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| freelance data science reddit: Bayesian Inference in Statistical Analysis George E. P. Box, George C. Tiao, 2011-01-25 Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach. |
| freelance data science reddit: Engineering Production-grade Shiny Apps Colin Fay, Vincent Guyader, Sebastien Rochette, Girard Cervan, 2021 Presented in full color, Engineering Production-Grade Shiny Apps helps people build production-grade shiny applications, by providing advice, tools, and a methodology to work on web applications with R. This book starts with an overview of the challenges which arise from any big web application project: organizing work, thinking about the user interface, challenges of teamwork & production environment. Then, it moves to a step by step methodology that goes from the idea to the end application. Each part of this process will cover in detail a series of tools and methods to use while building production-ready shiny applications. Finally, the book will end with a series of approaches and advice about optimizations for production-- |
| freelance data science reddit: Data Sketches Nadieh Bremer, Shirley Wu, 2021-02-09 In Data Sketches, Nadieh Bremer and Shirley Wu document the deeply creative process behind 24 unique data visualization projects, and they combine this with powerful technical insights which reveal the mindset behind coding creatively. Exploring 12 different themes – from the Olympics to Presidents & Royals and from Movies to Myths & Legends – each pair of visualizations explores different technologies and forms, blurring the boundary between visualization as an exploratory tool and an artform in its own right. This beautiful book provides an intimate, behind-the-scenes account of all 24 projects and shares the authors’ personal notes and drafts every step of the way. The book features: Detailed information on data gathering, sketching, and coding data visualizations for the web, with screenshots of works-in-progress and reproductions from the authors’ notebooks Never-before-published technical write-ups, with beginner-friendly explanations of core data visualization concepts Practical lessons based on the data and design challenges overcome during each project Full-color pages, showcasing all 24 final data visualizations This book is perfect for anyone interested or working in data visualization and information design, and especially those who want to take their work to the next level and are inspired by unique and compelling data-driven storytelling. |
| freelance data science reddit: Data Pipelines Pocket Reference James Densmore, 2021-02-10 Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting |
| freelance data science reddit: Python and R for the Modern Data Scientist Rick J. Scavetta, Boyan Angelov, 2021-06-22 Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together |
| freelance data science reddit: The Miracle Morning (Updated and Expanded Edition) Hal Elrod, 2024-01-09 Start waking up to your full potential every single day with the updated and expanded edition of the groundbreaking book that has sold more than two million copies. “So much more than a book. It is a proven methodology that will help you fulfil your potential and create the life you’ve always wanted.” —Mel Robbins, New York Times bestselling author of The High 5 Habit and The 5 Second Rule Getting everything you want out of life isn’t about doing more. It’s about becoming more. Hal Elrod and The Miracle Morning have helped millions of people become the person they need to be to create the life they’ve always wanted. Now, it’s your turn. Hal’s revolutionary SAVERS method is a simple, effective step-by-step process to transform your life in as little as six minutes per day: - Silence: Reduce stress and improve mental clarity by beginning each day with peaceful, purposeful quiet - Affirmations: Reprogram your mind to overcome any fears or beliefs that are limiting your potential or causing you to suffer - Visualization: Experience the power of mentally rehearsing yourself showing up at your best each day - Exercise: Boost your mental and physical energy in as little as sixty seconds - Reading: Acquire knowledge and expand your abilities by learning from experts - Scribing: Keep a journal to deepen gratitude, gain insights, track progress, and increase your productivity by getting clear on your top priorities This updated and expanded edition has more than forty pages of new content, including: - The Miracle Evening: Optimize your bedtime and sleep to wake up every day feeling refreshed and energized for your Miracle Morning - The Miracle Life: Begin your path to inner freedom so you can truly be happy and learn to love the life you have while you create the life you want |
| freelance data science reddit: Endangered Minds Jane M. Healy, 2011-07-19 Is today's fast-paced media culture creating a toxic environment for our children's brains? In this landmark, bestselling assessment tracing the roots of America's escalating crisis in education, Jane M. Healy, Ph.D., examines how television, video games, and other components of popular culture compromise our children's ability to concentrate and to absorb and analyze information. Drawing on neuropsychological research and an analysis of current educational practices, Healy presents in clear, understandable language: -- How growing brains are physically shaped by experience -- Why television programs -- even supposedly educational shows like Sesame Street -- develop habits of mind that place children at a disadvantage in school -- Why increasing numbers of children are diagnosed with attention deficit disorder -- How parents and teachers can make a critical difference by making children good learners from the day they are born |
| freelance data science reddit: Trace Lauret Savoy, 2015-11-01 With a New Preface by the Author Through personal journeys and historical inquiry, this PEN Literary Award finalist explores how America’s still unfolding history and ideas of “race” have marked its people and the land. Sand and stone are Earth’s fragmented memory. Each of us, too, is a landscape inscribed by memory and loss. One life–defining lesson Lauret Savoy learned as a young girl was this: the American land did not hate. As an educator and Earth historian, she has tracked the continent’s past from the relics of deep time; but the paths of ancestors toward her—paths of free and enslaved Africans, colonists from Europe, and peoples indigenous to this land—lie largely eroded and lost. A provocative and powerful mosaic that ranges across a continent and across time, from twisted terrain within the San Andreas Fault zone to a South Carolina plantation, from national parks to burial grounds, from “Indian Territory” and the U.S.–Mexico Border to the U.S. capital, Trace grapples with a searing national history to reveal the often unvoiced presence of the past. In distinctive and illuminating prose that is attentive to the rhythms of language and landscapes, she weaves together human stories of migration, silence, and displacement, as epic as the continent they survey, with uplifted mountains, braided streams, and eroded canyons. Gifted with this manifold vision, and graced by a scientific and lyrical diligence, she delves through fragmented histories—natural, personal, cultural—to find shadowy outlines of other stories of place in America. Every landscape is an accumulation, reads one epigraph. Life must be lived amidst that which was made before. Courageously and masterfully, Lauret Savoy does so in this beautiful book: she lives there, making sense of this land and its troubled past, reconciling what it means to inhabit terrains of memory—and to be one. |
| freelance data science reddit: 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 |
| freelance data science reddit: Scientific Computing Michael T. Heath, 2018-11-14 This book differs from traditional numerical analysis texts in that it focuses on the motivation and ideas behind the algorithms presented rather than on detailed analyses of them. It presents a broad overview of methods and software for solving mathematical problems arising in computational modeling and data analysis, including proper problem formulation, selection of effective solution algorithms, and interpretation of results.? In the 20 years since its original publication, the modern, fundamental perspective of this book has aged well, and it continues to be used in the classroom. This Classics edition has been updated to include pointers to Python software and the Chebfun package, expansions on barycentric formulation for Lagrange polynomial interpretation and stochastic methods, and the availability of about 100 interactive educational modules that dynamically illustrate the concepts and algorithms in the book. Scientific Computing: An Introductory Survey, Second Edition is intended as both a textbook and a reference for computationally oriented disciplines that need to solve mathematical problems. |
| freelance data science reddit: A Curious Moon Rob Conery, 2020-12-13 Starting an application is simple enough, whether you use migrations, a model-synchronizer or good old-fashioned hand-rolled SQL. A year from now, however, when your app has grown and you're trying to measure what's happened... the story can quickly change when data is overwhelming you and you need to make sense of what's been accumulating. Learning how PostgreSQL works is just one aspect of working with data. PostgreSQL is there to enable, enhance and extend what you do as a developer/DBA. And just like any tool in your toolbox, it can help you create crap, slice off some fingers, or help you be the superstar that you are.That's the perspective of A Curious Moon - data is the truth, data is your friend, data is your business. The tools you use (namely PostgreSQL) are simply there to safeguard your treasure and help you understand what it's telling you.But what does it mean to be data-minded? How do you even get started? These are good questions and ones I struggled with when outlining this book. I quickly realized that the only way you could truly understand the power and necessity of solid databsae design was to live the life of a new DBA... thrown into the fire like we all were at some point...Meet Dee Yan, our fictional intern at Red:4 Aerospace. She's just been handed the keys to a massive set of data, straight from Saturn, and she has to load it up, evaluate it and then analyze it for a critical project. She knows that PostgreSQL exists... but that's about it.Much more than a tutorial, this book has a narrative element to it a bit like The Martian, where you get to know Dee and the problems she faces as a new developer/DBA... and how she solves them.The truth is in the data... |
| freelance data science reddit: 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. |
| freelance data science reddit: Sustainable Development in Practice Adisa Azapagic, Slobodan Perdan, Roland Clift, 2004-07-23 This groundbreaking text provides background theory on the concept of sustainable development (environmental, social and economic aspects) and presents a series of practical case studies on such topics as waste water management, air quality, solid waste management and renewable energy. |
| freelance data science reddit: Programming Skills For Data Science Freeman, Programming Skills for Data Science brings together all the foundation skills needed to transform raw data into actionable insights for domains ranging from urban planning to precision medicine, even if you have no programming or data science experience. Guided by expert instructors Michael Freeman and Joel Ross, this book will help learners install the tools required to solve professional-level data science problems, including widely used R language, RStudio integrated development environment, and Git version-control system. It explains how to wrangle data into a form where it can be easily used, analyzed, and visualized so others can see the patterns uncovered. Step by step, students will master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. |
| freelance data science reddit: Introductory Relational Database Design for Business, with Microsoft Access Jonathan Eckstein, Bonnie R. Schultz, 2018-01-16 A hands-on beginner’s guide to designing relational databases and managing data using Microsoft Access Relational databases represent one of the most enduring and pervasive forms of information technology. Yet most texts covering relational database design assume an extensive, sophisticated computer science background. There are texts on relational database software tools like Microsoft Access that assume less background, but they focus primarily on details of the user interface, with inadequate coverage of the underlying design issues of how to structure databases. Growing out of Professor Jonathan Eckstein’s twenty years’ experience teaching courses on management information systems (MIS) at Rutgers Business School, this book fills this gap in the literature by providing a rigorous introduction to relational databases for readers without prior computer science or programming experience. Relational Database Design for Business, with Microsoft Access helps readers to quickly develop a thorough, practical understanding of relational database design. It takes a step-by-step, real-world approach, using application examples from business and finance every step the way. As a result, readers learn to think concretely about database design and how to address issues that commonly arise when developing and manipulating relational databases. By the time they finish the final chapter, students will have the knowledge and skills needed to build relational databases with dozens of tables. They will also be able to build complete Microsoft Access applications around such databases. This text: Takes a hands-on approach using numerous real-world examples drawn from the worlds of business, finance, and more Gets readers up and running, fast, with the skills they need to use and develop relational databases with Microsoft Access Moves swiftly from conceptual fundamentals to advanced design techniques Leads readers step-by-step through data management and design, relational database theory, multiple tables and the possible relationships between them, Microsoft Access features such as forms and navigation, formulating queries in SQL, and normalization Introductory Relational Database Design for Business, with MicrosoftAccess is the definitive guide for undergraduate and graduate students in business, finance, and data analysis without prior experience in database design. While Microsoft Access is its primary “hands-on” learning vehicle, most of the skills in this text are transferrable to other relational database software such as MySQL. |
| freelance data science reddit: Migration in European History Klaus Bade, 2008-04-15 Since the fall of the Iron Curtain, migration has become a major cause for concern in many European countries, but migrations to, from and within Europe are nothing new, as Klaus Bade reminds us in this timely history. A history of migration to, from and within Europe over a range of eras, countries and migration types. Examines the driving forces and currents of migration, their effects on the cultures of both migrants and host populations, including migration policies. Focuses on the nineteenth and twentieth centuries, particularly the period from the Second World War to the present. Illuminates concerns about migration in Europe today. Acts as a corrective to the alarmist reactions of host populations in twenty-first century Europe. |
| freelance data science reddit: Data Reduction and Error Analysis for the Physical Sciences Philip R. Bevington, D. Keith Robinson, 1992 This book is designed as a laboratory companion, student textbook or reference book for professional scientists. The text is for use in one-term numerical analysis, data and error analysis, or computer methods courses, or for laboratory use. It is for the sophomore-junior level, and calculus is a prerequisite. The new edition includes applications for PC use. |
| freelance data science reddit: My New Roots Sarah Britton, 2015-03-31 Holistic nutritionist and highly-regarded blogger Sarah Britton presents a refreshing, straight-forward approach to balancing mind, body, and spirit through a diet made up of whole foods. Sarah Britton's approach to plant-based cuisine is about satisfaction--foods that satiate on a physical, emotional, and spiritual level. Based on her knowledge of nutrition and her love of cooking, Sarah Britton crafts recipes made from organic vegetables, fruits, whole grains, beans, lentils, nuts, and seeds. She explains how a diet based on whole foods allows the body to regulate itself, eliminating the need to count calories. My New Roots draws on the enormous appeal of Sarah Britton's blog, which strikes the perfect balance between healthy and delicious food. She is a whole food lover, a cook who makes simple accessible plant-based meals that are a pleasure to eat and a joy to make. This book takes its cues from the rhythms of the earth, showcasing 100 seasonal recipes. Sarah simmers thinly sliced celery root until it mimics pasta for Butternut Squash Lasagna, and whips up easy raw chocolate to make homemade chocolate-nut butter candy cups. Her recipes are not about sacrifice, deprivation, or labels--they are about enjoying delicious food that's also good for you. |
| freelance data science reddit: Learning Data Mining with Python Robert Layton, 2015 About This Book Learn data mining in practical terms, using a wide variety of libraries and techniques Learn how to find, manipulate, and analyze data using Python Step-by-step instructions on creating real-world applications of data mining techniques Who This Book Is For If you are a programmer who wants to get started with data mining, then this book is for you. What You Will Learn Apply data mining concepts to real-world problems Predict the outcome of sports matches based on past results Determine the author of a document based on their writing style Use APIs to download datasets from social media and other online services Find and extract good features from difficult datasets Create models that solve real-world problems Design and develop data mining applications using a variety of datasets Set up reproducible experiments and generate robust results Recommend movies, online celebrities, and news articles based on personal preferences Compute on big data, including real-time data from the Internet In Detail The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations. |
| freelance data science reddit: The United States of Anonymous Jeff Kosseff, 2022-03-15 In The United States of Anonymous, Jeff Kosseff explores how the right to anonymity has shaped American values, politics, business, security, and discourse, particularly as technology has enabled people to separate their identities from their communications. Legal and political debates surrounding online privacy often focus on the Fourth Amendment's protection against unreasonable searches and seizures, overlooking the history and future of an equally powerful privacy right: the First Amendment's protection of anonymity. The United States of Anonymous features extensive and engaging interviews with people involved in the highest profile anonymity cases, as well as with those who have benefited from, and been harmed by, anonymous communications. Through these interviews, Kosseff explores how courts have protected anonymity for decades and, likewise, how law and technology have allowed individuals to control how much, if any, identifying information is associated with their communications. From blocking laws that prevent Ku Klux Klan members from wearing masks to restraining Alabama officials from forcing the NAACP to disclose its membership lists, and to refusing companies' requests to unmask online critics, courts have recognized that anonymity is a vital part of our free speech protections. The United States of Anonymous weighs the tradeoffs between the right to hide identity and the harms of anonymity, concluding that we must maintain a strong, if not absolute, right to anonymous speech. |
| freelance data science reddit: Blogging All-in-One For Dummies® Susan Gunelius, 2010-05-11 A complete guide to creating and establishing your place in the blogosphere! New blogs are being launched at the rate of 175,000 a day. To stand out from the masses, bloggers need the detailed information and advice packed into this all-in-one guide. Here's what new bloggers need to get started and what experienced bloggers need to upgrade and even earn money from their blogs. Eight self-contained minibooks cover joining the blogosphere, blogging software, tools that extend your blog, marketing your blog, microblogging, making money from your blog, corporate blogging, and niche blogging. Blogging is replacing traditional media and gaining credibility; to succeed, bloggers need a greater understanding of blogging basics, tools, and techniques Eight minibooks cover getting started, software, other tools, blog marketing, microblogging (including Twitter), monetizing your blog, and corporate and niche blogging Helps new bloggers become active and productive members of the blogging community Provides vital information for both hobby bloggers and those who want to build a career around blogging Presented in the fun and friendly For Dummies style, Blogging All-in-One For Dummies is a complete reference guide to starting and maintaining a successful blog. |
| freelance data science reddit: Data Science for Beginners Alex Campbell, 2021-01-12 Do you wonder what the fascination is around data these days? How do we obtain insights from this data? Do you know what a data scientist does? What is artificial intelligence and machine learning? Are these the same as data science? What does it take to become a data scientist? If you have ever wondered about these questions, you have come to the right place!There are many resources and courses online that you can use to learn more about data science, but with so much information available, it can become overwhelming. One of the best ways to learn about data science is to understand different machine learning concepts, statistics, and artificial intelligence to help you design models to perform an analysis.This book has all the information you need to learn what data science is, and what the prerequisites are to become a data scientist. If you're a beginner or if you already have experience in data science, this book will have something for you.In this book, you will: Learn what data science is about.Discover the difference between data science and business intelligence.Explore the tools required for data science.Find out the technical and non-technical skills every data scientist must have.Figure out how to create a visualization of the data set with clear and easy examples.Get advice on developing a Predictive Model Using R.Uncover detailed applications of data science.And much more!The book has been structured with easy-to-understand sections to help you learn everything you need to know about data science. In this book you will learn about the prerequisites of data science and the skills you need to become a data scientist. So, what are you waiting for? Grab your copy of this comprehensive guide now |
| freelance data science reddit: Data Science Workflow for Beginners Alejandro Garcia, This book brings to you a simple yet effective 40 to 60 mins introduction that will clear all your doubts about Data Sience and will answer some important questions like: What is data Science ? The book explores all the initial concepts a person might want to know about the data science workflow. There’s not coding, math or statistics required to successfully understand the goals and end results of this process. This book takes you on an exclusive tour of datasets and sites to download your first datasets. Then jumps into a comprehensive and easy-to-follow data science process letting you go through 3 data visualization projects. (Python Code Understanding is Recommended for the Data Visualization projects) - 40 to 60 mins reading time. - 3 Data Visualization projects. - 10 Datasets sources. - 26 Quality datasets for your first visualizations. - Get the code and reuse in your own projects. The ebook covers: - Intro to Data Science. - The Workflow of Data Science. - Data Science and Machine Learning. - Datasets to start right away. - Data Visualization Projects. (Python Code Understanding Recommended) |
| freelance data science reddit: Software Engineering for Data Scientists Catherine Nelson, 2024-04-16 Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more |
| freelance data science reddit: Python for Data Science For Dummies John Paul Mueller, Luca Massaron, 2015-07-07 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. |
| freelance data science reddit: Software Engineering for Data Scientists Catherine Nelson, 2024-04-16 Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more |
| freelance data science reddit: Analyzing the Analyzers Harlan Harris, Sean Murphy, Marck Vaisman, 2013-06-10 Despite the excitement around data science, big data, and analytics, the ambiguity of these terms has led to poor communication between data scientists and organizations seeking their help. In this report, authors Harlan Harris, Sean Murphy, and Marck Vaisman examine their survey of several hundred data science practitioners in mid-2012, when they asked respondents how they viewed their skills, careers, and experiences with prospective employers. The results are striking. Based on the survey data, the authors found that data scientists today can be clustered into four subgroups, each with a different mix of skillsets. Their purpose is to identify a new, more precise vocabulary for data science roles, teams, and career paths. This report describes: Four data scientist clusters: Data Businesspeople, Data Creatives, Data Developers, and Data Researchers Cases in miscommunication between data scientists and organizations looking to hire Why T-shaped data scientists have an advantage in breadth and depth of skills How organizations can apply the survey results to identify, train, integrate, team up, and promote data scientists |
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