Data Science Foundation Fundamentals

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Data Science Foundation Fundamentals

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Author : Mr. Ramkumar A
language : en
Publisher: Xoffencerpublication
Release Date : 2023-08-21

Data Science Foundation Fundamentals written by Mr. Ramkumar A and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-21 with Computers categories.

The academic field of computer science did not develop as a separate subject of study until the 1960s after it had been in existence since the 1950s. The mathematical theory that underpinned the fields of computer programming, compilers, and operating systems was one of the primary focuses of this class. Other important topics were the various programming languages and operating systems. Context-free languages, finite automata, regular expressions, and computability were a few of the topics that were discussed in theoretical computer science lectures. The area of study known as algorithmic analysis became an essential component of theory in the 1970s, after having been mostly overlooked for the majority of its existence up to that point in time. The purpose of this initiative was to investigate and identify practical applications for computer technology. At the time, a significant change is taking place, and a greater amount of attention is being paid to the vast number of different applications that may be utilized. This shift is the cumulative effect of several separate variables coming together at the same time. The convergence of computing and communication technology has been a major motivator, and as a result, this change may be primarily attributed to that convergence. Our current knowledge of data and the most effective approach to work with it in the modern world has to be revised in light of recent advancements in the capability to monitor, collect, and store data in a variety of fields, including the natural sciences, business, and other fields. This is necessary because of the recent breakthroughs in these capabilities. This is as a result of recent advancements that have been made in these capacities. The widespread adoption of the internet and other forms of social networking as indispensable components of people's lives brings with it a variety of opportunities for theoretical development as well as difficulties in actual use. Traditional subfields of computer science continue to hold a significant amount of weight in the field as a whole; however, researchers of the future will focus more on how to use computers to comprehend and extract usable information from massive amounts of data arising from applications rather than how to make computers useful for solving particular problems in a well-defined manner. This shift in emphasis is due to the fact that researchers of 1 | P a ge the future will be more concerned with how to use computers to comprehend and extract usable information from massive amounts of data arising from applications. This shift in emphasis is because researchers of the future will be more concerned with how to use the information they find. As a result of this, we felt it necessary to compile this book, which discusses a theory that would, according to our projections, play an important role within the next 40 years. We think that having a grasp of this issue will provide students with an advantage in the next 40 years, in the same way that having an understanding of automata theory, algorithms, and other topics of a similar sort provided students an advantage in the 40 years prior to this one, and in the 40 years after this one. A movement toward placing a larger emphasis on probabilities, statistical approaches, and numerical processes is one of the most significant shifts that has taken place as a result of the developments that have taken place. Early drafts of the book have been assigned reading at a broad variety of academic levels, ranging all the way from the undergraduate level to the graduate level. The information that is expected to have been learned before for a class that is taken at the undergraduate level may be found in the appendix. As a result of this, the appendix will provide you with some activities to do as a component of your project.

Fundamentals Of Data Science

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Author : Sanjeev J. Wagh
language : en
Publisher: CRC Press
Release Date : 2021-09-26

Fundamentals Of Data Science written by Sanjeev J. Wagh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-26 with Business & Economics categories.

Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.

Fundamentals Of Data Science

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Author : Mr.Desidi Narsimha Reddy
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-09-05

Fundamentals Of Data Science written by Mr.Desidi Narsimha Reddy and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-05 with Computers categories.

Mr.Desidi Narsimha Reddy, Data Consultant (Data Governance, Data Analytics: Enterprise Performance Management, AI & ML), Soniks consulting LLC, 101 E Park Blvd Suite 600, Plano, TX 75074, United States. Lova Naga Babu Ramisetti, EPM Consultant, Department of Information Technology, MiniSoft Empowering Techonolgy, 10333 Harwin Dr. #375e, Houston, TX 77036, USA. Mr.Harikrishna Pathipati, EPM Manager, Department of Information Technology, ITG Technologies, 10998 S Wilcrest Dr, Houston, TX 77099, USA.

Fundamentals Of Data Science

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Author : Dr.Vemuri Sudarsan Rao
language : en
Publisher: SK Research Group of Companies
Release Date : 2024-09-03

Fundamentals Of Data Science written by Dr.Vemuri Sudarsan Rao and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-03 with Computers categories.

Dr.Vemuri Sudarsan Rao, Professor & Head, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India. Dr.M.Sarada, Associate Professor, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India. Mrs.Masireddy Sadalaxmi, Associate Professor, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India.

Data Science Foundations And Hands On Experience

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Author : Fatwa Ramdani
language : en
Publisher: Springer Nature
Release Date : 2025-06-18

Data Science Foundations And Hands On Experience written by Fatwa Ramdani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-18 with Computers categories.

This book will take readers from foundational concepts to practical applications, enabling them to transform raw data into meaningful insights. It covers key skills such as data collection, cleaning, organization, exploration, analysis, and impactful presentation—core competencies for navigating today’s data-rich landscape. Each chapter is designed to build both theoretical understanding and hands-on expertise. The book’s unique dual-approach structure introduces foundational data science concepts, followed by exercises in RStudio using real-world datasets from social fields. This blend of theory and practice ensures readers grasp the ‘how’ and the ‘why’ behind data-driven research, making it ideal for students, researchers, and professionals seeking to enhance their analytical capabilities. Spatial data analysis stands out as one of the most unique in this book because it focuses on spatial data, a topic rarely covered in data science references. While there are many resources on data science, few explore the unique aspects of spatial data. Nowadays, most data includes location information, which can greatly enhance data science and decision-making. The final chapter will discuss critical topics in data ethics and reproducibility, encouraging readers to think responsibly about data use. By the end, readers will gain not only technical skills but also ethical awareness, empowering them to conduct rigorous, reliable, and socially conscious research. No prior experience with data science is required—just an eagerness to explore the power of data in understanding and shaping society. This textbook is suitable for adoption in both undergraduate and graduate classes. The book will help students build a solid theoretical foundation in data science while gaining hands-on experience with RStudio.

Foundations Of Data Science Principles And Applications

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Author : Dr. Punit Kumar Chaubey
language : en
Publisher: Academic Guru Publishing House
Release Date : 2024-07-24

Foundations Of Data Science Principles And Applications written by Dr. Punit Kumar Chaubey and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-24 with Study Aids categories.

The thorough reference "Foundations of Data Science: Principles and Applications" explores the fundamental ideas and real-world uses of data science. The book is appropriate for both novices and those wishing to expand their knowledge of the subject since it is organised to lead readers from the fundamentals to more complex subjects. The book is broken up into many important parts. The first few chapters cover foundational ideas such data types, statistical procedures, and data visualisation strategies. Big data analytics, data mining methods, and machine learning algorithms are introduced throughout the book. A combination of practical examples and theoretical insights are used to convey each subject, making sure that readers not only understand the ideas but also learn how to apply them. The book encourages readers to think about the wider effect of their work by addressing the ethical and social aspects of data science in addition to its technical content. Readers will have a solid foundation in data science at the conclusion of the book, along with the know-how to take on challenging data problems and make significant contributions to the field.

Data Science Foundations And Applications

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Author : Xintao Wu
language : en
Publisher: Springer Nature
Release Date : 2025-07-21

Data Science Foundations And Applications written by Xintao Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-21 with Computers categories.

The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10–13, 2025. The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.

Data Science Foundations Fundamentals

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Author : Barton Poulson
language : en
Publisher:
Release Date : 2019

Data Science Foundations Fundamentals written by Barton Poulson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

Fundamentals Of Data Science

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Author : Jugal K. Kalita
language : en
Publisher: Elsevier
Release Date : 2023-11-17

Fundamentals Of Data Science written by Jugal K. Kalita and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-17 with Mathematics categories.

Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics. Users will find the authors' research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, a number of descriptive learning tasks are included. - Presents the foundational concepts of data science along with advanced concepts and real-life applications for applied learning - Includes coverage of a number of key topics such as data quality and pre-processing, proximity and validation, predictive data science, descriptive data science, ensemble learning, association rule mining, Big Data analytics, as well as incremental and distributed learning - Provides updates on key applications of data science techniques in areas such as Computational Biology, Network Intrusion Detection, Natural Language Processing, Software Clone Detection, Financial Data Analysis, and Scientific Time Series Data Analysis - Covers computer program code for implementing descriptive and predictive algorithms

Data Science Essentials With R

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Author : Abhishek Das
language : en
Publisher: BPB Publications
Release Date : 2024-12-30

Data Science Essentials With R written by Abhishek Das and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-30 with Computers categories.

DESCRIPTION This book teaches you to draw insights from your data. In today's data-driven business landscape, making informed decisions requires effective data analysis. This book guides you through the steps to import, structure, and visualize your data in R, and apply statistical and ML algorithms to drive better insights. This book offers a thorough introduction to data science, starting with R programming basics and advancing to ML and data visualization. Learn to clean, explore, and transform data using tools like dplyr. Key statistical concepts like probability, hypothesis testing, and modeling are covered, providing a foundation for data-driven decisions. Discover supervised and unsupervised ML techniques, feature engineering, and model evaluation. The book also provides career guidance in data science, including skill-building tips and job search strategies, equipping you to excel in this growing field. By the end of this book, you will be able to confidently use R to prepare data for analysis and apply ML algorithms to make predictions and drive business decisions. KEY FEATURES ● Master R for effective data analysis and ML. ● Analyze data, identify patterns, and drive informed decision-making. ● Learn by doing hands-on R codes and applying ML techniques. WHAT YOU WILL LEARN ● Use R to clean, analyze, and visualize data effectively. ● Apply statistical techniques to find patterns and trends in data. ● Understand and implement key ML algorithms step-by-step. ● Data visualization techniques using ggplot2 to create informative visualizations. ● Strong foundation in statistical concepts, including probability theory, hypothesis testing, and statistical modeling. WHO THIS BOOK IS FOR This book is ideal for individuals with a basic understanding of programming and statistics who aspire to enter the field of data science. Professionals such as data analysts, software engineers, and researchers will find this book particularly valuable as it provides a practical approach to leveraging data for informed decision-making. TABLE OF CONTENTS 1. The Data Science Landscape 2. R Basics 3. Exploring Data 4. Wrangling Data 5. Working with Dates 6. Manipulating Strings 7. Visualizing Dat 8. Feature Engineering 9. Statistics and Probability 10. Introducing ML 11. Training Machine Learning Models 12. Building a Career in Data Science