Librarian
Research Starters for the Applied Data Sciences Option
-
Business Source Premier This link opens in a new windowAn essential article database for business research providing the full text for more than 2,000 periodicals, including about 1,000 scholarly journals. Covers virtually all disciplines in business and economics, including: accounting, economics, econometrics, finance, marketing, management, MIS, QMM and supply chain management.
-
Artificial Intelligence ReviewArtificial Intelligence Review publishes state-of-the-art research reports and critical evaluations of applications, techniques and algorithms in artificial intelligence, cognitive science and related disciplines.
-
Journal of Marketing AnalyticsProvides systematic, comprehensive coverage of the latest techniques, ideas and developments in data sourcing.
Books on Big Data Programming
-
Graph Neural Networks in Action
by
A hands-on guide to powerful graph-based deep learning models! Graph Neural Networks in Action is a great guide about how to build cutting-edge graph neural networks and powerful deep learning models for recommendation engines, molecular modeling, and more. You will learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. Ideal for Python programmers, you will also explore common graph neural network architectures and cutting-edge libraries, all clearly illustrated with well-annotated Python code. The main features include: Train and deploy a graph neural network Generate node embeddings Use GNNs at scale for very large datasets Build a graph data pipeline Create a graph data schema Understand the taxonomy of GNNs Manipulate graph data with NetworkX Go hands-on and explore relevant real-world projects as you dive into graph neural networks perfect for node prediction, link prediction, and graph classification. About the technology Graph neural networks expand the capabilities of deep learning beyond traditional tabular data, text, and images. This exciting new approach brings the amazing capabilities of deep learning to graph data structures, opening up new possibilities for everything - from recommendation engines to pharmaceutical research.
ISBN: 9781617299056Publication Date: 2025-04-15 -
Multivariate Analysis and Machine Learning Techniques [electronic resource] : Feature Analysis in Data Science Using Python
by
This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques - probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning. Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensive introduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications. The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.
ISBN: 9789819903535Publication Date: 2025 -
The Data Science Handbook
by
Practical, accessible guide to becoming a data scientist, updated to include the latest advances in data science and related fields. Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. The focus of The Data Science Handbook is on practical applications and the ability to solve real problems, rather than theoretical formalisms that are rarely needed in practice. Among its key points are: An emphasis on software engineering and coding skills, which play a significant role in most real data science problems. Extensive sample code, detailed discussions of important libraries, and a solid grounding in core concepts from computer science (computer architecture, runtime complexity, and programming paradigms). A broad overview of important mathematical tools, including classical techniques in statistics, stochastic modeling, regression, numerical optimization, and more. Extensive tips about the practical realities of working as a data scientist, including understanding related jobs functions, project life cycles, and the varying roles of data science in an organization. Exactly the right amount of theory. A solid conceptual foundation is required for fitting the right model to a business problem, understanding a tool's limitations, and reasoning about discoveries. Data science is a quickly evolving field, and this 2nd edition has been updated to reflect the latest developments, including the revolution in AI that has come from Large Language Models and the growth of ML Engineering as its own discipline. Much of data science has become a skillset that anybody can have, making this book not only for aspiring data scientists, but also for professionals in other fields who want to use analytics as a force multiplier in their organization.
ISBN: 9781394234509Publication Date: 2024-10-31 -
A Friendly Guide to Data Science [electronic resource] : Everything You Should Know About the Hottest Field in Tech
by
Curious about data science but not sure where to start? This book is a beginner-friendly guide to what data science is and how people use it. It walks you through the essential topics-what data analysis involves, which skills are useful, and how terms like "data analytics" and "machine learning" connect-without getting too technical too fast. Data science isn't just about crunching numbers, pulling data from a database, or running fancy algorithms. It's about asking the right questions, understanding the process from start to finish, and knowing what's possible (and what's not). This book teaches you all of that, while also introducing important topics like ethics, privacy, and security-because working with data means thinking about people, too. Whether you're a student exploring new skills, a professional navigating data-driven decisions, or someone considering a career change, this book is your friendly gateway into the world of data science, one of today's most exciting fields. No coding or programming experience? No problem. You'll build a solid foundation and gain the confidence to engage with data science concepts- just as AI and data become increasingly central to everyday life. What You Will Learn Know what foundational statistics is and how it matters in data analysis and data science Understand the data science project life cycle and how to manage a data science project Examine the ethics of working with data and its use in data analysis and data science Understand the foundations of data security and privacy Collect, store, prepare, visualize, and present data Identify the many types of machine learning and know how to gauge performance Prepare for and find a career in data science.
ISBN: 9798868811692Publication Date: 2025 -
Big Data Analytics
by
Successfully navigating the data-driven economy presupposes a certain understanding of the technologies and methods to gain insights from Big Data. This book aims to help data science practitioners to successfully manage the transition to Big Data. 　 Building on familiar content from applied econometrics and business analytics, this book introduces the reader to the basic concepts of Big Data Analytics. The focus of the book is on how to productively apply econometric and machine learning techniques with large, complex data sets, as well as on all the steps involved before analysing the data (data storage, data import, data preparation). The book combines conceptual and theoretical material with the practical application of the concepts using R and SQL. The reader will thus acquire the skills to analyse large data sets, both locally and in the cloud. Various code examples and tutorials, focused on empirical economic and business research, illustrate practical techniques to handle and analyse Big Data.　　 Key Features: 　 　 - Includes many code examples in R and SQL, with R/SQL scripts freely provided online. 　 - Extensive use of real datasets from empirical economic research and business analytics, with data files freely provided online. 　 - Leads students and practitioners to think critically about where the bottlenecks are in practical data analysis tasks with large data sets, and how to address them. 　 　 The book is a valuable resource for data science practitioners, graduate students and researchers who aim to gain insights from big data in the context of research questions in business, economics, and the social sciences.　
ISBN: 9781032458144Publication Date: 2023-08-01
Books on Artificial Intelligence
-
3D Data Science with Python
by
Our physical world is grounded in three dimensions. To create technology that can reason about and interact with it, our data must be 3D too. This practical guide offers data scientists, engineers, and researchers a hands-on approach to working with 3D data using Python. From 3D reconstruction to 3D deep learning techniques, you'll learn how to extract valuable insights from massive datasets, including point clouds, voxels, 3D CAD models, meshes, images, and more. Dr. Florent Poux helps you leverage the potential of cutting-edge algorithms and spatial AI models to develop production-ready systems with a focus on automation. You'll get the 3D data science knowledge and code to: Understand core concepts and representations of 3D data Load, manipulate, analyze, and visualize 3D data using powerful Python libraries Apply advanced AI algorithms for 3D pattern recognition (supervised and unsupervised) Use 3D reconstruction techniques to generate 3D datasets Implement automated 3D modeling and generative AI workflows Explore practical applications in areas like computer vision/graphics, geospatial intelligence, scientific computing, robotics, and autonomous driving Build accurate digital environments that spatial AI solutions can leverage Florent Poux is an esteemed authority in the field of 3D data science who teaches and conducts research for top European universities. He's also head professor at the 3D Geodata Academy and innovation director for French Tech 120 companies.
ISBN: 9781098161330Publication Date: 2025-05-20 -
Machine Learning Fundamentals
by
Machine Learning Fundamentals provides a comprehensive overview of data science, emphasizing machine learning (ML). This book covers ML fundamentals, processes, and applications, that are used as industry standards. Both supervised and unsupervised learning ML models are discussed. Topics include data collection and feature engineering techniques as well as regression, classification, neural networks (deep learning), and clustering. Motivated by the success of ML in various fields, this book is designed for a wide audience coming from various disciplines such as engineering, IT, or business and is suitable for those getting started with ML for the first time. This text can also serve as the main or supplementary text in any introductory data science course from any discipline, offering real-world applications and tools in all areas.
ISBN: 9781637427491Publication Date: 2025-03-18 -
AI-Assisted Programming for Web and Machine Learning
by
No detailed description available for "AI-Assisted Programming for Web and Machine Learning".
ISBN: 1835083897Publication Date: 2024-09-13
