| |
|
|
 Kent D. D. Lee, "Data Structures and Algorithms with Python " English | ISBN: 3319130714 | 2015 | 378 pages | MOBI | 5 MB This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. Features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those from a different language background.  Data Structures and Algorithms in Java by Kajal Singh English | 2026 | ASIN: B0GHPQWNX5 | 118 pages | pdf | 23 MB  Data Science with Python: A Beginner-Friendly Practical Guide: From Data Cleaning and Visualization to Machine Learning, Forecasting, and Real-World Projects - No Heavy Math, Step-by-Step Learning by prakash kumar English | 2026 | ASIN: B0GJQ8M35Z | 197 pages | pdf | 54 MB  Data Science with Julia: Build Models and Analyze Data by Grilo West English | 2026 | ASIN: B0GLLDTRNP | 353 Pages | PDF | 109 MB  Data Science in 7 days by Narayana Nemani English | 2026 | ASIN: B0GHYHZVCX | 195 pages | pdf | 48 MB Unlock the Power of Data - No Prior Experience Required!  Brennan Davis, "Data Science for All" English | ISBN: 0138323143 | 2025 | 600 pages | PDF | 84 MB We are all consumers of data, and you may become directly engaged with data work in your future career. Data Science for All, 1st Edition takes you on a thorough yet reader-friendly journey into the subject to help you navigate a data-rich world. The authors demystify data science, covering its entire lifecycle from preparation and analysis to storytelling. Designed for students of all majors and backgrounds, it distills the most applicable ideas from the component fields of statistics, computer science, and domain application, helping you apply them immediately to your everyday life. Learning by doing is emphasized through the authors' unique STAR framework and various tools that encourage a more engaging and practical experience.  Mohammed Mahmoud, "Data Science and Big Data in Biology, Physical Science and Engineering" English | ISBN: 3725800367 | 2024 | 238 pages | PDF | 17 MB Big Data analysis is one of the most contemporary areas of development and research in the present day. Tremendous amounts of data are generated every single day from digital technologies and modern information systems, such as cloud computing and Internet of Things (IoT) devices. Analysis of these enormous amounts of data has become a crucial need and requires a lot of effort in order to extract valuable knowledge for decision-making, which in turn will help both academia and industry.  Data Science and Artificial Intelligence (Communications in Computer and Information Science) by Chutiporn Anutariya, Marcello Bonsangue, Amalka Pinidiyaarachchi English | November 12, 2025 | ISBN: 9819544084 | 356 pages | True ePUB, PDF | 65 Mb The 22 full papers and 4 invited talks were included in this book were carefully reviewed and selected from 92 submissions.They were organized in topical sections as follows: Large Language Models and Advanced NLP, Computer Vision and Deep Learning for Image/Video Analysis, Healthcare, Medicine, and Survival Analysis, Time Series Forecasting and Predictive Modeling, Explainable AI and Bias Detection, Optimization, Efficiency, and Edge Computing, Domain-Specific Applications and Case Studies.  Theo Lynn, "Data Privacy and Trust in Cloud Computing: Building trust in the cloud through assurance and accountability " English | ISBN: 3030546594 | 2020 | 170 pages | MOBI | 655 KB This open access book brings together perspectives from multiple disciplines including psychology, law, IS, and computer science on data privacy and trust in the cloud. Cloud technology has fueled rapid, dramatic technological change, enabling a level of connectivity that has never been seen before in human history. However, this brave new world comes with problems. Several high-profile cases over the last few years have demonstrated cloud computing's uneasy relationship with data security and trust.  Data Pipelines with Apache Airflow: Orchestration for data and AI, 2nd Edition English | 2026 | ISBN: 1633436373 | 513 pages | True PDF | 64.29 MB Simplify, streamline, and scale your data operations with data pipelines built on Apache Airflow. |
:
