Verleger: Packt Publishing
Data Quality in the Age of AI. Building a foundation for AI strategy and data culture
Andrew Jones
As organizations worldwide seek to revamp their data strategies to leverage AI advancements and benefit from newfound capabilities, data quality emerges as the cornerstone for success. Without high-quality data, even the most advanced AI models falter. Enter Data Quality in the Age of AI, a detailed report that illuminates the crucial role of data quality in shaping effective data strategies.Packed with actionable insights, this report highlights the critical role of data quality in your overall data strategy. It equips teams and organizations with the knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for harnessing the power of data quality, enabling them to unlock their data's full potential, leading to improved performance, reduced costs, increased revenue, and informed strategic decisions.
Rohan Chopra , Aaron England, Mohamed Noordeen...
Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.As you make your way through the book, you will understand the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, discover how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.
David Natingga
Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well.Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis.By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Data Science for Decision Makers. Enhance your leadership skills with data science and AI expertise
Jon Howells
As data science and artificial intelligence (AI) become prevalent across industries, executives without formal education in statistics and machine learning, as well as data scientists moving into leadership roles, must learn how to make informed decisions about complex models and manage data teams. This book will elevate your leadership skills by guiding you through the core concepts of data science and AI.This comprehensive guide is designed to bridge the gap between business needs and technical solutions, empowering you to make informed decisions and drive measurable value within your organization. Through practical examples and clear explanations, you'll learn how to collect and analyze structured and unstructured data, build a strong foundation in statistics and machine learning, and evaluate models confidently. By recognizing common pitfalls and valuable use cases, you'll plan data science projects effectively, from the ground up to completion. Beyond technical aspects, this book provides tools to recruit top talent, manage high-performing teams, and stay up to date with industry advancements.By the end of this book, you’ll be able to characterize the data within your organization and frame business problems as data science problems.
Shane Molinari, Jim Packer
In today's world full of online threats, the complexity of harmful software presents a significant challenge for detection and analysis. This insightful guide will teach you how to apply the principles of data science to online security, acting as both an educational resource and a practical manual for everyday use.Data Science for Malware Analysis starts by explaining the nuances of malware, from its lifecycle to its technological aspects before introducing you to the capabilities of data science in malware detection by leveraging machine learning, statistical analytics, and social network analysis. As you progress through the chapters, you’ll explore the analytical methods of reverse engineering, machine language, dynamic scrutiny, and behavioral assessments of malicious software. You’ll also develop an understanding of the evolving cybersecurity compliance landscape with regulations such as GDPR and CCPA, and gain insights into the global efforts in curbing cyber threats.By the end of this book, you’ll have a firm grasp on the modern malware lifecycle and how you can employ data science within cybersecurity to ward off new and evolving threats.
Mirza Rahim Baig , Gururajan Govindan ,...
Unleash the power of data to reach your marketing goals with this practical guide to data science for business.This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects.You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions.As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior.By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.
Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar
Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments.The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions.
Gabriela Castillo Areco
Data is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3.You’ll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You’ll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data.The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you’ll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients.By the end of this book, you’ll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.