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| data science consulting rates: Business Intelligence Demystified Anoop Kumar V K, 2021-09-25 Clear your doubts about Business Intelligence and start your new journey KEY FEATURES ● Includes successful methods and innovative ideas to achieve success with BI. ● Vendor-neutral, unbiased, and based on experience. ● Highlights practical challenges in BI journeys. ● Covers financial aspects along with technical aspects. ● Showcases multiple BI organization models and the structure of BI teams. DESCRIPTION The book demystifies misconceptions and misinformation about BI. It provides clarity to almost everything related to BI in a simplified and unbiased way. It covers topics right from the definition of BI, terms used in the BI definition, coinage of BI, details of the different main uses of BI, processes that support the main uses, side benefits, and the level of importance of BI, various types of BI based on various parameters, main phases in the BI journey and the challenges faced in each of the phases in the BI journey. It clarifies myths about self-service BI and real-time BI. The book covers the structure of a typical internal BI team, BI organizational models, and the main roles in BI. It also clarifies the doubts around roles in BI. It explores the different components that add to the cost of BI and explains how to calculate the total cost of the ownership of BI and ROI for BI. It covers several ideas, including unconventional ideas to achieve BI success and also learn about IBI. It explains the different types of BI architectures, commonly used technologies, tools, and concepts in BI and provides clarity about the boundary of BI w.r.t technologies, tools, and concepts. The book helps you lay a very strong foundation and provides the right perspective about BI. It enables you to start or restart your journey with BI. WHAT YOU WILL LEARN ● Builds a strong conceptual foundation in BI. ● Gives the right perspective and clarity on BI uses, challenges, and architectures. ● Enables you to make the right decisions on the BI structure, organization model, and budget. ● Explains which type of BI solution is required for your business. ● Applies successful BI ideas. WHO THIS BOOK IS FOR This book is a must-read for business managers, BI aspirants, CxOs, and all those who want to drive the business value with data-driven insights. TABLE OF CONTENTS 1. What is Business Intelligence? 2. Why do Businesses need BI? 3. Types of Business Intelligence 4. Challenges in Business Intelligence 5. Roles in Business Intelligence 6. Financials of Business Intelligence 7. Ideas for Success with BI 8. Introduction to IBI 9. BI Architectures 10. Demystify Tech, Tools, and Concepts in BI |
| data science consulting rates: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data science consulting rates: Consulting Success Michael Zipursky, 2018-10-16 How can you take your skills and expertise and package and present it to become a successful consultant? There are proven time-tested principles, strategies, tactics and best-practices the most successful consultants use to start, run and grow their consulting business. Consulting Success teaches you what they are. In this book you'll learn: - How to position yourself as a leading expert and authority in your marketplace - Effective marketing and branding materials that get the attention of your ideal clients - Strategies to increase your fees and earn more with every project - The proposal template that has generated millions of dollars in consulting engagements - How to develop a pipeline of business and attract ideal clients - Productivity secrets for consultants including how to get more done in one week than most people do in a month - And much, much more |
| data science consulting rates: Consulting Fees Andréa Coutu, 2013-10-09 Thinking of consulting? Want to earn more from your clients? Discover how to set your consulting fees and get more money from every contract. Influence the way existing and potential clients view you and your consulting business by analyzing your market and using your fees as a marketing tool. Calculate your rate using consulting fee models. Navigate sticky situations - like requests to cut your fees, negotiate your quotes or work for free. Andrea Coutu's Consulting Fees helps consultants calculate consulting fees that they can ask for with confidence. |
| data science consulting rates: 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 consulting rates: Data Mining For Dummies Meta S. Brown, 2014-09-04 Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: Model creation, validity testing, and interpretation Effective communication of findings Available tools, both paid and open-source Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining. |
| data science consulting rates: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data science consulting rates: People Skills for Analytical Thinkers Gilbert Eijkelenboom, 2020-09-29 Your analytical skills are incredibly valuable. However, rational thinking alone isn't enough. Have you ever: Presented an idea, but then no one seemed to care? Explained your analysis, only to leave your colleague confused? Struggled to work with people who are less analytical and more emotional? In such situations, people skills make the difference. And that's what this book focuses on: boosting your communication skills as an analytical thinker. Research shows people skills are becoming increasingly important in the workplace, so start learning today. Filled with academic insights, exercises, and stories, this book will change your career. What you will learn Having fun and productive interactions, even with people who don't have an analytical personality Boost your confidence and increase your empathy Learn how to deal with small-talk you don't enjoy Advance your communication skills and build relationships (th)at work Become incredibly persuasive by avoiding the single mistake that almost everyone makes |
| data science consulting rates: Biostatistics For Dummies Monika Wahi, John C. Pezzullo, 2024-07-18 Break down biostatistics, make sense of complex concepts, and pass your class If you're taking biostatistics, you may need or want a little extra assistance as you make your way through. Biostatistics For Dummies follows a typical biostatistics course at the college level, helping you understand even the most difficult concepts, so you can get the grade you need. Start at the beginning by learning how to read and understand mathematical equations and conduct clinical research. Then, use your knowledge to analyze and graph your data. This new edition includes more example problems with step-by-step walkthroughs on how to use statistical software to analyze large datasets. Biostatistics For Dummies is your go-to guide for making sense of it all. Review basic statistics and decode mathematical equations Learn how to analyze and graph data from clinical research studies Look for relationships with correlation and regression Use software to properly analyze large datasets Anyone studying in clinical science, public health, pharmaceutical sciences, chemistry, and epidemiology-related fields will want this book to get through that biostatistics course. |
| data science consulting rates: 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 consulting rates: The Consulting Apprenticeship Steve Shu, 2015-07-16 The Consulting Apprenticeship is written for business professionals and consultants with a focus on nuances passed on during apprenticeship regarding consulting delivery. Business professionals can benefit with a jump-start approach to applying consulting principles to their business. Designed for the busy professional, The Consulting Apprenticeship is a book of forty, quick-read ideas. These forty, short chapters are divided into four sections: Consulting Mindset - This section covers consulting ways of thinking and can be adopted by both company personnel and consultants. Consulting Techniques - This section covers specific tactics and toolkit methods when using consultative approaches in the trenches as either a company- or consulting firm-practitioner. Consulting Mastery - This section covers advanced perspectives on consulting and may be more useful to either company personnel evaluating consultants or mid- to senior-level consultants. Consulting Special Situations - Whereas the prior sections are applicable to a wide variety of situations, this section covers more infrequent, specific business situations involving consultative approaches in the trenches as either a company- or consulting firm-practitioner. Each chapter of the book concludes with an optional, takeaway exercise. The exercises vary widely in terms of level of involvement. For example, in some cases you can refer to online material. In other cases, you can engage in deeper thinking or apply the concepts over an extended period of time. However you choose to use this book, consulting mastery is a lifelong pursuit. I hope this book helps you with your journey. Stephen Shu Praise for The Consulting Apprenticeship When one of the companies I worked for needed help taking its consulting organization to the next level, I hired Steve Shu. His ability to drive our management team - all with different opinions on what we should or should not do - to a 'so-what' conclusion and pragmatic next steps gave us the jump start we needed. He is one of the best and deeply understands how consulting organizations should work. His book provides great techniques as well as tools you can use immediately. - Prakash Panjwani, CEO at WatchGuard Technologies, former President and CEO of SafeNet Steve Shu has put together a comprehensive guide to the all-important nuts and bolts of being a great consultant. The information in Chapter 21, 'Eight Secret Weapons of the Modern Consultant, ' is worth the price of the book. If you're serious about being a more effective consultant, read this book. - Michael McLaughlin, Author of Winning the Professional Services Sale and Principal Consultant at MindShare Consulting LLC; former Partner at Deloitte Steve Shu has written a hands-on, highly practical guide for new management consultants and internal corporate business strategists alike. So many projects fail because they do not practice the basic consulting project management hygiene Steve describes in chapter 11. If you are new to the trade and want to greatly increase your chance of delivering successful consulting projects, read this book. - Robert Reppa, Vice President Strategy at Johnson Controls and former Partner at Booz & Company Steve Shu has written a Rosetta Stone for both new and experienced consultants. Filled with forty power-packed ideas and practical chapter takeaways, Consulting Apprenticeship is structured for busy executives to easily digest each concept. A must read for those who seek to go beyond the shallow bromides of the consulting profession, and hone their skills with deeper, more meaningful approaches. - Adrian C. Ott, Award-winning author of The 24-Hour Customer, and CEO, Exponential Edge Inc, called One of Silicon Valley's most respected strategists by Consulting Magazine |
| data science consulting rates: 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. |
| data science consulting rates: Data Science For Dummies Lillian Pierson, 2021-09-15 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data science consulting rates: Cleaning Data for Effective Data Science David Mertz, 2021-03-31 Think about your data intelligently and ask the right questions Key FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses. What you will learnIngest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structuresUnderstand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and BashApply useful rules and heuristics for assessing data quality and detecting bias, like Benford’s law and the 68-95-99.7 ruleIdentify and handle unreliable data and outliers, examining z-score and other statistical propertiesImpute sensible values into missing data and use sampling to fix imbalancesUse dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your dataWork carefully with time series data, performing de-trending and interpolationWho this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful. |
| data science consulting rates: Data science and health economics in precision public health Demetris Lamnisos, Michael A. Talias, Alexandros Heraclides, 2023-11-15 |
| data science consulting rates: Principles of Data Science Sinan Ozdemir, 2016-12-16 Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how to perform real-world data science tasks with R and Python Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn Get to know the five most important steps of data science Use your data intelligently and learn how to handle it with care Bridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results Build and evaluate baseline machine learning models Explore the most effective metrics to determine the success of your machine learning models Create data visualizations that communicate actionable insights Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world. |
| data science consulting rates: The Inn at Little Washington Cookbook Patrick O'Connell, 2011-11-02 110 sparklingly original recipes from the world-renowned self-taught chef and founder of the three-star Michelin restaurant The Inn at Little Washington Patrick O’Connell, a self-taught chef who read cookbooks to learn how to cook, began his culinary career with a catering business in an old farmhouse, cooking on a wood stove with an electric frying pan purchased for $1.49 at a garage sale. To O’Connell’s surprise, the pan was able for boil, sauté, and deep fry for parties of up to 300 guests, which sharpened his awareness of how much could be done with very little. In 1978, his catering business evolved into a country restaurant and Inn, operating out of a defunct garage in a small Virginia town affectionately referred to as “Little” Washington. Now a multiple James Beard Award–winning and Michelin star restaurant, The Inn at Little Washington was America’s first five-star Inn. In The Little Inn at Washington Cookbook, O’Connell assembles elegant, simple, and straightforward recipes that elevate everyday ingredients. With helpful, detailed instructions, O’Connell teaches you how to make over one hundred dishes, from Fresh Tuna Tartare on Tuna Carpaccio with Wasabi Mayonnaise and Miniature Caramelized Onion Tartlets to Rockfish Roasted with White Wine, Tomatoes, and Black Olives on Toasted Couscous and Steamed Lobster with Grapefruit Butter Sauce. He also includes delicious desserts, such as Rosemary Crème Brulé and Double-Pumpkin Roulade, and savory sides, like Creamy Garlic Polenta and My Grandmother’s Baked Beans. With over three hundred stunning, mouthwatering photographs and thoughtful reflections from O’Connell, The Inn at Little Washington Cookbook is a fresh and glorious resource and a romantic culinary journey through the Virginia countryside. |
| data science consulting rates: Reinforcement Learning Phil Winder Ph.D., 2020-11-06 Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website |
| data science consulting rates: Directory of Consultants , 1985 |
| data science consulting rates: SQL Pocket Guide Alice Zhao, 2021-08-26 If you use SQL in your day-to-day work as a data analyst, data scientist, or data engineer, this popular pocket guide is your ideal on-the-job reference. You'll find many examples that address the language's complexities, along with key aspects of SQL used in Microsoft SQL Server, MySQL, Oracle Database, PostgreSQL, and SQLite. In this updated edition, author Alice Zhao describes how these database management systems implement SQL syntax for both querying and making changes to a database. You'll find details on data types and conversions, regular expression syntax, window functions, pivoting and unpivoting, and more. Quickly look up how to perform specific tasks using SQL Apply the book's syntax examples to your own queries Update SQL queries to work in five different database management systems NEW: Connect Python and R to a relational database NEW: Look up frequently asked SQL questions in the How Do I? chapter |
| data science consulting rates: Forecasting: principles and practice Rob J Hyndman, George Athanasopoulos, 2018-05-08 Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. |
| data science consulting rates: Big Data Driven Supply Chain Management Nada R. Sanders, 2014-05-07 Master a complete, five-step roadmap for leveraging Big Data and analytics to gain unprecedented competitive advantage from your supply chain. Using Big Data, pioneers such as Amazon, UPS, and Wal-Mart are gaining unprecedented mastery over their supply chains. They are achieving greater visibility into inventory levels, order fulfillment rates, material and product delivery… using predictive data analytics to match supply with demand; leveraging new planning strengths to optimize their sales channel strategies; optimizing supply chain strategy and competitive priorities; even launching powerful new ventures. Despite these opportunities, many supply chain operations are gaining limited or no value from Big Data. In Big Data Driven Supply Chain Management, Nada Sanders presents a systematic five-step framework for using Big Data in supply chains. You'll learn best practices for segmenting and analyzing customers, defining competitive priorities for each segment, aligning functions behind strategy, dissolving organizational boundaries to sense demand and make better decisions, and choose the right metrics to support all of this. Using these techniques, you can overcome the widespread obstacles to making the most of Big Data in your supply chain — and earn big profits from the data you're already generating. For all executives, managers, and analysts interested in using Big Data technologies to improve supply chain performance. |
| data science consulting rates: Big Data Is Not a Monolith Cassidy R. Sugimoto, Hamid R. Ekbia, Michael Mattioli, 2016-10-21 Perspectives on the varied challenges posed by big data for health, science, law, commerce, and politics. Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies. The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control through monitoring, mining, and manipulation; big data and society, examining both its empowering and its constraining effects; big data and science, considering issues of data governance, provenance, reuse, and trust; and big data and organizations, discussing data responsibility, “data harm,” and decision making. Contributors Ryan Abbott, Cristina Alaimo, Kent R. Anderson, Mark Andrejevic, Diane E. Bailey, Mike Bailey, Mark Burdon, Fred H. Cate, Jorge L. Contreras, Simon DeDeo, Hamid R. Ekbia, Allison Goodwell, Jannis Kallinikos, Inna Kouper, M. Lynne Markus, Michael Mattioli, Paul Ohm, Scott Peppet, Beth Plale, Jason Portenoy, Julie Rennecker, Katie Shilton, Dan Sholler, Cassidy R. Sugimoto, Isuru Suriarachchi, Jevin D. West |
| data science consulting rates: Performance Consulting Dana Gaines Robinson, James C. Robinson, Jack J. Phillips, Patricia Pulliam Phillips, Dick Handshaw, 2015-06 NEW EDITION, REVISED AND UPDATED In America, organizations spend $175 billion in training initiatives and more than $500 billion in human resource solutions every year yet often have little to show for it. One reason is that people “jump to solutions” before they identify the causes of the problem. Performance consultants are effective because they partner with clients to clarify business goals and determine root causes for gaps between desired and current results. Only then are specific solutions agreed upon and implemented. This third edition of the classic book that introduced performance consulting adds a wealth of new material. There are new case examples throughout and four new chapters providing detailed steps for measuring results from performance consulting initiatives on five different levels, including ROI. The book includes a never-before-published Alignment and Measurement Model, allowing you to connect organizational needs and performance consulting initiatives designed to address those needs with the appropriate level of measurement. This remains a profoundly practical book, featuring tools, models, and checklists. It will enable you to make a difference in your organization that is valued, measurable, and sustainable. |
| data science consulting rates: Network Models for Data Science Alan Julian Izenman, 2023-01-05 This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component. |
| data science consulting rates: A Directory of Information Resources in the United States: Physical Sciences, Engineering National Referral Center (U.S.), 1971 |
| data science consulting rates: 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 |
| data science consulting rates: Data Science in Practice Alan Said, Vicenç Torra, 2018-09-19 This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage. |
| data science consulting rates: Problem Solving Chris Chatfield, 1995-05-11 This book illuminates the complex process of problem solving, including formulating the problem, collecting and analyzing data, and presenting the conclusions. |
| data science consulting rates: Think Bayes Allen B. Downey, 2021-05-18 If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and you'll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but there aren't many resources available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start. Use your programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing Get started with simple examples, using coins, dice, and a bowl of cookies Learn computational methods for solving real-world problems |
| data science consulting rates: Modern Fortran Milan Curcic, 2020-10-07 Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you’ll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. Summary Using Fortran, early and accurate forecasts for hurricanes and other major storms have saved thousands of lives. Better designs for ships, planes, and automobiles have made travel safer, more efficient, and less expensive than ever before. Using Fortran, low-level machine learning and deep learning libraries provide incredibly easy, fast, and insightful analysis of massive data. Fortran is an amazingly powerful and flexible programming language that forms the foundation of high performance computing for research, science, and industry. And it's come a long, long way since starting life on IBM mainframes in 1956. Modern Fortran is natively parallel, so it's uniquely suited for efficiently handling problems like complex simulations, long-range predictions, and ultra-precise designs. If you're working on tasks where speed, accuracy, and efficiency matter, it's time to discover—or re-discover—Fortran.. About the technology For over 60 years Fortran has been powering mission-critical scientific applications, and it isn't slowing down yet! Rock-solid reliability and new support for parallel programming make Fortran an essential language for next-generation high-performance computing. Simply put, the future is in parallel, and Fortran is already there. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you'll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. What's inside Fortran's place in the modern world Working with variables, arrays, and functions Module development Parallelism with coarrays, teams, and events Interoperating Fortran with C About the reader For developers and computational scientists. No experience with Fortran required. About the author Milan Curcic is a meteorologist, oceanographer, and author of several general-purpose Fortran libraries and applications. Table of Contents PART 1 - GETTING STARTED WITH MODERN FORTRAN 1 Introducing Fortran 2 Getting started: Minimal working app PART 2 - CORE ELEMENTS OF FORTRAN 3 Writing reusable code with functions and subroutines 4 Organizing your Fortran code using modules 5 Analyzing time series data with arrays 6 Reading, writing, and formatting your data PART 3 - ADVANCED FORTRAN USE 7 Going parallel with Fortan coarrays 8 Working with abstract data using derived types 9 Generic procedures and operators for any data type 10 User-defined operators for derived types PART 4 - THE FINAL STRETCH 11 Interoperability with C: Exposing your app to the web 12 Advanced parallelism with teams, events, and collectives |
| data science consulting rates: 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 consulting rates: National Patterns of Science and Technology Resources , 1986 |
| data science consulting rates: A Textbook of Data Science for Class 9 Shweta Mittal , Prof. (Dr.) Shalini Verma , Goyal Brothers Prakashan, 2021-09-01 Data sCIenCe is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science or data-driven science enables better decision-making, predictive analysis, and pattern discovery. It lets you find the leading cause of a problem by asking the right questions and performing an exploratory study on the data. It models the data using various algorithms and communicates and visualizes the results via graphs, dashboards, etc. This book is based on the latest CBSE syllabus. The book is divided into two sections: Part A and Part B. Part A includes the “Employability Skills” and Part B covers the “Subject-specific Skills”. This book presents the concepts in a very simple language with easy-to-understand examples adapted from day-to-day utilization of Data science technology. The chapters are supplemented with figures and additional information in the form of “DID yoU knoW”. In between the chapters, the students are given a chance to revise and challenge their understanding with the help of “CheCk yoUr knoWleDGe” and fun activities. At the end of every chapter, Multiple Choice Questions, Short and Long answer questions are given. It includes HOTS (Higher Order Thinking Skills) questions and Applied Projects for advanced and practical kinds of questions. |
| data science consulting rates: Hacking the Electorate Eitan Hersh, 2015-06-09 Hacking the Electorate focuses on the consequences of campaigns using microtargeting databases to mobilize voters in elections. Eitan Hersh shows that most of what campaigns know about voters comes from a core set of public records, and the content of public records varies from state to state. This variation accounts for differences in campaign strategies and voter coalitions across the nation. |
| data science consulting rates: The Business Case for AI Kavita Ganesan, 2022-04-05 AI is much more than a sexy marketing gimmick. At its core, it’s a powerful business tool that can make operations more efficient, employees more productive, and customers delighted. Unfortunately, leaders who want to take advantage of AI often don’t know where to start. The process can feel overwhelming—from choosing where to apply AI to preparing every tier of the organization for the transition. In this practical guide for business leaders, Kavita Ganesan takes the mystery out of implementing AI, showing you how to launch AI initiatives that get results. With real-world AI examples to spark your own ideas, you’ll learn how to identify high-impact AI opportunities, prepare for AI transitions, and measure your AI performance. Simple and compelling, The Business Case for AI gives leaders the information they need without the technical jargon. Whether you want to jumpstart your AI strategy, manage your AI initiatives for better outcomes, or simply find inspiration for your own AI applications, The Business Case for AI is your blueprint for AI success. Topics covered include: -Real-World AI Use Cases -What's AI in Business -The B-CIDS: The 5 pillars of AI preparation (or readiness) -The Jumpstart AI Approach -The HI-AI Discovery Framework -Measuring AI Success And more throughout the book… |
| data science consulting rates: Critical Analysis of Prototype Autonomous Vehicle Crash Rates Richard Young, 2021-11-30 Will Automated Vehicles be Safer than Conventional Vehicles? One of the critically important questions that has emerged about advanced technologies in transportation is how to test the actual effects of these advanced systems on safety, particularly how to evaluate the safety of highly automated driving systems. Richard Young's Critical Analysis of Prototype Autonomous Vehicle Crash Rates does a deep dive into these questions by reviewing and then critically analyzing the first six scientific studies of AV crash rates. |
| data science consulting rates: Value-Based Fees Alan Weiss, 2008-11-03 In this thoroughly revised edition of his classic book, Alan Weiss shows how consulting fees are dependent on only two things: value provided in the perception of the buyer and the intent of the buyer and the consultant to act ethically. Many consultants, however, fail to understand that perceived value is the basis of the fee, or that they must translate the importance of their advice into long-term gains for the client in the client's perception. Still others fail to have the courage and the belief system that support the high value delivered to clients, thereby reducing fees to a level commensurate with the consultant's own low self-esteem. Ultimately, says Weiss, consultants, not clients, are the main cause of low consulting fees. |
| data science consulting rates: Management Consulting in the Era of the Digital Organization David Brian Szabla, 2023-07-01 The 4th Industrial Revolution is well underway. Our lives are changing at an exponential rate, resulting in a multi-faceted, deeply interconnected world. The digital revolution is integrating multiple technologies, which is leading to unprecedented paradigm shifts in the economy, management, and society. Entire systems across countries, industries, and societies are being transformed, triggering a transformation that is unlike anything humankind has ever experienced. Given the confluence of dramatic changes in organizational life, triggering emerging technology breakthroughs such as robotics, the internet of things, biotechnology, materials science, data science and big data, and quantum computing, this volume of the Research in Management Consulting series explores how the research and practice of management consulting is unfolding in a new era of profound shifts in the way researchers and consultants sense, think, and act. The authors of this volume bring both to scholars and practitioners the latest discussions of efforts to understand consulting in organizations amplified by the fusion of technologies across physical, digital, and biological worlds. They also bring to light a movement from human supervised artificial intelligence systems to fully autonomous artificial intelligence systems that have the potential to demonstrate intelligence beyond uman capabilities. |
| data science consulting rates: Health Services Reports , 2002 |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
