。 …
对董事长、总经理等职务，最准确常用的商务英语翻译是什么？
中小公司： General Manager 或 Managing Director. 合伙人制公司中负责日常运作的那位合伙人： Managing Partner. 副总经理： 看公司大小. 大公司： Executive Vice President， 下面还有 …
外企职位层级的划分？从低到高，求中英文对照? - 知乎
而对大多数在职场打拼了半辈子的senior director来说，他们的职业生涯也就止步于此了。从director 到VP，是只属于少数职场精英们的最后一个升职（升值）瓶颈。 第八级： 副总 …
GM、VP、FVP、CIO都是什么职位？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
导演、监制、制片人、出品人的职责和分工是怎么样的？ - 知乎
本题已加入知乎圆桌 »影视职人说 | 青年导演养成记，更多「电影产业」讨论欢迎关注。
如何知道一个期刊是不是sci？ - 知乎
Master Journal List在这个网站能搜到的就是吗？我在web of knowledge 上能搜到文章的杂志就是sci吗？
执行董事和CEO有什么区别？ - 知乎
Nov 13, 2018 · 我的英国公司职位就是Manaing Director，然后中文名片上写“执行董事”，我承认这个名称一般中国客户都不明白，几乎都认为是董事会的什么角色。但实际上，就是CEO，或者 …
研究员为什么是principal investigator而不是researcher？ - 知乎
美国国家科学基金会(National Science Foundation，简称NSF)定义为”由受让入指定、美国国家自然科学基金委同意的负责项目科学技术方向的个体”，同时指出”这一术语一般用于研究领域”， …
PE、PM、PD、PR分别是什么岗位? - 知乎
PD（product designer/product director）：产品设计或产品负责人，多见于互联网等以产品为中心的行业。 PM（product manager/project manager）：产品经理或项目经理，前者多见于互联 …
外企中Managing Director和General Manager有什么区别？
外企特别是金融类外企经常有vice president，director, managing director之类，头衔很大，其实很多只是职级而已，大多不是字面表达的意思，更多是类似国内的职称。比如VP其实就是外企 …
名片中职务的对应英文翻译及欧美企业职务含义及级别？ - 知乎
1. 如果怕误解为中干，建议翻为CEO，这个老外一听就了解。至于Managing Director要看总经理是否董事会成员，如果是，CEO和Managing Director取其一（听你们领导的），不要重复用。 …
对董事长、总经理等职务，最准确常用的商务英语翻译是什么？
中小公司： General Manager 或 Managing Director. 合伙人制公司中负责日常运作的那位合伙人： Managing Partner. 副总经理： 看公司大小. 大公司： Executive Vice President， 下面还有 …
外企职位层级的划分？从低到高，求中英文对照? - 知乎
而对大多数在职场打拼了半辈子的senior director来说，他们的职业生涯也就止步于此了。从director 到VP，是只属于少数职场精英们的最后一个升职（升值）瓶颈。 第八级： 副总 …
GM、VP、FVP、CIO都是什么职位？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
导演、监制、制片人、出品人的职责和分工是怎么样的？ - 知乎
本题已加入知乎圆桌 »影视职人说 | 青年导演养成记，更多「电影产业」讨论欢迎关注。
如何知道一个期刊是不是sci？ - 知乎
Master Journal List在这个网站能搜到的就是吗？我在web of knowledge 上能搜到文章的杂志就是sci吗？
执行董事和CEO有什么区别？ - 知乎
Nov 13, 2018 · 我的英国公司职位就是Manaing Director，然后中文名片上写“执行董事”，我承认这个名称一般中国客户都不明白，几乎都认为是董事会的什么角色。但实际上，就是CEO，或者 …
研究员为什么是principal investigator而不是researcher？ - 知乎
美国国家科学基金会(National Science Foundation，简称NSF)定义为”由受让入指定、美国国家自然科学基金委同意的负责项目科学技术方向的个体”，同时指出”这一术语一般用于研究领域”， …
PE、PM、PD、PR分别是什么岗位? - 知乎
PD（product designer/product director）：产品设计或产品负责人，多见于互联网等以产品为中心的行业。 PM（product manager/project manager）：产品经理或项目经理，前者多见于互联 …
