| data science cheat sheet: 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 cheat sheet: Data Science Programming All-in-One For Dummies John Paul Mueller, Luca Massaron, 2020-01-09 Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s! |
| data science cheat sheet: Data Science Strategy For Dummies Ulrika Jägare, 2019-06-12 All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. Learn exactly what data science is and why it’s important Adopt a data-driven mindset as the foundation to success Understand the processes and common roadblocks behind data science Keep your data science program focused on generating business value Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value. |
| data science cheat sheet: Pandas for Everyone Daniel Y. Chen, 2017-12-15 The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning |
| data science cheat sheet: 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 cheat sheet: Algorithms For Dummies John Paul Mueller, Luca Massaron, 2017-04-24 Discover how algorithms shape and impact our digital world All data, big or small, starts with algorithms. Algorithms are mathematical equations that determine what we see—based on our likes, dislikes, queries, views, interests, relationships, and more—online. They are, in a sense, the electronic gatekeepers to our digital, as well as our physical, world. This book demystifies the subject of algorithms so you can understand how important they are business and scientific decision making. Algorithms for Dummies is a clear and concise primer for everyday people who are interested in algorithms and how they impact our digital lives. Based on the fact that we already live in a world where algorithms are behind most of the technology we use, this book offers eye-opening information on the pervasiveness and importance of this mathematical science—how it plays out in our everyday digestion of news and entertainment, as well as in its influence on our social interactions and consumerism. Readers even learn how to program an algorithm using Python! Become well-versed in the major areas comprising algorithms Examine the incredible history behind algorithms Get familiar with real-world applications of problem-solving procedures Experience hands-on development of an algorithm from start to finish with Python If you have a nagging curiosity about why an ad for that hammock you checked out on Amazon is appearing on your Facebook page, you'll find Algorithm for Dummies to be an enlightening introduction to this integral realm of math, science, and business. |
| data science cheat sheet: Cracking the Data Science Interview Maverick Lin, 2019-12-17 Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. Topics include: - Necessary Prerequisites (statistics, probability, linear algebra, and computer science) - 18 Big Ideas in Data Science (such as Occam's Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality) - Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization) - Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more) - Reinforcement Learning (Q-Learning and Deep Q-Learning) - Non-Machine Learning Tools (graph theory, ARIMA, linear programming) - Case Studies (a look at what data science means at companies like Amazon and Uber) Maverick holds a bachelor's degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics. |
| data science cheat sheet: Beautiful Visualization Julie Steele, Noah Iliinsky, 2010-04-23 Visualization is the graphic presentation of data -- portrayals meant to reveal complex information at a glance. Think of the familiar map of the New York City subway system, or a diagram of the human brain. Successful visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding. This book examines the methods of two dozen visualization experts who approach their projects from a variety of perspectives -- as artists, designers, commentators, scientists, analysts, statisticians, and more. Together they demonstrate how visualization can help us make sense of the world. Explore the importance of storytelling with a simple visualization exercise Learn how color conveys information that our brains recognize before we're fully aware of it Discover how the books we buy and the people we associate with reveal clues to our deeper selves Recognize a method to the madness of air travel with a visualization of civilian air traffic Find out how researchers investigate unknown phenomena, from initial sketches to published papers Contributors include: Nick Bilton,Michael E. Driscoll,Jonathan Feinberg,Danyel Fisher,Jessica Hagy,Gregor Hochmuth,Todd Holloway,Noah Iliinsky,Eddie Jabbour,Valdean Klump,Aaron Koblin,Robert Kosara,Valdis Krebs,JoAnn Kuchera-Morin et al.,Andrew Odewahn,Adam Perer,Anders Persson,Maximilian Schich,Matthias Shapiro,Julie Steele,Moritz Stefaner,Jer Thorp,Fernanda Viegas,Martin Wattenberg,and Michael Young. |
| data science cheat sheet: Python All-in-One For Dummies John C. Shovic, Alan Simpson, 2019-05-07 Your one-stop resource on all things Python Thanks to its flexibility, Python has grown to become one of the most popular programming languages in the world. Developers use Python in app development, web development, data science, machine learning, and even in coding education classes. There's almost no type of project that Python can't make better. From creating apps to building complex websites to sorting big data, Python provides a way to get the work done. Python All-in-One For Dummies offers a starting point for those new to coding by explaining the basics of Python and demonstrating how it’s used in a variety of applications. Covers the basics of the language Explains its syntax through application in high-profile industries Shows how Python can be applied to projects in enterprise Delves into major undertakings including artificial intelligence, physical computing, machine learning, robotics and data analysis This book is perfect for anyone new to coding as well as experienced coders interested in adding Python to their toolbox. |
| data science cheat sheet: Enterprise AI For Dummies Zachary Jarvinen, 2020-08-17 Master the application of artificial intelligence in your enterprise with the book series trusted by millions In Enterprise AI For Dummies, author Zachary Jarvinen simplifies and explains to readers the complicated world of artificial intelligence for business. Using practical examples, concrete applications, and straightforward prose, the author breaks down the fundamental and advanced topics that form the core of business AI. Written for executives, managers, employees, consultants, and students with an interest in the business applications of artificial intelligence, Enterprise AI For Dummies demystifies the sometimes confusing topic of artificial intelligence. No longer will you lag behind your colleagues and friends when discussing the benefits of AI and business. The book includes discussions of AI applications, including: Streamlining business operations Improving decision making Increasing automation Maximizing revenue The For Dummies series makes topics understandable, and as such, this book is written in an easily understood style that's perfect for anyone who seeks an introduction to a usually unforgiving topic. |
| data science cheat sheet: Introduction to Probability Joseph K. Blitzstein, Jessica Hwang, 2014-07-24 Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment. |
| data science cheat sheet: Microsoft Power BI For Dummies Jack A. Hyman, 2022-02-08 Reveal the insights behind your company’s data with Microsoft Power BI Microsoft Power BI allows intuitive access to data that can power intelligent business decisions and insightful strategies. The question is, do you have the Power BI skills to make your organization’s numbers spill their secrets? In Microsoft Power BI For Dummies, expert lecturer, consultant, and author Jack Hyman delivers a start-to-finish guide to applying the Power BI platform to your own firm’s data. You’ll discover how to start exploring your data sources, build data models, visualize your results, and create compelling reports that motivate decisive action. Tackle the basics of Microsoft Power BI and, when you’re done with that, move on to advanced functions like accessing data with DAX and app integrations Guide your organization’s direction and decisions with rock-solid conclusions based on real-world data Impress your bosses and confidently lead your direct reports with exciting insights drawn from Power BI’s useful visualization tools It’s one thing for your company to have data at its disposal. It’s another thing entirely to know what to do with it. Microsoft Power BI For Dummies is the straightforward blueprint you need to apply one of the most powerful business intelligence tools on the market to your firm’s existing data. |
| data science cheat sheet: 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 cheat sheet: Predictive Analytics For Dummies Anasse Bari, Mohamed Chaouchi, Tommy Jung, 2014-03-06 Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions. Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more. Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies. |
| data science cheat sheet: Excel Data Analysis For Dummies Paul McFedries, 2018-11-13 Take Excel to the next level Excel is the world’s leading spreadsheet application. It’s a key module in Microsoft Office—the number-one productivity suite—and it is the number-one business intelligence tool. An Excel dashboard report is a visual presentation of critical data and uses gauges, maps, charts, sliders, and other graphical elements to present complex data in an easy-to-understand format. Excel Data Analysis For Dummies explains in depth how to use Excel as a tool for analyzing big data sets. In no time, you’ll discover how to mine and analyze critical data in order to make more informed business decisions. Work with external databases, PivotTables, and Pivot Charts Use Excel for statistical and financial functions and data sharing Get familiar with Solver Use the Small Business Finance Manager If you’re familiar with Excel but lack a background in the technical aspects of data analysis, this user-friendly book makes it easy to start putting it to use for you. |
| data science cheat sheet: Python for Data Science For Dummies John Paul Mueller, Luca Massaron, 2015-06-23 Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover. |
| data science cheat sheet: Graph Algorithms Mark Needham, Amy E. Hodler, 2019-05-16 Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark |
| data science cheat sheet: Blockchain Data Analytics For Dummies Michael G. Solomon, 2020-09-02 Get ahead of the curve—learn about big data on the blockchain Blockchain came to prominence as the disruptive technology that made cryptocurrencies work. Now, data pros are using blockchain technology for faster real-time analysis, better data security, and more accurate predictions. Blockchain Data Analytics For Dummies is your quick-start guide to harnessing the potential of blockchain. Inside this book, technologists, executives, and data managers will find information and inspiration to adopt blockchain as a big data tool. Blockchain expert Michael G. Solomon shares his insight on what the blockchain is and how this new tech is poised to disrupt data. Set your organization on the cutting edge of analytics, before your competitors get there! Learn how blockchain technologies work and how they can integrate with big data Discover the power and potential of blockchain analytics Establish data models and quickly mine for insights and results Create data visualizations from blockchain analysis Discover how blockchains are disrupting the data world with this exciting title in the trusted For Dummies line! |
| data science cheat sheet: Deploying Machine Learning Robbie Allen, 2019-05 Increasingly, business leaders and managers recognize that machine learning offers their companies immense opportunities for competitive advantage. But most discussions of machine learning are intensely technical or academic, and don't offer practical information leaders can use to identify, evaluate, plan, or manage projects. Deploying Machine Learning fills that gap, helping them clarify exactly how machine learning can help them, and collaborate with technologists to actually apply it successfully. You'll learn: What machine learning is, how it compares to big data and artificial intelligence, and why it's suddenly so important What machine learning can do for you: solutions for computer vision, natural language processing, prediction, and more How to use machine learning to solve real business problems -- from reducing costs through improving decision-making and introducing new products Separating hype from reality: identifying pitfalls, limitations, and misconceptions upfront Knowing enough about the technology to work effectively with your technical team Getting the data right: sourcing, collection, governance, security, and culture Solving harder problems: exploring deep learning and other advanced techniques Understanding today's machine learning software and hardware ecosystem Evaluating potential projects, and addressing workforce concerns Staffing your project, acquiring the right tools, and building a workable project plan Interpreting results -- and building an organization that can increasingly learn from data Using machine learning responsibly and ethically Preparing for tomorrow's advances The authors conclude with five chapter-length case studies: image, text, and video analysis, chatbots, and prediction applications. For each, they don't just present results: they also illuminate the process the company undertook, and the pitfalls it overcame along the way. |
| data science cheat sheet: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| data science cheat sheet: The Algorithm Design Manual Steven S Skiena, 2009-04-05 This newly expanded and updated second edition of the best-selling classic continues to take the mystery out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: • Doubles the tutorial material and exercises over the first edition • Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video • Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them • Includes several NEW war stories relating experiences from real-world applications • Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java |
| data science cheat sheet: Data Lakes For Dummies Alan R. Simon, 2021-07-14 Take a dive into data lakes “Data lakes” is the latest buzz word in the world of data storage, management, and analysis. Data Lakes For Dummies decodes and demystifies the concept and helps you get a straightforward answer the question: “What exactly is a data lake and do I need one for my business?” Written for an audience of technology decision makers tasked with keeping up with the latest and greatest data options, this book provides the perfect introductory survey of these novel and growing features of the information landscape. It explains how they can help your business, what they can (and can’t) achieve, and what you need to do to create the lake that best suits your particular needs. With a minimum of jargon, prolific tech author and business intelligence consultant Alan Simon explains how data lakes differ from other data storage paradigms. Once you’ve got the background picture, he maps out ways you can add a data lake to your business systems; migrate existing information and switch on the fresh data supply; clean up the product; and open channels to the best intelligence software for to interpreting what you’ve stored. Understand and build data lake architecture Store, clean, and synchronize new and existing data Compare the best data lake vendors Structure raw data and produce usable analytics Whatever your business, data lakes are going to form ever more prominent parts of the information universe every business should have access to. Dive into this book to start exploring the deep competitive advantage they make possible—and make sure your business isn’t left standing on the shore. |
| data science cheat sheet: Python One-Liners Christian Mayer, 2020-05-12 Python programmers will improve their computer science skills with these useful one-liners. Python One-Liners will teach you how to read and write one-liners: concise statements of useful functionality packed into a single line of code. You'll learn how to systematically unpack and understand any line of Python code, and write eloquent, powerfully compressed Python like an expert. The book's five chapters cover tips and tricks, regular expressions, machine learning, core data science topics, and useful algorithms. Detailed explanations of one-liners introduce key computer science concepts and boost your coding and analytical skills. You'll learn about advanced Python features such as list comprehension, slicing, lambda functions, regular expressions, map and reduce functions, and slice assignments. You'll also learn how to: • Leverage data structures to solve real-world problems, like using Boolean indexing to find cities with above-average pollution • Use NumPy basics such as array, shape, axis, type, broadcasting, advanced indexing, slicing, sorting, searching, aggregating, and statistics • Calculate basic statistics of multidimensional data arrays and the K-Means algorithms for unsupervised learning • Create more advanced regular expressions using grouping and named groups, negative lookaheads, escaped characters, whitespaces, character sets (and negative characters sets), and greedy/nongreedy operators • Understand a wide range of computer science topics, including anagrams, palindromes, supersets, permutations, factorials, prime numbers, Fibonacci numbers, obfuscation, searching, and algorithmic sorting By the end of the book, you'll know how to write Python at its most refined, and create concise, beautiful pieces of Python art in merely a single line. |
| data science cheat sheet: Coding All-in-One For Dummies Nikhil Abraham, 2017-04-18 See all the things coding can accomplish The demand for people with coding know-how exceeds the number of people who understand the languages that power technology. Coding All-in-One For Dummies gives you an ideal place to start when you're ready to add this valuable asset to your professional repertoire. Whether you need to learn how coding works to build a web page or an application or see how coding drives the data revolution, this resource introduces the languages and processes you'll need to know. Peek inside to quickly learn the basics of simple web languages, then move on to start thinking like a professional coder and using languages that power big applications. Take a look inside for the steps to get started with updating a website, creating the next great mobile app, or exploring the world of data science. Whether you're looking for a complete beginner's guide or a trusted resource for when you encounter problems with coding, there's something for you! Create code for the web Get the tools to create a mobile app Discover languages that power data science See the future of coding with machine learning tools With the demand for skilled coders at an all-time high, Coding All-in-One For Dummies is here to propel coding newbies to the ranks of professional programmers. |
| data science cheat sheet: Better Presentations Jonathan Schwabish, 2016-11-15 Whether you are a university professor, researcher at a think tank, graduate student, or analyst at a private firm, chances are that at some point you have presented your work in front of an audience. Most of us approach this task by converting a written document into slides, but the result is often a text-heavy presentation saddled with bullet points, stock images, and graphs too complex for an audience to decipher—much less understand. Presenting is fundamentally different from writing, and with only a little more time, a little more effort, and a little more planning, you can communicate your work with force and clarity. Designed for presenters of scholarly or data-intensive content, Better Presentations details essential strategies for developing clear, sophisticated, and visually captivating presentations. Following three core principles—visualize, unify, and focus—Better Presentations describes how to visualize data effectively, find and use images appropriately, choose sensible fonts and colors, edit text for powerful delivery, and restructure a written argument for maximum engagement and persuasion. With a range of clear examples for what to do (and what not to do), the practical package offered in Better Presentations shares the best techniques to display work and the best tactics for winning over audiences. It pushes presenters past the frustration and intimidation of the process to more effective, memorable, and persuasive presentations. |
| data science cheat sheet: People Analytics For Dummies Mike West, 2019-03-19 Maximize performance with better data Developing a successful workforce requires more than a gut check. Data can help guide your decisions on everything from where to seat a team to optimizing production processes to engaging with your employees in ways that ring true to them. People analytics is the study of your number one business asset—your people—and this book shows you how to collect data, analyze that data, and then apply your findings to create a happier and more engaged workforce. Start a people analytics project Work with qualitative data Collect data via communications Find the right tools and approach for analyzing data If your organization is ready to better understand why high performers leave, why one department has more personnel issues than another, and why employees violate, People Analytics For Dummies makes it easier. |
| data science cheat sheet: Big Data For Dummies Judith S. Hurwitz, Alan Nugent, Fern Halper, Marcia Kaufman, 2013-04-02 Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization. |
| data science cheat sheet: Coding For Dummies Nikhil Abraham, 2016-05-27 Coding For Dummies, (9781119293323) was previously published as Coding For Dummies, (9781118951309). While this version features a new Dummies cover and design, the content is the same as the prior release and should not be considered a new or updated product. Hands-on exercises help you learn to code like a pro No coding experience is required for Coding For Dummies, your one-stop guide to building a foundation of knowledge in writing computer code for web, application, and software development. It doesn't matter if you've dabbled in coding or never written a line of code, this book guides you through the basics. Using foundational web development languages like HTML, CSS, and JavaScript, it explains in plain English how coding works and why it's needed. Online exercises developed by Codecademy, a leading online code training site, help hone coding skills and demonstrate results as you practice. The site provides an environment where you can try out tutorials built into the text and see the actual output from your coding. You'll also gain access to end-of-chapter challenges to apply newly acquired skills to a less-defined assignment. So what are you waiting for? The current demand for workers with coding and computer science skills far exceeds the supply Teaches the foundations of web development languages in an easy-to-understand format Offers unprecedented opportunities to practice basic coding languages Readers can access online hands-on exercises and end-of-chapter assessments that develop and test their new-found skills If you're a student looking for an introduction to the basic concepts of coding or a professional looking to add new skills, Coding For Dummies has you covered. |
| data science cheat sheet: Generative Adversarial Networks Projects Kailash Ahirwar, 2019-01-31 Explore various Generative Adversarial Network architectures using the Python ecosystem Key FeaturesUse different datasets to build advanced projects in the Generative Adversarial Network domainImplement projects ranging from generating 3D shapes to a face aging applicationExplore the power of GANs to contribute in open source research and projectsBook Description Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain. Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation. By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects. What you will learnTrain a network on the 3D ShapeNet dataset to generate realistic shapesGenerate anime characters using the Keras implementation of DCGANImplement an SRGAN network to generate high-resolution imagesTrain Age-cGAN on Wiki-Cropped images to improve face verificationUse Conditional GANs for image-to-image translationUnderstand the generator and discriminator implementations of StackGAN in KerasWho this book is for If you’re a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you. |
| data science cheat sheet: R for Stata Users Robert A. Muenchen, Joseph M. Hilbe, 2010-04-26 Stata is the most flexible and extensible data analysis package available from a commercial vendor. R is a similarly flexible free and open source package for data analysis, with over 3,000 add-on packages available. This book shows you how to extend the power of Stata through the use of R. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches. When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. A glossary defines over 50 R terms using Stata jargon and again using more formal R terminology. The table of contents and index allow you to find equivalent R functions by looking up Stata commands and vice versa. The example programs and practice datasets for both R and Stata are available for download. |
| data science cheat sheet: Statistical Analysis with R For Dummies Joseph Schmuller, 2017-03-20 Understanding the world of R programming and analysis has never been easier Most guides to R, whether books or online, focus on R functions and procedures. But now, thanks to Statistical Analysis with R For Dummies, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses—as well as step-by-step guidance that shows you exactly how to implement them using R programming. People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with R For Dummies enables you to perform these analyses and to fully understand their implications and results. Gets you up to speed on the #1 analytics/data science software tool Demonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modeling Shows you how R offers intel from leading researchers in data science, free of charge Provides information on using R Studio to work with R Get ready to use R to crunch and analyze your data—the fast and easy way! |
| data science cheat sheet: Statistics for Big Data For Dummies Alan Anderson, 2015-08-31 The fast and easy way to make sense of statistics for big data Does the subject of data analysis make you dizzy? You've come to the right place! Statistics For Big Data For Dummies breaks this often-overwhelming subject down into easily digestible parts, offering new and aspiring data analysts the foundation they need to be successful in the field. Inside, you'll find an easy-to-follow introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages, plain-English explanations of how to make sense of data in the real world, and much more. Data has never been easier to come by, and the tools students and professionals need to enter the world of big data are based on applied statistics. While the word statistics alone can evoke feelings of anxiety in even the most confident student or professional, it doesn't have to. Written in the familiar and friendly tone that has defined the For Dummies brand for more than twenty years, Statistics For Big Data For Dummies takes the intimidation out of the subject, offering clear explanations and tons of step-by-step instruction to help you make sense of data mining—without losing your cool. Helps you to identify valid, useful, and understandable patterns in data Provides guidance on extracting previously unknown information from large databases Shows you how to discover patterns available in big data Gives you access to the latest tools and techniques for working in big data If you're a student enrolled in a related Applied Statistics course or a professional looking to expand your skillset, Statistics For Big Data For Dummies gives you access to everything you need to succeed. |
| data science cheat sheet: The DevOps Handbook Gene Kim, Jez Humble, Patrick Debois, John Willis, 2016-10-06 Increase profitability, elevate work culture, and exceed productivity goals through DevOps practices. More than ever, the effective management of technology is critical for business competitiveness. For decades, technology leaders have struggled to balance agility, reliability, and security. The consequences of failure have never been greater―whether it's the healthcare.gov debacle, cardholder data breaches, or missing the boat with Big Data in the cloud. And yet, high performers using DevOps principles, such as Google, Amazon, Facebook, Etsy, and Netflix, are routinely and reliably deploying code into production hundreds, or even thousands, of times per day. Following in the footsteps of The Phoenix Project, The DevOps Handbook shows leaders how to replicate these incredible outcomes, by showing how to integrate Product Management, Development, QA, IT Operations, and Information Security to elevate your company and win in the marketplace. |
| data science cheat sheet: The Employee Experience Advantage Jacob Morgan, 2017-03-01 Research Shows Organizations That Focus on Employee Experience Far Outperform Those That Don't Recently a new type of organization has emerged, one that focuses on employee experiences as a way to drive innovation, increase customer satisfaction, find and hire the best people, make work more engaging, and improve overall performance. The Employee Experience Advantage is the first book of its kind to tackle this emerging topic that is becoming the #1 priority for business leaders around the world. Although everyone talks about employee experience nobody has really been able to explain concretely what it is and how to go about designing for it...until now. How can organizations truly create a place where employees want to show up to work versus need to show up to work? For decades the business world has focused on measuring employee engagement meanwhile global engagement scores remain at an all time low despite all the surveys and institutes that been springing up tackle this problem. Clearly something is not working. Employee engagement has become the short-term adrenaline shot that organizations turn to when they need to increase their engagement scores. Instead, we have to focus on designing employee experiences which is the long term organizational design that leads to engaged employees. This is the only long-term solution. Organizations have been stuck focusing on the cause instead of the effect. The cause is employee experience; the effect is an engaged workforce. Backed by an extensive research project that looked at over 150 studies and articles, featured extensive interviews with over 150 executives, and analyzed over 250 global organizations, this book clearly breaks down the three environments that make up every single employee experience at every organization around the world and how to design for them. These are the cultural, technological, and physical environments. This book explores the attributes that organizations need to focus on in each one of these environments to create COOL spaces, ACE technology, and a CELEBRATED culture. Featuring exclusive case studies, unique frameworks, and never before seen research, The Employee Experience Advantage guides readers on a journey of creating a place where people actually want to show up to work. Readers will learn: The trends shaping employee experience How to evaluate their own employee experience using the Employee Experience Score What the world's leading organizations are doing around employee experience How to design for technology, culture, and physical spaces The role people analytics place in employee experience Frameworks for how to actually create employee experiences The role of the gig economy The future of employee experience Nine types of organizations that focus on employee experience And much more! There is no question that engaged employees perform better, aspire higher, and achieve more, but you can't create employee engagement without designing employee experiences first. It's time to rethink your strategy and implement a real-world framework that focuses on how to create an organization where people want to show up to work. The Employee Experience Advantage shows you how to do just that. |
| data science cheat sheet: R for Business Analytics A Ohri, 2012-09-14 This book examines common tasks performed by business analysts and helps the reader navigate the wealth of information in R and its 4000 packages to create useful analytics applications. Includes interviews with corporate users of R, and easy-to-use examples. |
| data science cheat sheet: DOS Cheat Sheet Jennifer Fulton, 1995 Each section is broken into task-based lessons which cover the basic steps first, followed by more in-depth information. Essential steps are highlighted in a second color for ease of use and handwritten tips are in the margin. The first page of each lesson is a cheat sheet of the basic steps covered in that lesson for a handy reference. |
| data science cheat sheet: Deep Learning For Dummies John Paul Mueller, Luca Massaron, 2019-05-14 Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day. |
| data science cheat sheet: R and Data Mining Yanchang Zhao, 2012-12-31 R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work |
| data science cheat sheet: Data Science Field Cady, 2020-12-30 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. |
| data science cheat sheet: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
Data 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 …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their …
Data and Digital Outp…
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Da…
Jan 10, 2019 · The SEI CRA will closely link research …
Open Data Policy and P…
The data policy includes the following principles: Data …
Belmont Forum Adop…
Jan 27, 2016 · Adoption of the open data policy and principles …
Belmont Forum Da…
The DAS encourages researchers to plan for the …
