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| data science vs web development: 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 |
| data science vs web development: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data science vs web development: JavaScript Allongé Reginald Braithwaite, 2013-10-04 JavaScript Allongé solves two important problems for the ambitious JavaScript programmer. First, JavaScript Allongé gives you the tools to deal with JavaScript bugs, hitches, edge cases, and other potential pitfalls. There are plenty of good directions for how to write JavaScript programs. If you follow them without alteration or deviation, you will be satisfied. Unfortunately, software is a complex thing, full of interactions and side-effects. Two perfectly reasonable pieces of advice when taken separately may conflict with each other when taken together. An approach may seem sound at the outset of a project, but need to be revised when new requirements are discovered. When you “leave the path” of the directions, you discover their limitations. In order to solve the problems that occur at the edges, in order to adapt and deal with changes, in order to refactor and rewrite as needed, you need to understand the underlying principles of the JavaScript programming language in detail. You need to understand why the directions work so that you can understand how to modify them to work properly at or beyond their original limitations. That’s where JavaScript Allongé comes in. JavaScript Allongé is a book about programming with functions, because JavaScript is a programming language built on flexible and powerful functions. JavaScript Allongé begins at the beginning, with values and expressions, and builds from there to discuss types, identity, functions, closures, scopes, and many more subjects up to working with classes and instances. In each case, JavaScript Allongé takes care to explain exactly how things work so that when you encounter a problem, you’ll know exactly what is happening and how to fix it. Second, JavaScript Allongé provides recipes for using functions to write software that is simpler, cleaner, and less complicated than alternative approaches that are object-centric or code-centric. JavaScript idioms like function combinators and decorators leverage JavaScript’s power to make code easier to read, modify, debug and refactor, thus avoiding problems before they happen. JavaScript Allongé teaches you how to handle complex code, and it also teaches you how to simplify code without dumbing it down. As a result, JavaScript Allongé is a rich read releasing many of JavaScript’s subtleties, much like the Café Allongé beloved by coffee enthusiasts everywhere. License: CC BY-SA 3.0 Source is available from Github * https://github.com/justinkelly/javascript-allonge |
| data science vs web development: Python for Data Science Erick Thompson, 2020-10-30 |
| data science vs web development: Getting Started with Streamlit for Data Science Tyler Richards, 2021-08-20 Create, deploy, and test your Python applications, analyses, and models with ease using Streamlit Key Features Learn how to showcase machine learning models in a Streamlit application effectively and efficiently Become an expert Streamlit creator by getting hands-on with complex application creation Discover how Streamlit enables you to create and deploy apps effortlessly Book DescriptionStreamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.What you will learn Set up your first development environment and create a basic Streamlit app from scratch Explore methods for uploading, downloading, and manipulating data in Streamlit apps Create dynamic visualizations in Streamlit using built-in and imported Python libraries Discover strategies for creating and deploying machine learning models in Streamlit Use Streamlit sharing for one-click deployment Beautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebar Implement best practices for prototyping your data science work with Streamlit Who this book is for This book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you’re a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered. |
| data science vs web development: Machine Learning for Hackers Drew Conway, John Myles White, 2012-02-13 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data |
| data science vs web development: 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. |
| data science vs web development: R Programming for Data Science Roger D. Peng, 2012-04-19 Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox. |
| data science vs web development: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science vs web development: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data science vs web development: Web Design with HTML, CSS, JavaScript and jQuery Set Jon Duckett, 2014-09-29 A two-book set for web designers and front-end developers This two-book set combines the titles HTML & CSS: Designing and Building Web Sites and JavaScript & jQuery: Interactive Front-End Development. Together these two books form an ideal platform for anyone who wants to master HTML and CSS before stepping up to JavaScript and jQuery. HTML & CSS covers structure, text, links, images, tables, forms, useful options, adding style with CSS, fonts, colors, thinking in boxes, styling lists and tables, layouts, grids, and even SEO, Google analytics, ftp, and HTML5. JavaScript & jQuery offers an excellent combined introduction to these two technologies using a clear and simple visual approach using diagrams, infographics, and photographs. A handy two-book set that uniquely combines related technologies Highly visual format and accessible language makes these books highly effective learning tools Perfect for beginning web designers and front-end developers |
| data science vs web development: Introduction to Data Science Laura Igual, Santi Seguí, 2017-02-22 This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website. |
| data science vs web development: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science vs web development: Elements of Programming Interviews Adnan Aziz, Tsung-Hsien Lee, Amit Prakash, 2012 The core of EPI is a collection of over 300 problems with detailed solutions, including 100 figures, 250 tested programs, and 150 variants. The problems are representative of questions asked at the leading software companies. The book begins with a summary of the nontechnical aspects of interviewing, such as common mistakes, strategies for a great interview, perspectives from the other side of the table, tips on negotiating the best offer, and a guide to the best ways to use EPI. The technical core of EPI is a sequence of chapters on basic and advanced data structures, searching, sorting, broad algorithmic principles, concurrency, and system design. Each chapter consists of a brief review, followed by a broad and thought-provoking series of problems. We include a summary of data structure, algorithm, and problem solving patterns. |
| data science vs web development: Conceptual Statistics for Beginners Isadore Newman, Carole Newman, 1994 This new edition emphasizes and facilitates the conceptual understanding of statistics and statistical concepts for the purpose of reading and accurately interpreting research literature. The use of hand calculators is deemphasized. Instead, computer example setups are supplied for SPSS and SAS. |
| data science vs web development: 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 science vs web development: 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 vs web development: Data Science Live Book Pablo Casas, 2018-03-16 This book is a practical guide to problems that commonly arise when developing a machine learning project. The book's topics are: Exploratory data analysis Data Preparation Selecting best variables Assessing Model Performance More information on predictive modeling will be included soon. This book tries to demonstrate what it says with short and well-explained examples. This is valid for both theoretical and practical aspects (through comments in the code). This book, as well as the development of a data project, is not linear. The chapters are related among them. For example, the missing values chapter can lead to the cardinality reduction in categorical variables. Or you can read the data type chapter and then change the way you deal with missing values. You¿ll find references to other websites so you can expand your study, this book is just another step in the learning journey. It's open-source and can be found at http://livebook.datascienceheroes.com |
| data science vs web development: Learning Java Patrick Niemeyer, Jonathan Knudsen, 2002 This updated edition introduces the basics of Java and everything necessary to get up to speed on the new 1.4 version quickly. CD contains the Java 2 SDK for Windows, Linux and Solaris. |
| data science vs web development: Learn Python 3 the Hard Way Zed A. Shaw, 2017-06-26 You Will Learn Python 3! Zed Shaw has perfected the world’s best system for learning Python 3. Follow it and you will succeed—just like the millions of beginners Zed has taught to date! You bring the discipline, commitment, and persistence; the author supplies everything else. In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Variables Strings and text Interact with users Work with files Looping and logic Data structures using lists and dictionaries Program design Object-oriented programming Inheritance and composition Modules, classes, and objects Python packaging Automated testing Basic game development Basic web development It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer. This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven’t written code in years Seasoned professionals looking for a fast, simple, crash course in Python 3 |
| data science vs web development: Web App Development and Real-Time Web Analytics with Python Tshepo Chris Nokeri, 2021-11-20 Learn to develop and deploy dashboards as web apps using the Python programming language, and how to integrate algorithms into web apps. Author Tshepo Chris Nokeri begins by introducing you to the basics of constructing and styling static and interactive charts and tables before exploring the basics of HTML, CSS, and Bootstrap, including an approach to building web pages with HTML. From there, he’ll show you the key Python web frameworks and techniques for building web apps with them. You’ll then see how to style web apps and incorporate themes, including interactive charts and tables to build dashboards, followed by a walkthrough of creating URL routes and securing web apps. You’ll then progress to more advanced topics, like building machine learning algorithms and integrating them into a web app. The book concludes with a demonstration of how to deploy web apps in prevalent cloud platforms. Web App Development and Real-Time Web Analytics with Python is ideal for intermediate data scientists, machine learning engineers, and web developers, who have little or no knowledge about building web apps that implement bootstrap technologies. After completing this book, you will have the knowledge necessary to create added value for your organization, as you will understand how to link front-end and back-end development, including machine learning. What You Will Learn Create interactive graphs and render static graphs into interactive ones Understand the essentials of HTML, CSS, and Bootstrap Gain insight into the key Python web frameworks, and how to develop web applications using them Develop machine learning algorithms and integrate them into web apps Secure web apps and deploy them to cloud platforms Who This Book Is For Intermediate data scientists, machine learning engineers, and web developers. |
| data science vs web development: Data Science and Analytics Dr.Venkateswara Rao Gera, Dr.Padamata Ramesh Babu, Dr.Kalyankumar Dasari, Dr.Shaik Mohammed Jany, 2024-09-07 Dr.Venkateswara Rao Gera, Professor, Department of Computer Science and Engineering, Kallam Haranadhareddy Institute of Technology, NH-16, Chowdavaram, Guntur, (D.T), Andhra Pradesh, India. Dr.Padamata Ramesh Babu, Associate Professor, Department of Computer Science and Engineering – Data Science, Bapatla Engineering College, Bapatla (D.T), Andhra Pradesh, India. Dr.Kalyankumar Dasari, Associate Professor & Head, Department of Computer Science and Engineering - Cyber Security, Chalapathi Institute of Technology, A.R.Nagar, Mothadaka, Guntur (D.T), Andhra Pradesh, India. Dr.Shaik Mohammed Jany, Associate Professor, Department of Information Technology and CSE (AI), Narasaraopeta Engineering College, Narasaraopeta, Palnadu (D.T), Andhra Pradesh, India. |
| data science vs web development: Business Statistics for Contemporary Decision Making Ignacio Castillo, Ken Black, Tiffany Bayley, 2023-05-08 Show students why business statistics is an increasingly important business skill through a student-friendly pedagogy. In this fourth Canadian edition of Business Statistics For Contemporary Decision Making authors Ken Black, Tiffany Bayley, and Ignacio Castillo uses current real-world data to equip students with the business analytics techniques and quantitative decision-making skills required to make smart decisions in today's workplace. |
| data science vs web development: Design Patterns Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, 1995 Software -- Software Engineering. |
| data science vs web development: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| data science vs web development: Programming Interviews Exposed John Mongan, Noah Suojanen Kindler, Eric Giguère, 2011-08-10 The pressure is on during the interview process but with the right preparation, you can walk away with your dream job. This classic book uncovers what interviews are really like at America's top software and computer companies and provides you with the tools to succeed in any situation. The authors take you step-by-step through new problems and complex brainteasers they were asked during recent technical interviews. 50 interview scenarios are presented along with in-depth analysis of the possible solutions. The problem-solving process is clearly illustrated so you'll be able to easily apply what you've learned during crunch time. You'll also find expert tips on what questions to ask, how to approach a problem, and how to recover if you become stuck. All of this will help you ace the interview and get the job you want. What you will learn from this book Tips for effectively completing the job application Ways to prepare for the entire programming interview process How to find the kind of programming job that fits you best Strategies for choosing a solution and what your approach says about you How to improve your interviewing skills so that you can respond to any question or situation Techniques for solving knowledge-based problems, logic puzzles, and programming problems Who this book is for This book is for programmers and developers applying for jobs in the software industry or in IT departments of major corporations. Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved. |
| data science vs web development: Django: Web Development with Python Samuel Dauzon, Aidas Bendoraitis, Arun Ravindran, 2016-08-31 From an idea to a prototype – a complete guide for web development with the Django framework About This Book Explore the best practices to develop applications of a superior quality with Django framework Unravel the common problems of web development in Django This course teaches you major Django functions and will help you improve your skills by developing models, forms, views, and templates Experience the challenges of working on an end-to-end social network project Who This Book Is For Web developers who want to use modern Python-based web frameworks like Django to build powerful web applications. The course is mostly self-contained and introduces web development with Python to a reader who is familiar with web development concepts and can help him become an expert in this trade. It's intended for all levels of web developers, both students and practitioners from novice to experts. What You Will Learn Use Django models to store information in the database and generate queries to access a database across models Quickly develop web pages to create, read, update, and delete data from the model using class-based views Generate very maintainable forms with Django Import data from local sources and external web services as well as exporting your data to third parties Deep dive into various aspects of Django from models and views to testing and deployment Familiarize yourself with the various nuances of web development such as browser attacks and databases In Detail Data science is hot right now, and the need for multitalented developers is greater than ever before. A basic grounding in building apps with a framework as minimalistic, powerful, and easy-to-learn as Django will be a useful skill to launch your career as an entrepreneur or web developer. Django is a web framework that was designed to strike a balance between rapid web development and high performance. This course will take you on a journey to become an efficient web developer thoroughly understanding the key concepts of Django framework. This learning path is divided into three modules. The course begins with basic concepts of the Django framework. The first module, Django Essentials, is like a practical guide, filled with many real-world examples to build highly effective Django web application. After getting familiar with core concepts of Django, it's time to practice your learning from the first module with the help of over 90 recipes available in this module. In the second module, Web Development with Django Cookbook, you'll learn varying complexities to help you create multilingual, responsive, and scalable websites with Django. By the end of this module, you will have a good understanding of the new features added to Django 1.8 and be an expert at web development processes.The next step is to discover the latest best practices and idioms in this rapidly evolving Django framework. This is what you'll be learning in our third module, Django Design Patterns and Best Practices. This module will teach you common design patterns to develop better Django code. By the end of the module, you will be able to leverage the Django framework to develop a fully functional web application with minimal effort. Style and approach This course includes all the resources that will help you jump into the web development field with Django and learn how to make scalable and robust web applications. The aim is to create a smooth learning path that will teach you how to get started with the powerful Django framework and perform various web development techniques in depth. Through this comprehensive course, you'll learn web development with Django from scratch to finish! |
| data science vs web development: The Comprehensive Guide to Website Design, Web Development, and Web Marketing Ron Legarski, Ned Hamzic, 2024-09-08 The Comprehensive Guide to Website Design, Web Development, and Web Marketing: Online & Offline Strategies, Programming, Software, Devices, and Applications is an essential resource for mastering the digital world. Co-authored by industry leaders Ron Legarski and Ned Hamzic, this book covers every aspect of website creation, development, and marketing. From the fundamentals of coding to the latest in digital marketing trends, this guide is designed to provide readers with actionable insights and practical strategies. Whether you're a web developer, designer, marketer, or business owner looking to enhance your online presence, this guide delves deep into essential topics such as: Web design principles, including UX/UI, responsive design, and visual hierarchy. Web development using HTML5, CSS, JavaScript, and backend technologies like PHP and MySQL. Comprehensive digital marketing strategies, including SEO, SEM, social media, and email marketing. Mobile-first design and emerging technologies such as AI, IoT, and blockchain. Online and offline marketing integration for holistic business growth. The book also includes insights into cloud services, web hosting, and security practices, ensuring that your website is not only functional but also scalable and secure. With their combined expertise, Ron Legarski and Ned Hamzic offer a complete guide for anyone looking to navigate the complexities of website design, development, and marketing, making this book a valuable resource for both beginners and seasoned professionals. |
| data science vs web development: A Tour of Data Science Nailong Zhang, 2020-11-11 A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective. |
| data science vs web development: LEARN ALL ABOUT PROGRAMMING Marcel Souza, Embark on an exciting journey into the world of programming with Learn All About Programming. Whether you're a complete beginner or looking to enhance your coding skills, this comprehensive guide is your ultimate companion to mastering the art of programming. Explore the fundamental concepts of coding, from basic syntax to complex algorithms. This book serves as your roadmap, guiding you through various programming languages, including Python, Java, C#, and SQL. Unlock the ability to create dynamic websites, streamline data processing, and develop user-friendly applications that leave a lasting impact. But the adventure doesn't end there – delve into advanced techniques that push the boundaries of your programming expertise. Dive into modern concepts like machine learning, artificial intelligence, and data analysis, and discover how to transform raw data into valuable insights. Whether you're a coding newbie or an experienced enthusiast, Learn All About Programming equips you with the tools to turn your ideas into functional code. Don't miss out on this opportunity to become a programming virtuoso. Grab your copy now and immerse yourself in the captivating world of programming. Your journey to coding excellence starts here! |
| data science vs web development: The Dictionary of Artificial Intelligence Utku Taşova, 2023-11-03 Unveiling the Future: Your Portal to Artificial Intelligence Proficiency In the epoch of digital metamorphosis, Artificial Intelligence (AI) stands as the vanguard of a new dawn, a nexus where human ingenuity intertwines with machine precision. As we delve deeper into this uncharted realm, the boundary between the conceivable and the fantastical continually blurs, heralding a new era of endless possibilities. The Dictionary of Artificial Intelligence, embracing a compendium of 3,300 meticulously curated titles, endeavors to be the torchbearer in this journey of discovery, offering a wellspring of knowledge to both the uninitiated and the adept. Embarking on the pages of this dictionary is akin to embarking on a voyage through the vast and often turbulent seas of AI. Each entry serves as a beacon, illuminating complex terminologies, core principles, and the avant-garde advancements that characterize this dynamic domain. The dictionary is more than a mere compilation of terms; it's a labyrinth of understanding waiting to be traversed. The Dictionary of Artificial Intelligence is an endeavor to demystify the arcane, to foster a shared lexicon that enhances collaboration, innovation, and comprehension across the AI community. It's a mission to bridge the chasm between ignorance and insight, to unravel the intricacies of AI that often seem enigmatic to the outsiders. This profound reference material transcends being a passive repository of terms; it’s an engagement with the multifaceted domain of artificial intelligence. Each title encapsulated within these pages is a testament to the audacity of human curiosity and the unyielding quest for advancement that propels the AI domain forward. The Dictionary of Artificial Intelligence is an invitation to delve deeper, to grapple with the lexicon of a field that stands at the cusp of redefining the very fabric of society. It's a conduit through which the curious become enlightened, the proficient become masters, and the innovators find inspiration. As you traverse through the entries of The Dictionary of Artificial Intelligence, you are embarking on a journey of discovery. A journey that not only augments your understanding but also ignites the spark of curiosity and the drive for innovation that are quintessential in navigating the realms of AI. We beckon you to commence this educational expedition, to explore the breadth and depth of AI lexicon, and to emerge with a boundless understanding and an unyielding resolve to contribute to the ever-evolving narrative of artificial intelligence. Through The Dictionary of Artificial Intelligence, may your quest for knowledge be as boundless and exhilarating as the domain it explores. |
| data science vs web development: The Golden Book of Python 2024 Edition Diego Rodrigues, 2024-11-09 Discover the power of Python with The Golden Book of Python: From Secrets to Advanced Applications. This essential book, written by renowned expert Diego Rodrigues, is a comprehensive guide for students and professionals who want to master Python from the basics to advanced applications. Ideal for those seeking a deep and practical understanding of the language, the book covers a wide range of topics, ensuring no aspect is left out. You will begin your journey with a detailed introduction to the importance of Python in today's world, its history, and evolution. As you progress, each chapter offers an in-depth dive into essential areas such as operators, expressions, functions, modules, and advanced data structures. Additionally, the book explores object-oriented programming, file manipulation, web development, data science, automation, game development, networking, connectivity, and much more. With a focus on advanced tips and secrets, you will learn techniques that even specialists rarely know. Discover how to use Python for game development with Pygame, network communication with sockets and asyncio, and desktop application development with Tkinter and PyQt. Learn to integrate Python with other languages like C/C++, Java, and .NET, and apply advanced optimization and performance techniques. The Golden Book of Python is not just a technical manual; it is a companion that guides you through the challenges and opportunities of Python programming. Filled with practical examples, challenges, and additional resources, this book is designed to ensure you apply your knowledge to real-world projects. Take advantage of the promotional launch value and get your copy of The Golden Book of Python: From Secrets to Advanced Applications now. Don't miss the chance to transform your career with this indispensable resource. Buy now and start your journey towards mastering Python! 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| data science vs web development: Urban Analytics Alex D. Singleton, Seth Spielman, David Folch, 2017-11-27 The economic and political situation of cities has shifted in recent years in light of rapid growth amidst infrastructure decline, the suburbanization of poverty and inner city revitalization. At the same time, the way that data are used to understand urban systems has changed dramatically. Urban Analytics offers a field-defining look at the challenges and opportunities of using new and emerging data to study contemporary and future cities through methods including GIS, Remote Sensing, Big Data and Geodemographics. Written in an accessible style and packed with illustrations and interviews from key urban analysts, this is a groundbreaking new textbook for students of urban planning, urban design, geography, and the information sciences. |
| data science vs web development: Visual Studio 2022 In-Depth Ockert J. du Preez, 2022-09-29 Develop Intelligent Hybrid and Cross-platform Applications with Powerful Coding Assistance for Developers DESCRIPTION Visual Studio 2022 is the most professional and sophisticated IDE for.NET and C# developers. This book provides comprehensive coverage of Visual Studio 2022, including all the tools and capabilities you can use to improve and streamline the software development process. Author Ockert J. du Preez takes you under the scenes of the Visual Studio IDE and guides you through the newest 2022 version in this revised and enlarged second edition. You will discover new areas of the application and the best way to utilize the capabilities you already know as soon as you install it. The following are some of the essential elements of this newer edition: ● Create cross-platform mobile and desktop applications using.NET MAUI. ● Utilize IntelliCode, an AI-powered code completion tool, for next-generation developer productivity. ● Work with the Razor Editor and troubleshoot problems with visualizations. ● Build, test, and debug.NET, C#, and C++ applications. ● Enhance coding skills to create effective web, mobile, and Azure solutions. ● Get Visual Studio 2022 rolling on Linux and macOS systems. ● Create and use custom IDE extensions. The book exemplifies topics such as extending Visual Studio with your customizations to make it function as you desire effectively. The book will help you learn everything you need to know about.NET 6, diagnosing and debugging programs, and utilizing all the collaborative tools in Visual Studio 2022. This book will teach you how to use the brand new AI IntelliCode and implement .NET MAUI and C++ 20 features. WHO THIS BOOK IS FOR This is the book for you, developers, and programmers who wish to master Visual Studio and produce high-quality code in any deployment setting and for any application. You only need to be familiar with.NET and C#. Nothing else is required. TABLE OF CONTENTS Section - I: Getting to Know the Visual Studio 2022 IDE 1. Getting Started with Visual Studio 2022 2. Having a Look at .NET 6 3. Language and Coding Changes in C# 4. Digging into the Visual Studio 2022 IDE Section - II: Using The Tools in Visual Studio 2022 5. AI IntelliCode 6. Built-in Coding Experience Tools 7. Diagnostics and Debugging Tools Section - III: Advanced Tools 8. Web Tools and Extensions 9. Mobile Tools 10. Azure Tools Section - IV: Advanced Topics 11. C++ 20 Appendix 'A' |
| data science vs web development: Data Science for Everyone Fatih AKAY, 2023-03-20 Data Science for Everyone: A Beginner's Guide to Big Data and Analytics is a comprehensive guide for anyone interested in exploring the field of data science. Written in a user-friendly style, this book is designed to be accessible to readers with no prior background in data science. The book covers the fundamentals of data science and analytics, including data collection, data analysis, and data visualization. It also provides an overview of the most commonly used tools and techniques for working with big data. The book begins with an introduction to data science and its applications, followed by an overview of the different types of data and the challenges of working with them. The subsequent chapters delve into the main topics of data science, such as data exploration, data cleaning, data modeling, and data visualization, providing step-by-step instructions and practical examples to help readers master each topic. Throughout the book, the authors emphasize the importance of data ethics and responsible data management. They also cover the basics of machine learning, artificial intelligence, and deep learning, and their applications in data science. By the end of this book, readers will have a solid understanding of the key concepts and techniques used in data science, and will be able to apply them to real-world problems. Whether you are a student, a professional, or simply someone interested in the field of data science, this book is an essential resource for learning about the power and potential of big data and analytics. |
| data science vs web development: Getting a Coding Job For Dummies Nikhil Abraham, 2015-07-20 Your friendly guide to getting a job in coding Getting a Coding Job For Dummies explains how a coder works in (or out of) an organization, the key skills any job requires, the basics of the technologies a coding pro will encounter, and how to find formal or informal ways to build your skills. Plus, it paints a picture of the world a coder lives in, outlines how to build a resume to land a coding job, and so much more. Coding is one of the most in-demand skills in today's job market, yet there seems to be an ongoing deficit of candidates qualified to take these jobs. Getting a Coding Job For Dummies provides a road map for students, post-grads, career switchers, and anyone else interested in starting a career in coding. Inside this friendly guide, you'll find the steps needed to learn the hard and soft skills of coding—and the world of programming at large. Along the way, you'll set a clear career path based on your goals and discover the resources that can best help you build your coding skills to make you a suitable job candidate. Covers the breadth of job opportunities as a coder Includes tips on educational resources for coders and ways to build a positive reputation Shows you how to research potential employers and impress interviewers Offers access to online video, articles, and sample resume templates If you're interested in pursuing a job in coding, but don't know the best way to get there, Getting a Coding Job For Dummies is your compass! |
| data science vs web development: Data Science and Analytics (with Python, R and SPSS Programming) V.K. Jain, The Book has been written completely as per AICTE recommended syllabus on Data Sciences. SALIENT FEATURES OF THE BOOK: Explains how data is collected, managed and stored for data science. With complete courseware for understand the key concepts in data science including their real-world applications and the toolkit used by data scientists. Implement data collection and management. Provided with state of the arts subjectwise. With all required tutorials on R, Python and Bokeh, Anaconda, IBM SPSS-21 and Matplotlib. |
| data science vs web development: Architectural Framework for Web Development and Micro Distributed Applications Rodriguez, Guillermo, 2023-04-17 In recent years, we have ushered in a new age where applications will become smaller, distributed, JavaScript-laden, microservices-infused, and utilize the hardware of the client to operate. A new paradigm has been forced upon us by the large search providers, and because of this, we can now leverage them to help our applications obtain influence where our applications become the voice of authority on the internet and consequently help our organizations reap the benefits of mass adoption. To better understand this, we must first consider the history that has taken us to where we find ourselves. Architectural Framework for Web Development and Micro Distributed Applications helps readers to come to an understanding of how the indexing domain may be leveraged by this new wave of JavaScript applications that have been termed micro distributed applications and by whose creation and implementation will allow the enterprise to reap the benefit of influence by the existing search systems that the masses utilize. It helps to fill in the picture of the evolution that has occurred and will continue to occur in web development whereby the new breed of applications will become JavaScript-laden and highly distributed and whereby the businesses that implement them will stand a chance to win the indexing race and consequently stand to win the attention of the masses. Covering topics such as distributed systems, search engine optimization, and software as a service, this premier reference source is a dynamic resource for web developers, students and educators of higher education, software developers, technical personnel, IT managers, computer scientists, librarians, researchers, and academicians. |
| data science vs web development: Practical Programming Paul Gries, Jennifer Campbell, Jason Montojo, 2013 Previous edition: published as by Jennifer Campbell ... [et al]. 2009. |
| data science vs web development: Python Programming – A Modern Approach Dr. J. Jayaganesh, Mr. Yeddula Bhaskar Reddy, Mr. S. Muthurajan, Mrs. Amita Ravi, 2024-06-28 Python Programming – A Modern Approach that introduces readers to Python, emphasizing clarity and practical application. Designed for both beginners and seasoned programmers, the book covers fundamental concepts, advanced programming techniques, and best practices in Python. It includes hands-on examples, problem-solving strategies, and real-world applications, making it a valuable resource for mastering Python. This modern approach to learning Python prepares readers for both academic study and professional software development, fostering a deep understanding of the language and its versatility in diverse programming domains. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
