Navigating Data Science
Download Navigating Data Science PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Navigating Data Science book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Navigating Data Science
Author: Babita Singla
language: en
Publisher: Emerald Group Publishing
Release Date: 2025-04-14
Navigating Data Science: Unleashing the Creative Potential of Artificial Intelligence can offer significant contributions in the field of generative intelligence in the context of data science and help stakeholders formulate strategies to tackle its adoption, implementation, and control challenges.
Navigating Data Science in the Age of AI
Author: Babita Singla
language: en
Publisher: Emerald Group Publishing
Release Date: 2025-04-14
Navigating Data Science in the Age of AI: Exploring Possibilities of Generative Intelligence can offer significant contributions in the field of generative intelligence in the context of data science and help stakeholders formulate strategies to tackle its adoption, implementation, and control challenges.
Introduction to Data Science
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course.