Practical Time Series Analysis. Master Time Series Data Processing, Visualization, and Modeling using Python

Avishek Pal, PKS Prakash

Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality, and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python.

Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition

Sinan Ozdemir

Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling.By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.

Principles of Data Science. Mathematical techniques and theory to succeed in data-driven industries

Sinan Ozdemir

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas.With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.

Principles of Data Science. Understand, analyze, and predict data using Machine Learning concepts and tools - Second Edition

Sinan Ozdemir, Sunil Kakade, Marco Tibaldeschi

Need to turn programming skills into effective data science skills? This book helps you connect mathematics, programming, and business analysis. You’ll feel confident asking—and answering—complex, sophisticated questions of your data, making abstract and raw statistics into actionable ideas.Going through the data science pipeline, you'll clean and prepare data and learn effective data mining strategies and techniques to gain a comprehensive view of how the data science puzzle fits together. You’ll learn fundamentals of computational mathematics and statistics and pseudo-code used by data scientists and analysts. You’ll learn machine learning, discovering statistical models that help control and navigate even the densest datasets, and learn powerful visualizations that communicate what your data means.

Programming PowerPoint With VBA Straight to the Point. Harness VBA to Customize, Automate, and Revolutionize PowerPoint Presentations

MrExcel's Holy Macro! Books, Eduardo N Sanchez

This book dives deep into the world of PowerPoint VBA with this practical guide. Starting with basic operations, the book moves through advanced automation techniques, covering themes, layouts, and animations. Readers will learn to craft professional-grade presentations, create interactive quizzes, and integrate PowerPoint with other Office apps, while mastering VBA essentials.This book provides a comprehensive introduction to PowerPoint VBA, enabling readers to apply programming concepts to automate repetitive tasks, enhance presentations, and build creative visual effects. With real-world projects and practical code examples, the book helps readers gain confidence in customizing PowerPoint features.Whether you're creating professional reports, interactive presentations, or seamless Office integrations, this guide ensures you stay ahead with advanced VBA techniques, enabling you to transform your PowerPoint workflow.

Puppet 3 Cookbook. An essential book if you have responsibility for servers. Real-world examples and code will give you Puppet expertise, allowing more control over servers, cloud computing, and desktops. A time-saving, career-enhancing tutorial - Second Edition

John Arundel

A revolution is happening in web operations. Configuration management tools can build servers in seconds, and automate your entire network. Tools like Puppet are essential to taking full advantage of the power of cloud computing, and building reliable, scalable, secure, high-performance systems. More and more systems administration and IT jobs require some knowledge of configuration management, and specifically Puppet.Puppet 3 Cookbook takes you beyond the basics to explore the full power of Puppet, showing you in detail how to tackle a variety of real-world problems and applications. At every step it shows you exactly what commands you need to type, and includes full code samples for every recipe.The book takes the reader from a basic knowledge of Puppet to a complete and expert understanding of Puppet's latest and most advanced features, community best practices, writing great manifests, scaling and performance, and extending Puppet by adding your own providers and resources. It starts with guidance on how to set up and expand your Puppet infrastructure, then progresses through detailed information on the language and features, external tools, reporting, monitoring, and troubleshooting, and concludes with many specific recipes for managing popular applications.The book includes real examples from production systems and techniques that are in use in some of the world's largest Puppet installations, including a distributed Puppet architecture based on the Git version control system. You'll be introduced to powerful tools that work with Puppet such as Hiera. The book also explains managing Ruby applications and MySQL databases, building web servers, load balancers, high-availability systems with Heartbeat, and many other state-of-the-art techniques

PySpark Cookbook. Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

Denny Lee, Tomasz Drabas

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem.You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command.By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.

Python 3 and Data Analytics Pocket Primer. A Quick Guide to NumPy, Pandas, and Data Visualization

Mercury Learning and Information, Oswald Campesato

This book, part of the best-selling Pocket Primer series, introduces readers to the fundamental concepts of data analytics using Python 3. The course begins with a concise introduction to Python, covering essential programming constructs and data manipulation techniques. This foundation sets the stage for deeper dives into data analytics, emphasizing the importance of data cleaning, a critical step in any data analysis process.Following the Python basics, the course explores powerful libraries such as NumPy and Pandas for efficient data handling and manipulation. It then delves into statistical concepts, providing the necessary background for understanding data distributions and analytical methods. The course culminates in data visualization techniques using Matplotlib and Seaborn, demonstrating how to effectively communicate insights through graphical representations.Throughout the course, numerous code samples and practical examples are provided, reinforcing learning and offering hands-on experience. Companion files with source code and figures are available online, supporting the learning journey. This comprehensive guide equips both beginners and seasoned professionals with the skills needed to excel in data analytics.

Python 3 and Data Visualization. Mastering Graphics and Data Manipulation with Python

Mercury Learning and Information, Oswald Campesato

Python 3 and Data Visualization provides an in-depth exploration of Python 3 programming and data visualization techniques. The course begins with an introduction to Python, covering essential topics from basic data types and loops to advanced constructs such as dictionaries and matrices. This foundation prepares readers for the next section, which focuses on NumPy and its powerful array operations, seamlessly leading into data visualization using prominent libraries like Matplotlib.Chapter 6 delves into Seaborn's rich visualization tools, providing insights into datasets like Iris and Titanic. The appendix covers additional visualization tools and techniques, including SVG graphics and D3 for dynamic visualizations. The companion files include numerous Python code samples and figures, enhancing the learning experience.From foundational Python concepts to advanced data visualization techniques, this course serves as a comprehensive resource for both beginners and seasoned professionals, equipping them with the necessary skills to effectively visualize data.

Python 3 and Machine Learning Using ChatGPT / GPT-4. Harness the Power of Python, Machine Learning, and Generative AI

Mercury Learning and Information, Oswald Campesato

This book bridges the gap between theoretical knowledge and practical application in Python programming, machine learning, and using ChatGPT-4 in data science. It starts with an introduction to Pandas for data manipulation and analysis. The book then explores various machine learning classifiers, from kNN to SVMs. Later chapters cover GPT-4's capabilities, enhancing linear regression analysis, and using ChatGPT in data visualization, including AI apps, GANs, and DALL-E.The journey begins with mastering Pandas and machine learning fundamentals. It progresses to applying GPT-4 in linear regression and machine learning classifiers. The final chapters focus on using ChatGPT for data visualization, making complex results accessible and understandable.Understanding these concepts is crucial for modern data scientists. This book transitions readers from basic Python programming to advanced applications of ChatGPT-4 in data science. Companion files with source code, datasets, and figures enhance learning, making this an essential resource for mastering Python, machine learning, and AI-driven data visualization.

Python 3 Data Visualization Using ChatGPT / GPT-4. Master Python Visualization Techniques with AI Integration

Mercury Learning and Information, Oswald Campesato

This book teaches Python 3 programming and data visualization, exploring cutting-edge techniques with ChatGPT/GPT-4 for generating compelling visuals. It starts with Python essentials, covering basic data types, loops, functions, and advanced constructs like dictionaries and matrices. The journey progresses to NumPy's array operations and data visualization using libraries such as Matplotlib and Seaborn. The book also covers tools like SVG graphics and D3 for dynamic visualizations.The course begins with foundational Python concepts, moves into NumPy and data visualization with Pandas, Matplotlib, and Seaborn. Advanced chapters explore ChatGPT and GPT-4, demonstrating their use in creating data visualizations from datasets like the Titanic. Each chapter builds on the previous one, ensuring a comprehensive understanding of Python and visualization techniques.These concepts are crucial for Python practitioners, data scientists, and anyone in data analytics. This book transitions readers from basic Python programming to advanced data visualization, blending theoretical knowledge with practical skills. Companion files with code, datasets, and figures enhance learning, making this an essential resource for mastering Python and data visualization.

Python 3 Data Visualization Using Google Gemini. Unlock the Power of Python and Google Gemini for Stunning Data Visualizations

Mercury Learning and Information, Oswald Campesato

This book teaches Python 3 programming and data visualization, exploring cutting-edge techniques with ChatGPT/GPT-4 for generating compelling visuals. It starts with Python essentials, covering basic data types, loops, functions, and advanced constructs like dictionaries and matrices. The journey progresses to NumPy's array operations and data visualization using libraries such as Matplotlib and Seaborn. The book also covers tools like SVG graphics and D3 for dynamic visualizations.The course begins with foundational Python concepts, moves into NumPy and data visualization with Pandas, Matplotlib, and Seaborn. Advanced chapters explore ChatGPT and GPT-4, demonstrating their use in creating data visualizations from datasets like the Titanic. Each chapter builds on the previous one, ensuring a comprehensive understanding of Python and visualization techniques.These concepts are crucial for Python practitioners, data scientists, and anyone in data analytics. This book transitions readers from basic Python programming to advanced data visualization, blending theoretical knowledge with practical skills. Companion files with code, datasets, and figures enhance learning, making this an essential resource for mastering Python and data visualization.

Python 3 Text Processing with NLTK 3 Cookbook

Jacob Perkins

Python Business Intelligence Cookbook. Leverage the computational power of Python with more than 60 recipes that arm you with the required skills to make informed business decisions

Robert Dempsey, Stefan Urbanek, Saurabh Chhajed

The amount of data produced by businesses and devices is going nowhere but up. In this scenario, the major advantage of Python is that it's a general-purpose language and gives you a lot of flexibility in data structures. Python is an excellent tool for more specialized analysis tasks, and is powered with related libraries to process data streams, to visualize datasets, and to carry out scientific calculations. Using Python for business intelligence (BI) can help you solve tricky problems in one go.Rather than spending day after day scouring Internet forums for “how-to” information, here you’ll find more than 60 recipes that take you through the entire process of creating actionable intelligence from your raw data, no matter what shape or form it’s in. Within the first 30 minutes of opening this book, you’ll learn how to use the latest in Python and NoSQL databases to glean insights from data just waiting to be exploited.We’ll begin with a quick-fire introduction to Python for BI and show you what problems Python solves. From there, we move on to working with a predefined data set to extract data as per business requirements, using the Pandas library and MongoDB as our storage engine.Next, we will analyze data and perform transformations for BI with Python. Through this, you will gather insightful data that will help you make informed decisions for your business. The final part of the book will show you the most important task of BI—visualizing data by building stunning dashboards using Matplotlib, PyTables, and iPython Notebook.

Python Data Analysis Cookbook. Clean, scrape, analyze, and visualize data with the power of Python!

Ivan Idris

Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning.Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining.In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code.By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.

Python Data Analysis. Data manipulation and complex data analysis with Python - Second Edition

Armando Fandango, Ivan Idris

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.