Advertisement
| django and data science: Python for Artificial Intelligence and Data Science Mr.G.Hubert, Dr.Sowmya Naik.P.T, Dr.Ambika.P.R,, Mrs.Laxmi.M.C, 2024-09-10 Mr.G.Hubert, Assistant Professor & Head, Department of Artificial Intelligence, S.I.V.E.T. College, Chennai, Tamil Nadu, India. Dr.Sowmya Naik.P.T, Professor & Head, Department of Computer Science and Engineering, City Engineering College, Bengaluru, Karnataka, India. Dr.Ambika.P.R, Professor, Department of Computer Science and Engineering, City Engineering College, Bengaluru, Karnataka, India. Mrs.Laxmi.M.C, Assistant Professor, Department of Computer Science and Engineering, City Engineering College, Bengaluru, Karnataka, India. |
| django and data science: 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. |
| django and data science: Building Data Science Applications with FastAPI Francois Voron, 2021-10-08 Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications Key FeaturesCover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injectionDevelop efficient RESTful APIs for data science with modern PythonBuild, test, and deploy high performing data science and machine learning systems with FastAPIBook Description FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you'll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you'll cover best practices relating to testing and deployment to run a high-quality and robust application. You'll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you'll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you'll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you'll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI. What you will learnExplore the basics of modern Python and async I/O programmingGet to grips with basic and advanced concepts of the FastAPI frameworkImplement a FastAPI dependency to efficiently run a machine learning modelIntegrate a simple face detection algorithm in a FastAPI backendIntegrate common Python data science libraries in a web backendDeploy a performant and reliable web backend for a data science applicationWho this book is for This Python data science book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended. |
| django and data science: Django for Professionals William S. Vincent, 2022-05-19 Completely updated for Django 4.0! Django for Professionals takes your web development skills to the next level, teaching you how to build production-ready websites with Python and Django. Once you have learned the basics of Django there is a massive gap between building simple toy apps and what it takes to build a production-ready web application suitable for deployment to thousands or even millions of users. In the book you’ll learn how to: * Build a Bookstore website from scratch * Use Docker and PostgreSQL locally to mimic production settings * Implement advanced user registration with email * Customize permissions to control user access * Write comprehensive tests * Adopt advanced security and performance improvements * Add search and file/image uploads * Deploy with confidence If you want to take advantage of all that Django has to offer, Django for Professionals is a comprehensive best practices guide to building and deploying modern websites. |
| django and data science: Numerical Python Robert Johansson, |
| django and data science: Data Science Beiji Zou, Min Li, Hongzhi Wang, Xianhua Song, Wei Xie, Zeguang Lu, 2017-09-15 This two volume set (CCIS 727 and 728) constitutes the refereed proceedings of the Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017 (originally ICYCSEE) held in Changsha, China, in September 2017. The 112 revised full papers presented in these two volumes were carefully reviewed and selected from 987 submissions. The papers cover a wide range of topics related to Basic Theory and Techniques for Data Science including Mathematical Issues in Data Science, Computational Theory for Data Science, Big Data Management and Applications, Data Quality and Data Preparation, Evaluation and Measurement in Data Science, Data Visualization, Big Data Mining and Knowledge Management, Infrastructure for Data Science, Machine Learning for Data Science, Data Security and Privacy, Applications of Data Science, Case Study of Data Science, Multimedia Data Management and Analysis, Data-driven Scientific Research, Data-driven Bioinformatics, Data-driven Healthcare, Data-driven Management, Data-driven eGovernment, Data-driven Smart City/Planet, Data Marketing and Economics, Social Media and Recommendation Systems, Data-driven Security, Data-driven Business Model Innovation, Social and/or organizational impacts of Data Science. |
| django and data science: Web Development with Django Ben Shaw, Saurabh Badhwar, Andrew Bird, Bharath Chandra K S, Chris Guest, 2021-02-25 Django is a popular Python-based framework for web application development. Like Python, Django is easy for beginners to learn and enables constant progress. This book will help aspiring web developers gain the skills to use Django to develop robust web apps. |
| django and data science: Data Science in Production Ben Weber, 2020 Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and deploy scalable model pipelines, data scientists can own more of the model production process and more rapidly deliver data products. This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production. From startups to trillion dollar companies, data science is playing an important role in helping organizations maximize the value of their data. This book helps data scientists to level up their careers by taking ownership of data products with applied examples that demonstrate how to: Translate models developed on a laptop to scalable deployments in the cloud Develop end-to-end systems that automate data science workflows Own a data product from conception to production The accompanying Jupyter notebooks provide examples of scalable pipelines across multiple cloud environments, tools, and libraries (github.com/bgweber/DS_Production). Book Contents Here are the topics covered by Data Science in Production: Chapter 1: Introduction - This chapter will motivate the use of Python and discuss the discipline of applied data science, present the data sets, models, and cloud environments used throughout the book, and provide an overview of automated feature engineering. Chapter 2: Models as Web Endpoints - This chapter shows how to use web endpoints for consuming data and hosting machine learning models as endpoints using the Flask and Gunicorn libraries. We'll start with scikit-learn models and also set up a deep learning endpoint with Keras. Chapter 3: Models as Serverless Functions - This chapter will build upon the previous chapter and show how to set up model endpoints as serverless functions using AWS Lambda and GCP Cloud Functions. Chapter 4: Containers for Reproducible Models - This chapter will show how to use containers for deploying models with Docker. We'll also explore scaling up with ECS and Kubernetes, and building web applications with Plotly Dash. Chapter 5: Workflow Tools for Model Pipelines - This chapter focuses on scheduling automated workflows using Apache Airflow. We'll set up a model that pulls data from BigQuery, applies a model, and saves the results. Chapter 6: PySpark for Batch Modeling - This chapter will introduce readers to PySpark using the community edition of Databricks. We'll build a batch model pipeline that pulls data from a data lake, generates features, applies a model, and stores the results to a No SQL database. Chapter 7: Cloud Dataflow for Batch Modeling - This chapter will introduce the core components of Cloud Dataflow and implement a batch model pipeline for reading data from BigQuery, applying an ML model, and saving the results to Cloud Datastore. Chapter 8: Streaming Model Workflows - This chapter will introduce readers to Kafka and PubSub for streaming messages in a cloud environment. After working through this material, readers will learn how to use these message brokers to create streaming model pipelines with PySpark and Dataflow that provide near real-time predictions. Excerpts of these chapters are available on Medium (@bgweber), and a book sample is available on Leanpub. |
| django and data science: 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. |
| django and data science: Data Science Bookcamp Leonard Apeltsin, 2021-12-07 Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution |
| django and data science: Django 3 By Example Antonio Melé, 2020-03-31 Learn Django 3 with four end-to-end web projects Key FeaturesLearn Django 3 by building real-world web applications from scratch in Python, using coding best practicesIntegrate other technologies into your application with clear, step-by-step explanations and comprehensive example codeImplement advanced functionalities like a full-text search engine, a user activity stream, or a recommendation engineAdd real-time features with Django Channels and WebSocketsBook Description If you want to learn the entire process of developing professional web applications with Python and Django, then this book is for you. In the process of building four professional Django projects, you will learn about Django 3 features, how to solve common web development problems, how to implement best practices, and how to successfully deploy your applications. In this book, you will build a blog application, a social image bookmarking website, an online shop, and an e-learning platform. Step-by-step guidance will teach you how to integrate popular technologies, enhance your applications with AJAX, create RESTful APIs, and set up a production environment for your Django projects. By the end of this book, you will have mastered Django 3 by building advanced web applications. What you will learnBuild real-world web applicationsLearn Django essentials, including models, views, ORM, templates, URLs, forms, and authenticationImplement advanced features such as custom model fields, custom template tags, cache, middleware, localization, and moreCreate complex functionalities, such as AJAX interactions, social authentication, a full-text search engine, a payment system, a CMS, a RESTful API, and moreIntegrate other technologies, including Redis, Celery, RabbitMQ, PostgreSQL, and Channels, into your projectsDeploy Django projects in production using NGINX, uWSGI, and DaphneWho this book is for This book is intended for developers with Python knowledge who wish to learn Django in a pragmatic way. Perhaps you are completely new to Django, or you already know a little but you want to get the most out of it. This book will help you to master the most relevant areas of the framework by building practical projects from scratch. You need to have familiarity with programming concepts in order to read this book. Some previous knowledge of HTML and JavaScript is assumed. |
| django and data science: Django in Production Arghya Saha, 2024-04-05 Enhance your Django skills and elevate your employability by mastering the tools and practices employed by seasoned senior developers Key Features Gain a comprehensive understanding of the available deployment strategies for Django applications Explore techniques and best practices to enhance the efficiency and performance of your Django application Troubleshoot common production issues through efficient error logging and handling techniques Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionYou may have got your first Django developer job after a six-week bootcamp or online course, and that’s great, but what’s next? In small companies, mentorship can be hard to come by and gaining the traits of a senior developer without that can take a long time. This is precisely where Django in Production comes into play. This book will first delve into the true meaning of good practice and help you understand the rationale behind industry professionals building websites in specific ways to develop a solid foundation for your Django projects. Next, you will uncover hidden Django secrets through hands-on exploration, leveraging the power of Docker and version control to your advantage. You will gain insights into mastering Git hooks for efficient code maintenance, establishing a robust CI pipeline, and harnessing the capabilities of AWS Beanstalk. These tools will empower you to develop highly scalable products—an essential skill set for aspiring developers transitioning from junior to senior roles. Later, you will understand the significance of monitoring and be introduced to industry-standard tools utilized by professionals for effective monitoring practices. By the end of this book, you will have set yourself apart from the crowd, equipped with the knowledge and expertise to thrive as a seasoned Django developer.What you will learn Write scalable and maintainable code like a Django expert Become proficient in Docker for Django and experience platform-agnostic development Explore intelligent practices for continuous integration Leverage the power of AWS to seamlessly deploy your application in a production environment Optimize unstable systems through effective performance monitoring Effortlessly handle authentication and authorization issues Automate repetitive tasks by creating custom middleware Thoroughly test your code using factory_boy and craft comprehensive API tests Who this book is for This book is for Python and Django developers who aspire to elevate their Django skills to an advanced level. It assumes an intermediate level of proficiency in Python and Django programming and aims to impart comprehensive knowledge on optimizing the production environment and utilizing associated toolsets. By implementing these best practices, you will enhance the efficiency, robustness, and scalability of your production systems, thereby accelerating your career growth and professional development. |
| django and data science: DATA SCIENCE NARAYAN CHANGDER, 2023-10-18 THE DATA SCIENCE MCQ (MULTIPLE CHOICE QUESTIONS) SERVES AS A VALUABLE RESOURCE FOR INDIVIDUALS AIMING TO DEEPEN THEIR UNDERSTANDING OF VARIOUS COMPETITIVE EXAMS, CLASS TESTS, QUIZ COMPETITIONS, AND SIMILAR ASSESSMENTS. WITH ITS EXTENSIVE COLLECTION OF MCQS, THIS BOOK EMPOWERS YOU TO ASSESS YOUR GRASP OF THE SUBJECT MATTER AND YOUR PROFICIENCY LEVEL. BY ENGAGING WITH THESE MULTIPLE-CHOICE QUESTIONS, YOU CAN IMPROVE YOUR KNOWLEDGE OF THE SUBJECT, IDENTIFY AREAS FOR IMPROVEMENT, AND LAY A SOLID FOUNDATION. DIVE INTO THE DATA SCIENCE MCQ TO EXPAND YOUR DATA SCIENCE KNOWLEDGE AND EXCEL IN QUIZ COMPETITIONS, ACADEMIC STUDIES, OR PROFESSIONAL ENDEAVORS. THE ANSWERS TO THE QUESTIONS ARE PROVIDED AT THE END OF EACH PAGE, MAKING IT EASY FOR PARTICIPANTS TO VERIFY THEIR ANSWERS AND PREPARE EFFECTIVELY. |
| django and data science: Python Data Cleaning Cookbook Michael Walker, 2020-12-11 Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricks Key FeaturesGet well-versed with various data cleaning techniques to reveal key insightsManipulate data of different complexities to shape them into the right form as per your business needsClean, monitor, and validate large data volumes to diagnose problems before moving on to data analysisBook Description Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data. By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it. What you will learnFind out how to read and analyze data from a variety of sourcesProduce summaries of the attributes of data frames, columns, and rowsFilter data and select columns of interest that satisfy given criteriaAddress messy data issues, including working with dates and missing valuesImprove your productivity in Python pandas by using method chainingUse visualizations to gain additional insights and identify potential data issuesEnhance your ability to learn what is going on in your dataBuild user-defined functions and classes to automate data cleaningWho this book is for This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book. |
| django and data science: Django for Beginners William Vincent, 2023-09-21 Learn how to build, test, and deploy real-world web applications using Python and Django. |
| django and data science: 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 |
| django and data science: Geographic Information Systems - Data Science Approach Rifaat Abdalla, 2024-03-13 Dive into the dynamic world of Geographic Information Systems (GIS) and data science with our comprehensive book in which innovation and insights converge. This book presents a pioneering exploration at the intersection of GIS and data science, providing a comprehensive view of their symbiotic relationship and transformative potential. It encapsulates advanced methodologies, real-world applications, and interdisciplinary approaches that redefine how we perceive and utilize spatial data. Offering a gateway to cutting-edge research and practical insights, this book serves as a crucial resource for scholars, practitioners, and enthusiasts alike. It addresses pressing challenges across diverse domains, from environmental studies to public health and predictive analytics, demonstrating the paramount significance of integrating GIS with data science methodologies. It is an essential compass guiding readers toward a deeper understanding and application of these dynamic fields in today's data-driven world. |
| django and data science: Data Science with Python Robert Johnson, 2024-10-26 Data Science with Python: Unlocking the Power of Pandas and Numpy is an essential guide for beginners and professionals alike, striving to master the art of data analysis using Python's robust ecosystem. This book delves into the foundational aspects of data science, providing readers with a comprehensive understanding of how to harness Python's capabilities for data manipulation and exploration. By covering key libraries such as Pandas and Numpy, it equips readers with the skills necessary to perform high-performance numerical computations and sophisticated data analysis tasks. Structured to ensure a seamless learning experience, this book introduces essential Python programming concepts and progressively advances to more complex topics in data cleaning, preprocessing, and visualization. Each chapter is crafted to build upon the last, ensuring a coherent progression and a deepening of knowledge. With a series of practical projects, readers will gain hands-on experience in real-world data science applications, learning how to develop predictive models and deploy solutions effectively. Through this approach, the book bridges the gap between theoretical understanding and practical application, empowering readers to unlock the full potential of data science in today's data-driven landscape. |
| django and data science: Python Web Development with Django Jeff Forcier, Paul Bissex, Wesley J Chun, 2008-10-24 Using the simple, robust, Python-based Django framework, you can build powerful Web solutions with remarkably few lines of code. In Python Web Development with Django®, three experienced Django and Python developers cover all the techniques, tools, and concepts you need to make the most of Django 1.0, including all the major features of the new release. The authors teach Django through in-depth explanations, plus provide extensive sample code supported with images and line-by-line explanations. You’ll discover how Django leverages Python’s development speed and flexibility to help you solve a wide spectrum of Web development problems and learn Django best practices covered nowhere else. You’ll build your first Django application in just minutes and deepen your real-world skills through start-to-finish application projects including Simple Web log (blog) Online photo gallery Simple content management system Ajax-powered live blogger Online source code sharing/syntax highlighting tool How to run your Django applications on the Google App Engine This complete guide starts by introducing Python, Django, and Web development concepts, then dives into the Django framework, providing a deep understanding of its major components (models, views, templates), and how they come together to form complete Web applications. After a discussion of four independent working Django applications, coverage turns to advanced topics, such as caching, extending the template system, syndication, admin customization, and testing. Valuable reference appendices cover using the command-line, installing and configuring Django, development tools, exploring existing Django applications, the Google App Engine, and how to get more involved with the Django community. Introduction 1 Part I: Getting Started Chapter 1: Practical Python for Django 7 Chapter 2: Django for the Impatient: Building a Blog 57 Chapter 3: Starting Out 77 Part II: Django in Depth Chapter 4: Defining and Using Models 89 Chapter 5: URLs, HTTP Mechanisms, and Views 117 Chapter 6: Templates and Form Processing 135 Part III: Django Applications by Example Chapter 7: Photo Gallery 159 Chapter 8: Content Management System 181 Chapter 9: Liveblog 205 Chapter 10: Pastebin 221 Part IV: Advanced Django Techniques and Features Chapter 11: Advanced Django Programming 235 Chapter 12: Advanced Django Deployment 261 Part V: Appendices Appendix A: Command Line Basics 285 Appendix B: Installing and Running Django 295 Appendix C: Tools for Practical Django Development 313 Appendix D: Finding, Evaluating, and Using Django Applications 321 Appendix E: Django on the Google App Engine 325 Appendix F: Getting Involved in the Django Project 337 Index 339 Colophon 375 |
| django and data science: 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. |
| django and data science: Fundamentals of Data Science Mr.Desidi Narsimha Reddy, Lova Naga Babu Ramisetti, Mr.Harikrishna Pathipati, 2024-09-05 Mr.Desidi Narsimha Reddy, Data Consultant (Data Governance, Data Analytics: Enterprise Performance Management, AI & ML), Soniks consulting LLC, 101 E Park Blvd Suite 600, Plano, TX 75074, United States. Lova Naga Babu Ramisetti, EPM Consultant, Department of Information Technology, MiniSoft Empowering Techonolgy, 10333 Harwin Dr. #375e, Houston, TX 77036, USA. Mr.Harikrishna Pathipati, EPM Manager, Department of Information Technology, ITG Technologies, 10998 S Wilcrest Dr, Houston, TX 77099, USA. |
| django and data science: 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! |
| django and data science: Two Scoops of Django Audrey Roy Greenfeld, Daniel Roy Greenfeld, 2015-05-15 Two scoops of Django introduces you to various tips, tricks, patterns, code snippets, and techniques . . .--Page 4 of cover. |
| django and data science: Fundamentals of Data Science Sanjeev J. Wagh, Manisha S. Bhende, Anuradha D. Thakare, 2021-09-26 Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals. |
| django and data science: Fundamentals of Data Science Dr.Vemuri Sudarsan Rao, Dr.M.Sarada, Mrs.Masireddy Sadalaxmi, 2024-09-03 Dr.Vemuri Sudarsan Rao, Professor & Head, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India. Dr.M.Sarada, Associate Professor, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India. Mrs.Masireddy Sadalaxmi, Associate Professor, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India. |
| django and data science: Django 2 by Example Antonio Melé, 2018-05-31 Learn Django 2.0 with four end-to-end projects Key Features Learn Django by building real-world web applications from scratch Develop powerful web applications quickly using the best coding practices Integrate other technologies into your application with clear, step-by-step explanations and comprehensive example code Book Description If you want to learn about the entire process of developing professional web applications with Django, then this book is for you. This book will walk you through the creation of four professional Django projects, teaching you how to solve common problems and implement best practices. You will learn how to build a blog application, a social image-bookmarking website, an online shop, and an e-learning platform. The book will teach you how to enhance your applications with AJAX, create RESTful APIs, and set up a production environment for your Django projects. The book walks you through the creation of real-world applications, while solving common problems and implementing best practices. By the end of this book, you will have a deep understanding of Django and how to build advanced web applications What you will learn Build practical, real-world web applications with Django Use Django with other technologies, such as Redis and Celery Develop pluggable Django applications Create advanced features, optimize your code, and use the cache framework Add internationalization to your Django projects Enhance your user experience using JavaScript and AJAX Add social features to your projects Build RESTful APIs for your applications Who this book is for If you are a web developer who wants to see how to build professional sites with Django, this book is for you. You will need a basic knowledge of Python, HTML, and JavaScript, but you don't need to have worked with Django before. |
| django and data science: Streamlit for Data Science Tyler Richards, 2023-09-29 An easy-to-follow and comprehensive guide to creating data apps with Streamlit, including how-to guides for working with cloud data warehouses like Snowflake, using pretrained Hugging Face and OpenAI models, and creating apps for job interviews. Key Features Create machine learning apps with random forest, Hugging Face, and GPT-3.5 turbo models Gain an insight into how experts harness Streamlit with in-depth interviews with Streamlit power users Discover the full range of Streamlit’s capabilities via hands-on exercises to effortlessly create and deploy well-designed apps Book DescriptionIf you work with data in Python and are looking to create data apps that showcase ML models and make beautiful interactive visualizations, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy data apps quickly, all within Python. This helps you create prototypes in hours instead of days! Written by a prolific Streamlit user and senior data scientist at Snowflake, this fully updated second edition builds on the practical nature of the previous edition with exciting updates, including connecting Streamlit to data warehouses like Snowflake, integrating Hugging Face and OpenAI models into your apps, and connecting and building apps on top of Streamlit databases. Plus, there is a totally updated code repository on GitHub to help you practice your newfound skills. You'll start your journey with the fundamentals of Streamlit and gradually build on this foundation by working with machine learning models and producing high-quality interactive apps. The practical examples of both personal data projects and work-related data-focused web applications will help you get to grips with more challenging topics such as Streamlit Components, beautifying your apps, and quick deployment. By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly.What you will learn Set up your first development environment and create a basic Streamlit app from scratch Create dynamic visualizations using built-in and imported Python libraries Discover strategies for creating and deploying machine learning models in Streamlit Deploy Streamlit apps with Streamlit Community Cloud, Hugging Face Spaces, and Heroku Integrate Streamlit with Hugging Face, OpenAI, and Snowflake Beautify Streamlit apps using themes and components Implement best practices for prototyping your data science work with Streamlit Who this book is forThis book is for data scientists and machine learning enthusiasts who want to get started with creating data apps in Streamlit. It is terrific for junior data scientists looking to gain some valuable new skills in a specific and actionable fashion and is also a great resource for senior data scientists looking for a comprehensive overview of the library and how people use it. Prior knowledge of Python programming is a must, and you’ll get the most out of this book if you’ve used Python libraries like Pandas and NumPy in the past. |
| django and data science: Data Science and Big Data Analytics Durgesh Mishra, |
| django and data science: The Data Science Handbook Field Cady, 2017-02-28 A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon. |
| django and data science: 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. |
| django and data science: 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. |
| django and data science: Mastering Python for Data Science Samir Madhavan, 2015-08-31 Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios. |
| django and data science: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| django and data science: Big Data Analytics: From Data to Discovery Dr. Sudhakar.K, Mrs.Noor Sumaiya, Mrs.Niveditha.S, Mr.Debarshi Mazumder, 2024-06-12 Dr. Sudhakar.K, Associate Professor, Department of Artificial Intelligence & Data Science, NITTE Meenakshi Institute of Technology, Bangalore, Karnataka, India. Mrs.Noor Sumaiya, Assistant Professor, Department of Computer Science Engineering, The Oxford College of Engineering, Bangalore, Karnataka, India. Mrs.Niveditha.S, Assistant Professor, Department of Information Science & Engineering, Don Bosco Institute of Technology, Bangalore, Karnataka, India. Mr.Debarshi Mazumder, Assistant Professor, Department of Artificial Intelligence & Data Science, NITTE Meenakshi Institute of Technology, Bangalore, Karnataka, India. |
| django and data science: Test-Driven Development with Python Harry Percival, 2017-08-02 By taking you through the development of a real web application from beginning to end, the second edition of this hands-on guide demonstrates the practical advantages of test-driven development (TDD) with Python. You’ll learn how to write and run tests before building each part of your app, and then develop the minimum amount of code required to pass those tests. The result? Clean code that works. In the process, you’ll learn the basics of Django, Selenium, Git, jQuery, and Mock, along with current web development techniques. If you’re ready to take your Python skills to the next level, this book—updated for Python 3.6—clearly demonstrates how TDD encourages simple designs and inspires confidence. Dive into the TDD workflow, including the unit test/code cycle and refactoring Use unit tests for classes and functions, and functional tests for user interactions within the browser Learn when and how to use mock objects, and the pros and cons of isolated vs. integrated tests Test and automate your deployments with a staging server Apply tests to the third-party plugins you integrate into your site Run tests automatically by using a Continuous Integration environment Use TDD to build a REST API with a front-end Ajax interface |
| django and data science: Foundations of Data Science Dr. M. Muthamizh Selvam, Sanjay Srivastava, Dr. R Thiru Murugan, Dr. B Gayathri, 2024-09-05 Foundations of Data Science is a comprehensive guide that introduces key concepts and techniques essential for understanding and analyzing data in the modern world. Foundational topics like statistics, probability, linear algebra, and machine learning, offering practical insights and applications in real-world data science. With a focus on both theory and implementation, it is designed for students and professionals seeking to build a solid grounding in data science principles and develop skills in data-driven problem-solving, analysis, and predictive modeling across diverse domains. |
| django and data science: Python Machine Learning Django Smith, 2019-06-10 Start Programming Python What if you could make your own program, one that is able to learn by trial and error, or based on the information that you show it? What if you could get a program that could adapt and change based on the input of the user? And what if you were able to make all of this happen with the Python coding language, helping even beginner's work with more complicated codes? This is all possible with Python machine learning. This guidebook is going to take some time to look at Python machine learning and all of the neat things that you are able to do with it. Machine learning is a growing field, one that a lot of programmers want to spend their time on. But even though this sounds like a complicated part of technology to work with, you will find that with the help of the Python coding language, anyone can start writing their own codes in machine learning. This guidebook is going to take a look at all of the different topics that you need to know in order to get started with Python machine learning. Some of the topics that we will explore inside include: The basics of machine learning The difference between supervised and unsupervised machine learning. Setting up your new environment in the Python language. Data preprocessing with the help of machine learning. How to use Python coding to help with linear regression. Decision trees and random forests. How to work with support vector regression problems. Can machine learning really help with Naïve Bayes problems? Accelerated data analysis using the Python code. And so much more! If you have been interested in learning more about machine learning, and you want to be able to learn a few of the codes that can make it happen for you, make sure to check out this guidebook to help you get started! If all of this sounds like your ideal book, then hop on over and hit now that buy button! Well, stress no more! Buy this book and also learn all... and DOWNLOAD IT NOW! ★★Buy the Paperback Version of this Book and get the Kindle Book version for FREE ★★ |
| django and data science: Hands-On Data Science and Python Machine Learning Frank Kane, 2017-07-31 This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time. |
| django and data science: Scripting Superpack For Beginners Rob Botwright, 101-01-01 Introducing the Scripting Superpack for Beginners - your ultimate gateway to mastering scripting languages. This bundle comprises four dynamic books, each designed to empower you with scripting mastery across Python, PowerShell, Bash, and Java. 📘 Book 1 - Scripting Simplified: A Beginner's Guide to Python Discover the gentle giant of scripting - Python. Perfect for beginners, this book will demystify Python's syntax and unveil its versatility, setting you on a journey to Python scripting excellence. 📗 Book 2 - Mastering PowerShell Scripting: From Novice to Ninja Unleash the power of Windows automation with PowerShell. Whether you're an IT professional or a scripting enthusiast, this book will guide you from novice to ninja in managing systems and automating tasks. 📙 Book 3 - Bash Scripting Unleashed: A Practical Approach for Beginners and Beyond Unlock the world of command-line wizardry with Bash. From mastering the Linux file system to creating powerful scripts, this book provides a practical approach for beginners and beyond. 📕 Book 4 - Java Scripting Mastery: A Step-by-Step Guide from Beginner to Pro Experience the fusion of scripting and Java programming. Learn to create Java applications, harness libraries and APIs, and dive into multithreading. This book is your pathway to becoming a scripting pro. Whether you want to automate tasks, manage systems, analyze data, or develop applications, the Scripting Superpack for Beginners has got you covered. Each book is meticulously crafted to offer a hands-on and immersive learning experience. This superpack is your key to scripting excellence, no matter your background or experience level. Grab your bundle today and embark on a transformative journey into the world of scripting. Don't miss this opportunity to script your way to success! |
| django and data science: Agile Data Science Russell Jurney, 2013-10-15 Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track |
How do I do a not equal in Django queryset filtering?
Feb 22, 2017 · Meanwhile, use exclude() The Django issue tracker has the remarkable entry #5763, titled "Queryset doesn't have a "not equal" filter operator". It is remarkable because (as …
How to do an OR filter in a Django query? - Stack Overflow
The question is specifically about building a query in Django to get records with field1 = 'value1' OR field2 == 'value2'. Your answer doesn't answer the question.
Django: How to manage development and production settings?
May 19, 2012 · The DJANGO_SETTINGS_MODULE environment variable controls which settings file Django will load. You therefore create separate configuration files for your respective …
How to use "get_or_create ()" in Django? - Stack Overflow
NOTE: As of Django 3.2, any statement passed in defaults are evaluated even if a new object is not created. I ended up writing try catch statement whenever create needs a complex …
Django - makemigrations - No changes detected - Stack Overflow
Mar 22, 2016 · I was trying to create migrations within an existing app using the makemigrations command but it outputs "No changes detected". Usually I create new apps using …
python - Django optional URL parameters - Stack Overflow
Jan 16, 2013 · python django django-views django-urls edited Feb 15, 2023 at 7:48 Sunderam Dubey 8,866 12 25 43
How to count the number of rows in a database table in Django
Feb 7, 2017 · I have database created by Django model, where status and id and username are the fields of the table Messages. I need to count the number of rows where status value= 0 for …
How to change the Django default runserver port? - Stack Overflow
Inside that you will find your django installation Open the django folder-> core -> management -> commands Inside the commands folder open up the runserver.py script with a text editor Find …
How to execute a Python script from the Django shell?
The django shell is the good way to execute a python module with the django environment, but it is not always easy and tiresome to import modules and execute functions manually especially …
Django : HTML form action directing to view (or url?) with 2 …
Feb 24, 2017 · Become good django developer You can also use Django ModelForms for your model. Using model forms or simple form you can do multiple things Modular approach Write …
Loan Prediction Using Machine Learning Techniques With …
Data to Promote Fair Pricing and Lending M. Cary Collins is the author. Year: 2013 Material: Banks should do a better job of handling data associated with loan approval processes in …
PYTHON FOR WEB DEVELOPMENT - IJCSMC
development in Python is aided by the powerful frameworks such as Django, web2py, Pyramid, and Flask that Python supports. Thus, Python promises to emerge as one of the preferred …
Review on Cricket Analysis and Prediction using Machine
artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Predictions like who would …
A Wedge of Django
We like to think of Django like a delicious aged cheese like Boerenkaas Gouda , with a sweet, intense flavor that only comes with maturity. Reason #2 - Django is Python Python is an …
STUDENTS PERFORMANCE PREDICTION
Data Collection and Preprocessing: Collects and prepares data for the machine learning models. Prediction Models: Analyzes the data and generates performance predictions. Visualization: …
Online Job Portal using Django - IJISME
International Journal of Innovative Science and Modern Engineering (IJISME) ISSN: 2319-6386 (Online), Volume-12 Issue-1, January 2024 ... was created using Python Django, HTML, CSS, …
Introduction to web development with Python and Django …
Data, or information, needs to be stored somewhere. Typically we save data in files. Databases are another way of saving data which has some advantages over plain files. Web applications …
Singkat: A Keyword-Based URL Shortener and Click Tracker …
based Django web framework. It can be deployed and customized in any web project based on Django framework. This makes sure that administrators can gain control over data in their own …
SHANTABEN NAGINDAS KHANDWALA COLLEGE OF SCIENCE …
Advanced Data Structures and Algorithms (04) Mathematical foundations of Computer Science(02) II Agile Practices and DevOps (04) Python Web Development Using Django(04) …
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY …
Django) 0 0 2 1 Total 15 0 12 20 III YEAR I SEMESTER S. No. Course Code Course Title L T P Credits 1 Design and Analysis of Algorithms 3 1 0 4 2 Introduction to Data Science 3 0 0 3 3 …
CRIME DETECTION USING MACHINE LEARNING - Sathyabama …
The Support vector Regression model results in classifying the test data. It predicts the stock crime rate value that is going to be happening in future. It uses train set to train and fit the …
A Generic Review of Web Technology: DJango and Flask
International Journal of Advanced Computing Science and Engineering ISSN 2714-7533 Vol. 2, No. 1, April 2020, pp. 34-40 Nuruldelmia Idris et.al (A Generic Review of Web Technology: …
DJANGO WEBSITE FOR DISEASE PREDICTION USING …
DJANGO WEBSITE FOR DISEASE PREDICTION USING MACHINE LEARNING ... A.Nabisha, P.M.Ansho ,Shajin.V, 1,2,4,5Assistant Professor, Loyola Institute of Technology and …
List of Add on / Certificate/ Value added programs offered …
Page | 1 List of Add on / Certificate/ Value added programs offered during the last five years S.No Data Collection Year for Assesment (2021-22) 1 Name of Add on /Certificate programs offered …
BANKING MANAGEMENT SYSTEM USING PYTHON - IRJMETS
[] "Django for Beginners" by William S. Vincent. Django is a powerful Python web framework often used to develop complex applications. Covering topics such as models, views, data, …
Bridging the skills gap - Hitachi Vantara LLC
Applied Data Science course from Hitachi Vantara is centered on the business-led application of data science. It provides access to methods and technology which powers data-driven projects …
SWAYAM-NPTEL LOCAL CHAPTER - ssgmce.ac.in
SWAYAM-NPTEL LOCAL CHAPTER SPOC Name - Ms. Kalyani P. Sable Designation - Assistant Professor, Computer Science and Engineering Partnering since - Oct-2015 College ID - 235 …
Social Media Website with Python using Django
3. Set up the Django environment: Install Django and set up the environment on your system. 4. Create a Django project: Create a new Django project using the "Django-adminstart project" …
R22 B.Tech. CSE Syllabus JNTU Hyderabad
B.Tech. in COMPUTER SCIENCE AND ENGINEERING COURSE STRUCTURE, I & II YEAR SYLLABUS (R22 Regulations) ... 8 Data visualization- R Programming/ Power BI 0 0 2 1 ... 9 …
Using Django framework and DLT for Drug Supply Chain …
Fig. 1: The software architecture of the proposed system. In blue the components and artifacts common to all supply chains, in red the data that configure and manage a specific …
Web Development using Python (Django Framework)
Django is a high-level Python web framework that enables the rapid development of secure and maintainable websites. Django follows the model-template-views (MTV) ... They can help you …
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY …
B.Tech. in ARTIFICIAL INTELLIGENCE AND DATA SCIENCE COURSE STRUCTURE, I & II YEAR SYLLABUS (R22 Regulations) Applicable from AY 2022-23 Batch I YEAR I SEMESTER …
Management of Django Web Development in Python
Journal of Management and Service Science, 2021, Vol. 01, Iss. 02, S. No. 005, pp. 1-17 ... and data transfers which helps to create a good interaction between the users and the ... Django is …
ORGAN DONATION MANAGEMENT SYSTEM (PROJECT: …
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 13, No. 01, February 2023 20 4. GENERAL DESCRIPTION: PYTHON AND DJANGO Python-Django …
Streamlining HPC Operations: Integrating Django Helpdesk in …
Data Science and Robotics Spare time: Try to automate and monitor things around my house with ESP32’s 02. CHPC: - HPC Ecosystems Project - Virtual Lab - OpenHPC ... • Django is a …
Candidate Packet - portugal.iom.int
A diligent Data Analyst holding a bachelor’s degree in Civil Engineering and a Diploma in Data Science, with 2+ years of experience in Data collection, Data cleaning, predictive analytics, …
Research on Python Data Visualization Technology
Data using Python Django Framework K. Manikanta Vamsi, P. Lokesh, K. Neha Reddy et al.- ... 0-1130,HNJG-2020-1124.2020General Project of Hunan Social Science …
Tom Lam
B.Sc. Computer Science Sep 2024 - Present University of Warwick Coventry, UK International Foundation Programme in Computer Science Sep 2023 - Jun 2024 • Grade: Distinction (92%) …
Smart Health Care System Using Python Django And Machine …
6.2 DATA PREPROCESSING Data preprocessing is an essential step in any machine learning project, including the smart healthcare prediction system using Naive Bayes algorithm with …
Hands-On Microservices
he builds web and data engineering applications with Django and Python data transformation packages such as pandas. As a technical writer, he has written software and user …
ONE-STOP ONLINE BUSINESS MODEL USING DJANGO
International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) ... ONE-STOP ONLINE …
Xarray: N-D Labeled Arrays and Datasets in Python
“data science, machine ... python-growing-quickly pandas django numpy matplotlib flask 2012 2014 2016 2018 Time % of Stack Overflow question views per month 1.00% 0.75% 0.50% …
Personalizing Education through an Adaptive LMS with …
a data pipeline that mirrors some aspects of the fundamental data science process [26]. First, raw data is collected from a reputable source, typically a human expert with a comprehen-sive …
Yijingxiu (Louise) Lu
LEADERSHIP EXPERIENCE • Head Teaching Assistant Sep. 2024 - Dec. 2024 Seoul National University, Department of Computer Science Led the 2024-2 Algorithm (001) course, …
SOFTWARE ENGINEERING FOR SCIENCE
SCIENTIFIC DATA MANAGEMENT: CHALLENGES, TECHNOLOGY, AND DEPLOYMENT Edited by Arie Shoshani and Doron Rotem SOFTWARE ENGINEERING FOR SCIENCE …
Formation Flask - Ambient IT
Créer un serveur web avec Flask Utiliser le moteur de template Jinja2 de Flask Comment créer des applications web basées sur Python Flask. Ajoutez des styles CSS simples à votre …
Yash Mittra - GitHub Pages
Technologies: Git, Django, Amazon Web Services EC2, Celery, RabbitMQ, REST API, MongoDB, MySQL, jQuery, Bootstrap3, Selenium, LaTeX, Nginx Other: Web Scraping, Data Science, …
ISTUDENT PORTAL ONLINE ACADEMIC MANAGEMENT …
student and teacher data. The iStudent portal project will be built using Python, Django, HTML, CSS, and JavaScript. Python is a widely used high-level programming language that is ideal …
Argha Ray - csupueblo.edu
Business Data Science Technologies. MSIS2103 In Person Fall 2021 4.4/5 Business Data Science Technologies. MSIS2103 In Person Fall 2021 4.3/5 Business Data Science …
DATA VISUALIZATION, DASHBOARDS & STORYTELLING
DevOps, Back & Front Ends, and Data Science and Engineering Modules (AWS, Spark, Bachelor in Data and Business Analytics BDBA SEP-2023 DVDSN-DBA.3.M.A Area Information …
School of Computer Science - Carnegie Mellon University
CSS • Django • Android • L. A. TEX• Git Data structures • Software design patterns . COURSEWORK . Parallel and Sequential Data Structures and Algorithms Introduction to …
Codility Library Overview 2021
Evaluate candidates for every role and seniority level 01 Assess Real-life skills Created in partnership with industry experts, real-life engineering tasks must reflect actions which an …
Manohar Sai
• Collaborated cross-functionally with data science, product, and infrastructure teams to align architectural decisions with key business objectives and deliver high-impact features. Projects …
NEWS CONTENT AGGREGATOR USING WEB SCRAPING
stored data further used in visualization. 3. SERVING THE DATA OBJECTS Serving the data that is stored is a simple process, and is done by creating a view function. The function is defined …
Brian Alexander Jalleh
• Developed a full-stack application using Python (Django), HTML, CSS, and JavaScript • Integrated Stripe’s API for real-time payment and email verification during signing up ...
Mastering Python - Koenig-solutions.com
Django Web Framework • Features of Django • Installation of Django • MVC model • HTTP concepts • Views Templates and Form ... Data Science for AI and Machine Learning Using …
GURU GOBIND SINGH INDRAPRASTHA UNIVERSITY
bcat212: introduction to data science bcat214: introduction to artificial intelligence bcat216: network security bcat218: web development with python and django (regular) (batch: 2021 …
Python Flask and Django | Full Stack Python for Web …
Setup your Virtual Environment for Django on macOS/Linux Installing Pipenv Globally Your First Blank Django Project Introducing Text Editors Setting Up Django on VS Code ... frequently …
