Mathe Basics F R Data Scientists
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A Mathematical Introduction To Data Science
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Author : Yi Sun
language : en
Publisher: Springer Nature
Release Date : 2025-07-09
A Mathematical Introduction To Data Science written by Yi Sun and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-09 with Computers categories.
This textbook provides a comprehensive foundation in the mathematics needed for data science for students and self-learners with a basic mathematical background who are interested in the principles behind computational algorithms in data science. It covers sets, functions, linear algebra, and calculus, and delves deeply into probability and statistics, which are key areas for understanding the algorithms driving modern data science applications. Readers are guided toward unlocking the secrets of algorithms like Principal Component Analysis, Singular Value Decomposition, Linear Regression in two and more dimensions, Simple Neural Networks, Maximum Likelihood Estimation, Logistic Regression and Ridge Regression, illuminating the path from mathematical principles to algorithmic mastery. It is designed to make the material accessible and engaging, guiding readers through a step-by-step progression from basic mathematical concepts to complex data science algorithms. It stands out for its emphasis on worked examples and exercises that encourage active participation, making it particularly beneficial for those with limited mathematical backgrounds but a strong desire to learn. This approach facilitates a smoother transition into more advanced topics. The authors expect readers to be proficient in handling numbers in various formats, including fractions, decimals, percentages, and surds. They should also have a knowledge of introductory algebra, such as manipulating simple algebraic expressions, solving simple equations, and graphing elementary functions, along with a basic understanding of geometry including angles, trigonometry and Pythagoras’ theorem.
Data Science Fundamentals And Practical Approaches
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Author : Dr. Gypsy Nandi
language : en
Publisher: BPB Publications
Release Date : 2020-06-02
Data Science Fundamentals And Practical Approaches written by Dr. Gypsy Nandi and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-02 with Computers categories.
Learn how to process and analysis data using PythonÊ KEY FEATURESÊ - The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. - The book is not just dealing with the background mathematics alone or only the programs but beautifully correlates the background mathematics to the theory and then finally translating it into the programs. - A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. DESCRIPTION This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems.Ê Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic.Ê WHAT WILL YOU LEARNÊ Perform processing on data for making it ready for visual plot and understand the pattern in data over time. Understand what machine learning is and how learning can be incorporated into a program. Know how tools can be used to perform analysis on big data using python and other standard tools. Perform social media analytics, business analytics, and data analytics on any data of a company or organization. WHO THIS BOOK IS FOR The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. TABLE OF CONTENTS 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics
Mathematics Of Intelligent Computing And Data Science
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Author : P. B. Vinod Kumar
language : en
Publisher: Springer Nature
Release Date : 2025-03-29
Mathematics Of Intelligent Computing And Data Science written by P. B. Vinod Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-29 with Computers categories.
This book comprises carefully selected contributions derived from plenary and invited talks delivered during the International Conference on Mathematics of Intelligent Computing and Data Science (ICMICDS-22), held at the Rajagiri School of Engineering and Technology, Cochin, India, from 15 to 17 September 2022. The volume encompasses a diverse spectrum of subjects, including but not limited to artificial intelligence, machine learning, data analysis, and optimization. Acting as a valuable forum, this volume offers a pivotal platform for both researchers and practitioners to showcase their scholarly endeavors. It facilitates vibrant discussions on the latest advancements and serves as an avenue for the exploration of novel research trajectories within the realm of intelligent computing and data science.
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.
Data Science
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Author : Pallavi Vijay Chavan
language : en
Publisher: CRC Press
Release Date : 2022-08-15
Data Science written by Pallavi Vijay Chavan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-15 with Business & Economics categories.
This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science. Key Features • Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science. • Presents predictive outcomes by applying data science techniques to real-life applications. • Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. • Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields. The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful.
Data Mining For Beginners
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Author : Agasti Khatri
language : en
Publisher: Educohack Press
Release Date : 2025-01-03
Data Mining For Beginners written by Agasti Khatri and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.
Data Mining for Beginners: A Programmer’s Guide delves into the world of data mining, a process of discovering patterns and trends in large volumes of data using various algorithms and techniques. This book offers a comprehensive introduction to data mining, focusing on important concepts and their implementation using Python, a popular programming language. We provide step-by-step guidance through Python code to help readers understand and apply data mining techniques. The book covers essential topics like clustering, anomaly detection, data visualization, and processing, making it easier to grasp these concepts and use them in various fields. By the end of the book, readers will be well-versed in data mining concepts and capable of implementing them with Python. What you will learn: • Introduction to data mining and its various concepts. • Data visualization and processing techniques. • The importance of statistics in data mining. • Different data mining algorithms and their implementation in Python. • Cluster analysis and anomaly detection using Python. • Data Cube Technology. • Future trends and research frontiers in data mining. Who the book is for: This book is ideal for programmers seeking to implement data mining algorithms using Python and for students looking for a solid introduction to data mining.
Interdisciplinary Mathematics Education
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Author : Brian Doig
language : en
Publisher: Springer
Release Date : 2019-02-22
Interdisciplinary Mathematics Education written by Brian Doig and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-22 with Education categories.
This open access book is the first major publication on the topic of “Interdisciplinary Mathematics Education” and arose from the work of the first International Topic Study Group of the same name at the ICME-13 conference in Hamburg in 2016. It offers extensive theoretical insights, empirical research, and practitioner accounts of interdisciplinary mathematics work in STEM and beyond (e.g. in music and the arts). Scholars and practitioners from four continents contributed to this comprehensive book, and present studies on: the conceptualizations of interdisciplinarity; implementation cases at schools and tertiary institutions; teacher education; and implications for policy and practice. Each chapter, and the book itself, closes with an assessment of the most significant aspects that those involved in policy and practice, as well as future researchers, should take into account.
The Math S Fix
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Author : Conrad Wolfram
language : en
Publisher: Wolfram Media
Release Date : 2020-06-10
The Math S Fix written by Conrad Wolfram and has been published by Wolfram Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-10 with Mathematics categories.
Now with a new ChatGPT-era foreword (ebook exclusive) that explains how The Math(s) Fix addresses key issues not only for the future of maths, but for AI-age education in general. This book uniquely puts the ChatGPT shock into perspective by offering the reformer's roadmap for reaction to policymakers, employers, parents, teachers, and students. Why are we all taught maths for years of our lives? Does it really empower everyone? Or fail most and disenfranchise many? Is it crucial for the AI age or an obsolete rite of passage? The Math(s) Fix: An Education Blueprint for the AI Age is a groundbreaking book that exposes why maths education is in crisis worldwide and how the only fix is a fundamentally new mainstream subject. It argues that today's maths education is not working to elevate society with modern computation, data science and AI. Instead, students are subjugated to compete with what computers do best, and lose. This is the only book to explain why being “bad at maths” may be as much the subject's fault as the learner's: how a stuck educational ecosystem has students, parents, teachers, schools, employers and policymakers running in the wrong direction to catch up with real-world requirements. But it goes further too—for the first time setting out a completely alternative vision for a core computational school subject to fix the problem and seed more general reformation of education for the AI age.
From Social Science To Data Science
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Author : Bernie Hogan
language : en
Publisher: SAGE
Release Date : 2022-11-23
From Social Science To Data Science written by Bernie Hogan and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-23 with Social Science categories.
From Social Science to Data Science is a fundamental guide to scaling up and advancing your programming skills in Python. From beginning to end, this book will enable you to understand merging, accessing, cleaning and interpreting data whilst gaining a deeper understanding of computational techniques and seeing the bigger picture. With key features such as tables, figures, step-by-step instruction and explanations giving a wider context, Hogan presents a clear and concise analysis of key data collection and skills in Python.
Python For Data Science
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Author : A. Lakshmi Muddana
language : en
Publisher: Springer Nature
Release Date : 2024-04-18
Python For Data Science written by A. Lakshmi Muddana and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-18 with Computers categories.
The book is designed to serve as a textbook for courses offered to undergraduate and graduate students enrolled in data science. This book aims to help the readers understand the basic and advanced concepts for developing simple programs and the fundamentals required for building machine learning models. The book covers basic concepts like data types, operators, and statements that enable the reader to solve simple problems. As functions are the core of any programming, a detailed illustration of defining & invoking functions and recursive functions is covered. Built-in data structures of Python, such as strings, lists, tuples, sets, and dictionary structures, are discussed in detail with examples and exercise problems. Files are an integrated part of programming when dealing with large data. File handling operations are illustrated with examples and a case study at the end of the chapter. Widely used Python packages for data science, such as Pandas, Data Visualization libraries, and regular expressions, are discussed with examples and case studies at the end of the chapters. The book also contains a chapter on SQLite3, a small relational database management system of Python, to understand how to create and manage databases. As AI applications are becoming popular for developing intelligent solutions to various problems, the book includes chapters on Machine Learning and Deep Learning. They cover the basic concepts, example applications, and case studies using popular frameworks such as SKLearn and Keras on public datasets