Verleger: Packt Publishing
Tony Fischetti
Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility.This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Data Analytics for Marketing. A practical guide to analyzing marketing data using Python
Guilherme Diaz-Bérrio
Most marketing professionals are familiar with various sources of customer data that promise insights for success. There are extensive sources of data, from customer surveys to digital marketing data. Moreover, there is an increasing variety of tools and techniques to shape data, from small to big data. However, having the right knowledge and understanding the context of how to use data and tools is crucial.In this book, you’ll learn how to give context to your data and turn it into useful information. You’ll understand how and where to use a tool or dataset for a specific question, exploring the what and why questions to provide real value to your stakeholders. Using Python, this book will delve into the basics of analytics and causal inference. Then, you’ll focus on visualization and presentation, followed by understanding guidelines on how to present and condense large amounts of information into KPIs. After learning how to plan ahead and forecast, you’ll delve into customer analytics and insights. Finally, you’ll measure the effectiveness of your marketing efforts and derive insights for data-driven decision-making.By the end of this book, you’ll understand the tools you need to use on specific datasets to provide context and shape your data, as well as to gain information to boost your marketing efforts.
De Mauro
Data Analytics Made Easy is an accessible beginner’s guide for anyone working with data. The book interweaves four key elements:Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience.Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components.Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own.Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.
Dr. Nadine Shillingford
Splunk 9 improves on the existing Splunk tool to include important features such as federated search, observability, performance improvements, and dashboarding. This book helps you to make the best use of the impressive and new features to prepare a Splunk installation that can be employed in the data analysis process.Starting with an introduction to the different Splunk components, such as indexers, search heads, and forwarders, this Splunk book takes you through the step-by-step installation and configuration instructions for basic Splunk components using Amazon Web Services (AWS) instances. You’ll import the BOTS v1 dataset into a search head and begin exploring data using the Splunk Search Processing Language (SPL), covering various types of Splunk commands, lookups, and macros. After that, you’ll create tables, charts, and dashboards using Splunk’s new Dashboard Studio, and then advance to work with clustering, container management, data models, federated search, bucket merging, and more.By the end of the book, you’ll not only have learned everything about the latest features of Splunk 9 but also have a solid understanding of the performance tuning techniques in the latest version.
Duc Haba
Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset.The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You’ll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you’ll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges.By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques.
Andrea Mauro , Paolo Valsecchi
This exam guide enables you to install, configure, and manage the vSphere 6.5 infrastructure in all its components: vCenter Server, ESXi hosts, and virtual machines, while helping you to prepare for the industry standard certification.This data center book will assist you in automating administration tasks and enhancing your environment’s capabilities. You will begin with an introduction to all aspects related to security, networking, and storage in vSphere 6.5. Next, you will learn about resource management and understand how to back up and restore the vSphere 6.5 infrastructure. As you advance, you will also cover troubleshooting, deployment, availability, and virtual machine management. This is followed by two mock tests that will test your knowledge and challenge your understanding of all the topics included in the exam.By the end of this book, you will not only have learned about virtualization and its techniques, but you’ll also be prepared to pass the VCP6.5-DCV (2V0-622) exam.
Michael Walker
Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results.As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You’ll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you’ll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You’ll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book.By the end of this book, you’ll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.