Quickly start programming with Python 3 for data visualization with this step-by-step, detailed guide. This book’s programming-friendly approach using libraries such as leather, NumPy, Matplotlib, and Pandas will serve as a template for business and scientific visualizations. You’ll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python’s popular data visualization charting library. You’ll also be introduced to the scientific Python 3 ecosystem and work with the basics of NumPy, an integral part of that ecosystem. Later chapters are focused on various NumPy routines along with getting started with Scientific Data visualization using matplotlib. You’ll review the visualization of 3D data using graphs and networks and finish up by looking at data visualization with Pandas, including the visualization of COVID-19 data sets. The code examples are tested on popular platforms like Ubuntu, Windows, and Raspberry Pi OS. WithPractical Python Data Visualization you’ll master the core concepts of data visualization with Pandas and the Jupyter notebook interface. What You'll Learn Review practical aspects of Python Data Visualization with programming-friendly abstractions Install Python 3 and Jupyter on multiple platforms including Windows, Raspberry Pi, and Ubuntu Visualize COVID-19 data sets with Pandas Who This Book Is For Data Science enthusiasts and professionals, Business analysts and managers, software engineers, data engineers.

Today, software engineers need to know not only how to program effectively but also how to develop proper engineering practices to make their codebase sustainable and healthy. This book emphasizes this difference between programming and software engineering. How can software engineers manage a living codebase that evolves and responds to changing requirements and demands over the length of its life? Based on their experience at Google, software engineers Titus Winters and Hyrum Wright, along with technical writer Tom Manshreck, present a candid and insightful look at how some of the world's leading practitioners construct and maintain software. This book covers Google's unique engineering culture, processes, and tools and how these aspects contribute to the effectiveness of an engineering organization. You'll explore three fundamental principles that software organizations should keep in mind when designing, architecting, writing, and maintaining code: How time affects the sustainability of software and how to make your code resilient over time How scale affects the viability of software practices within an engineering organization What trade-offs a typical engineer needs to make when evaluating design and development decisions

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. For introductory-level Python programming and/or data-science courses. A groundbreaking, flexible approach to computer science and data science The Deitels’ Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs), and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science. Related Content Video: Python Fundamentals Live courses: Python Full Throttle with Paul Deitel: A One-Day, Fast-Paced, Code-Intensive Python Presentation Python® Data Science Full Throttle with Paul Deitel: Introductory Artificial Intelligence (AI), Big Data and Cloud Case Studies The book’s modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they’d like, and data-science instructors can integrate as much or as little Python as they’d like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.

Data Science Projects with Python introduces you to data science and machine learning using Python through practical examples. In this book, you'll learn to analyze, visualize, and model data, applying techniques like logistic regression and random forests. With a case-study method, you'll build confidence implementing insights in real-world scenarios. What this Book will help me do Set up a data science environment with necessary Python libraries such as pandas and scikit-learn. Effectively visualize data insights through Matplotlib and summary statistics. Apply machine learning models including logistic regression and random forests to solve data problems. Identify optimal models through evaluation metrics like k-fold cross-validation. Develop confidence in data preparation and modeling techniques for real-world data challenges. Author(s) Stephen Klosterman is a seasoned data scientist with a keen interest in practical applications of machine learning. He combines a strong academic foundation with real-world experience to craft relatable content. Stephen excels in breaking down complex topics into approachable lessons, helping learners grow their data science expertise step by step. Who is it for? This book is ideal for data analysts, scientists, and business professionals looking to enhance their skills in Python and data science. If you have some experience in Python and a foundational understanding of algebra and statistics, you'll find this book approachable. It offers an excellent gateway to mastering advanced data analysis techniques. Whether you're seeking to explore machine learning or apply data insights, this book supports your growth.

Learn the essentials of data science with Python through this comprehensive guide. By the end of this book, you'll have an in-depth understanding of core data science workflows, tools, and techniques. What this Book will help me do Understand and apply data manipulation techniques with pandas and NumPy. Build and optimize machine learning models with scikit-learn. Analyze and visualize complex datasets for derived insights. Implement exploratory data analysis to uncover trends in data. Leverage advanced techniques like graph analysis and deep learning for sophisticated projects. Author(s) Alberto Boschetti and Luca Massaron combine their extensive expertise in data science and Python programming to guide readers effectively. With hands-on knowledge and a passion for teaching, they provide practical insights across the data science lifecycle. Who is it for? This book is ideal for aspiring data scientists, data analysts, and software developers aiming to enhance their data analysis skills. Suited for beginners familiar with Python and basic statistics, this guide bridges the gap to real-world applications. Advance your career by unlocking crucial data science expertise.

Understand, evaluate, and visualize data About This Book Learn basic steps of data analysis and how to use Python and its packages A step-by-step guide to predictive modeling including tips, tricks, and best practices Effectively visualize a broad set of analyzed data and generate effective results Who This Book Is For This book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner. What You Will Learn Get acquainted with NumPy and use arrays and array-oriented computing in data analysis Process and analyze data using the time-series capabilities of Pandas Understand the statistical and mathematical concepts behind predictive analytics algorithms Data visualization with Matplotlib Interactive plotting with NumPy, Scipy, and MKL functions Build financial models using Monte-Carlo simulations Create directed graphs and multi-graphs Advanced visualization with D3 In Detail You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn. After this, you will move on to a data analytics specialization - predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You'll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling. After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examples This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan Learning Predictive Analytics with Python, Ashish Kumar Mastering Python Data Visualization, Kirthi Raman Style and approach The course acts as a step-by-step guide to get you familiar with data analysis and the libraries supported by Python with the help of real-world examples and datasets. It also helps you gain practical insights into predictive modeling by implementing predictive-analytics algorithms on public datasets with Python. The course offers a wealth of practical guidance to help you on this journey to data visualization

Scala will be a valuable tool to have on hand during your data science journey for everything from data cleaning to cutting-edge machine learning About This Book Build data science and data engineering solutions with ease An in-depth look at each stage of the data analysis process — from reading and collecting data to distributed analytics Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulations, and source code Who This Book Is For This learning path is perfect for those who are comfortable with Scala programming and now want to enter the field of data science. Some knowledge of statistics is expected. What You Will Learn Transfer and filter tabular data to extract features for machine learning Read, clean, transform, and write data to both SQL and NoSQL databases Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Load data from HDFS and HIVE with ease Run streaming and graph analytics in Spark for exploratory analysis Bundle and scale up Spark jobs by deploying them into a variety of cluster managers Build dynamic workflows for scientific computing Leverage open source libraries to extract patterns from time series Master probabilistic models for sequential data In Detail Scala is especially good for analyzing large sets of data as the scale of the task doesn’t have any significant impact on performance. Scala’s powerful functional libraries can interact with databases and build scalable frameworks — resulting in the creation of robust data pipelines. The first module introduces you to Scala libraries to ingest, store, manipulate, process, and visualize data. Using real world examples, you will learn how to design scalable architecture to process and model data — starting from simple concurrency constructs and progressing to actor systems and Apache Spark. After this, you will also learn how to build interactive visualizations with web frameworks. Once you have become familiar with all the tasks involved in data science, you will explore data analytics with Scala in the second module. You’ll see how Scala can be used to make sense of data through easy to follow recipes. You will learn about Bokeh bindings for exploratory data analysis and quintessential machine learning with algorithms with Spark ML library. You’ll get a sufficient understanding of Spark streaming, machine learning for streaming data, and Spark graphX. Armed with a firm understanding of data analysis, you will be ready to explore the most cutting-edge aspect of data science — machine learning. The final module teaches you the A to Z of machine learning with Scala. You’ll explore Scala for dependency injections and implicits, which are used to write machine learning algorithms. You’ll also explore machine learning topics such as clustering, dimentionality reduction, Naïve Bayes, Regression models, SVMs, neural networks, and more. This learning path combines some of the best that Packt has to offer into one complete, curated package. It includes content from the following Packt products: Scala for Data Science, Pascal Bugnion Scala Data Analysis Cookbook, Arun Manivannan Scala for Machine Learning, Patrick R. Nicolas Style and approach A complete package with all the information necessary to start building useful data engineering and data science solutions straight away. It contains a diverse set of recipes that cover the full spectrum of interesting data analysis tasks and will help you revolutionize your data analysis skills using Scala. Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

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

Learn how to harness Python for business intelligence tasks with the 'Python Business Intelligence Cookbook.' This guide provides practical recipes that help transform raw data into actionable insights for better decision-making. From preparing and analyzing to visualizing data, you will acquire useful skills for implementing efficient BI systems within your organization. What this Book will help me do Master installing and setting up tools like Anaconda and MongoDB for BI work. Prepare datasets by cleaning, standardizing, and extracting essential data. Use Pandas and NoSQL databases to analyze data and extract insights. Build business dashboards utilizing visualization tools like Matplotlib. Gain the ability to create complete BI systems for various business needs. Author(s) None Dempsey has extensive experience in Python programming and data analysis. With a passion for teaching and applied business intelligence, Dempsey writes in a straightforward and approachable style, making complex topics accessible to readers. The recipes compiled in this book are built to be both practical and intuitive. Who is it for? This book is ideal for data analysts, managers, and professionals who have a basic understanding of Python and want to apply it to business intelligence tasks. It's also helpful for those familiar with BI concepts looking to enhance or modernize their workflows with Python-based tools. If you're seeking to gain actionable insights from data in your business, this book is for you.

Dive into the powerful world of interactive computing and data visualization with Python in the Jupyter Notebook. In this book, you will gain foundational skills in Python and learn how to analyze and visualize data using popular libraries like pandas, NumPy, matplotlib, and more. By the end, you will be creating efficient computations and meaningful visualizations effortlessly. What this Book will help me do Understand the installation and usage of Anaconda and coding in Python through the Jupyter Notebook Gain practical experience in manipulating and exploring datasets with pandas Design advanced visualizations for data representation using matplotlib and seaborn Learn numerical computation and simulation techniques with NumPy and other tools Accelerate performance-sensitive tasks using tools like Numba and Cython Author(s) Cyrille Rossant, the author of this book, is a software developer and data scientist with extensive experience in Python, numerical computing, and data visualization. With a passion for making technical concepts approachable, his writing style blends clarity with practicality, ensuring readers from diverse backgrounds can successfully enhance their skills. Who is it for? This book is ideal for students, professionals, and hobbyists interested in data analysis and visualization. Beginners to Python programming will find it highly approachable. Those with some programming background but new to Python will also benefit greatly. Advanced readers will enjoy the in-depth discussions of performance optimizations and visualization customizations.

In this report, F# contributor Tomas Petricek explains many of the key features of the F# language that make it a great tool for data science and machine learning. Real world examples take you through the entire data science workflow with F#, from data access and analysis to presenting the results. You'll learn about: How F# and its unique features—such as type providers—ease the chore of data access The process of data analysis and visualization, using the Deedle library, R type provider and the XPlot charting library Implementations for a clustering algorithm using the standard F# library and how the F# type inference helps you understand your code The report also includes a list of resources to help you learn more about using F# for data science.

Dive into the world of data visualization with "OpenGL Data Visualization Cookbook." This guide offers over 35 hands-on recipes using OpenGL to craft stunning and interactive visualizations for various applications. You will learn how to render data dynamically, handle complex datasets, and create compelling graphics across platforms. What this Book will help me do Set up an efficient OpenGL development environment across different operating systems. Build interactive data visualizations with 2D and 3D graphics using OpenGL and related libraries. Master advanced techniques for rendering volumetric and motion data on a variety of devices. Integrate shaders to create realistic visual effects and dynamic content within your applications. Develop impactful OpenGL-based applications for both conventional and modern mobile platforms. Author(s) The authors behind "OpenGL Data Visualization Cookbook" are experienced developers and educators with years of expertise in computer graphics and real-time visualizations. They specialize in breaking down complex topics into digestible and hands-on lessons, offering practical guidance for using OpenGL. Who is it for? This book is perfect for developers, engineers, and scientists who wish to leverage OpenGL for advanced data visualization. It suits hands-on learners with basic programming skills in languages like C/C++ or similar languages. If you are eager to expand your skills in real-time graphics and explore the cutting-edge visualization techniques, this book is a suitable fit for you.

Using Agile methods, you can bring far greater innovation, value, and quality to any data warehouse, business intelligence, or analytics project. However, conventional Agile methodologies must be carefully adapted to address the unique characteristics of DW/BI projects. In , Agile pioneer Ken Collier shows how to do just that. Agile Analytics Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, legacy, and specialty systems that mix commercial and custom code. Using working examples, he shows how to manage analytics development teams with widely diverse skill sets; support enormous and fast-growing data volumes; and more. Collier's techniques offer equal value whether your projects involve "back-end" data management, "front-end" business analysis, or both. Part I focuses on Agile project management techniques and delivery team coordination, introducing core practices that shape the way your agile DW/BI project community works together towards success Part II presents technical methods for enabling continuous delivery of business value at production-quality levels, including evolving superior designs; test-driven DW development; version control; and project automation Collier brings together proven solutions you can apply right now--whether you're an IT decision-maker, data warehouse professional, DBA, business intelligence specialist, or database developer. With his help, you can mitigate project risk, improve business alignment, achieve better results--and have fun along the way.

Through Visual Six Sigma, learn what your data is telling your business In the typical business environment of process improvement, you want simple-to-use tools that everyone can use at all levels to rapidly explore and interpret data. Visual Six Sigma: Making Data Analysis Lean helps you use your own data to drive incredible improvement within your business. Divided into three parts-background, case studies, and JMP highlights- Visual Six Sigma covers Six Sigma and Visual Six Sigma A first look at JMP Transforming pricing management Improving white polymer manufacturing Designing experiments and modeling relationships Broaden and deepen your application of Six Sigma thinking within your organization with the intuitive and easy to use tools in Visual Six Sigma: Making Data Analysis Lean.