Data Science Essentials Foundations And Analytics Fundamentals
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Data Science Essentials Foundations And Analytics Fundamentals
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Author : Venkata Naidu Udamala,
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-10-29
Data Science Essentials Foundations And Analytics Fundamentals written by Venkata Naidu Udamala, 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 2024-10-29 with Language Arts & Disciplines categories.
Venkata Naidu Udamala, Solution Architect, Cloudera, Irving, Texas, United
Foundations Of Data Science
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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23
Foundations Of Data Science written by Avrim Blum and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-23 with Computers categories.
Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.
Python Data Science Essentials
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Author : MARK JOHN LADO
language : en
Publisher: Amazon Digital Services LLC - Kdp
Release Date : 2024-03-18
Python Data Science Essentials written by MARK JOHN LADO and has been published by Amazon Digital Services LLC - Kdp this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-18 with Computers categories.
The field of data science has emerged as a critical component in extracting actionable insights and making informed decisions from vast amounts of data. This comprehensive guide explores the fundamentals of data science using the Python language, a versatile toolset widely adopted in the industry. The journey begins with an introduction to data science, outlining its principles, methodologies, and real-world applications. Next, the basics of Python programming are covered, providing a solid foundation for data manipulation and analysis. Data types and structures in Python are then explored, followed by an in-depth look at essential libraries such as NumPy and Pandas, which facilitate efficient data handling and manipulation. The importance of data visualization is emphasized through tutorials on Matplotlib and Seaborn, enabling effective communication of insights and trends. Data cleaning and preprocessing techniques are discussed, addressing common challenges in data quality and preparation. Statistical analysis is introduced as a fundamental aspect of data science, showcasing its applications in hypothesis testing, correlation analysis, and regression modeling using Python. Machine learning concepts are then explored, covering both supervised and unsupervised learning algorithms, including linear regression, decision trees, clustering, and dimensionality reduction. Model evaluation and validation techniques are essential for assessing model performance and generalization ability, ensuring robust and reliable predictions. Additionally, an introduction to deep learning with Python provides insights into advanced neural network architectures and their applications in solving complex problems. Handling big data is a critical aspect of modern data science, and this guide provides an overview of using Python and Spark for scalable and distributed data processing. Real-world case studies across various domains illustrate the practical applications of data science techniques, from e-commerce recommendation systems to healthcare analytics. Finally, best practices and tips for data science projects are discussed, highlighting key considerations for project success, including data exploration, feature engineering, model selection, and collaboration. By mastering these fundamentals, aspiring data scientists can embark on their journey with confidence, equipped to tackle real-world challenges and drive impactful insights from data.
Data Science Basics
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Author : Zoe Codewell
language : en
Publisher: Publifye AS
Release Date : 2025-01-13
Data Science Basics written by Zoe Codewell and has been published by Publifye AS this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-13 with Computers categories.
Data Science Basics offers a comprehensive introduction to transforming raw data into actionable insights, structured around three fundamental pillars: exploratory data analysis, statistical visualization, and machine learning applications. This practical guide stands out for its problem-first approach, introducing technical concepts as solutions to real-world analytical challenges rather than abstract theories, making it particularly valuable for aspiring analysts and business professionals. The book's progression is thoughtfully organized across four main sections, beginning with essential data manipulation techniques and advancing through visualization methods, statistical analysis, and machine learning implementations. What sets this resource apart is its emphasis on combining technical proficiency with critical thinking and clear communication, illustrated through diverse case studies from business, healthcare, and scientific research. The content bridges theoretical understanding with practical application through hands-on exercises using Python and R programming languages. Throughout the text, readers encounter real-world datasets and practical examples that demonstrate the universal applicability of data science methods. The book maintains accessibility while covering complex topics, using clear explanations and relevant examples to build a solid foundation in data literacy. By incorporating interactive exercises and end-of-chapter projects, it enables readers to develop practical problem-solving skills while mastering essential concepts in statistical analysis, data visualization, and machine learning fundamentals.
Foundations For Analytics With Python
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Author : Clinton W. Brownley
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-08-16
Foundations For Analytics With Python written by Clinton W. Brownley and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-16 with Business & Economics categories.
If you’re like many of Excel’s 750 million users, you want to do more with your data—like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats—by using Python. After author Clinton Brownley takes you through Python basics, you’ll be able to write simple scripts for processing data in spreadsheets as well as databases. You’ll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary. Create and run your own Python scripts by learning basic syntax Use Python’s csv module to read and parse CSV files Read multiple Excel worksheets and workbooks with the xlrd module Perform database operations in MySQL or with the mysqlclient module Create Python applications to find specific records, group data, and parse text files Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn Produce summary statistics, and estimate regression and classification models Schedule your scripts to run automatically in both Windows and Mac environments
Machine Learning And Data Science Basics
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date :
Machine Learning And Data Science Basics written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Your Essential Guide to Understanding Data-driven Technologies In a world inundated with data, the ability to harness its power through machine learning and data science is a vital skill. "Machine Learning and Data Science Basics" is your gateway to unraveling the complexities of these transformative technologies, offering a comprehensive introduction to the fundamental concepts that drive data-driven decision-making. About the Book: In an era where data has become the driving force behind innovation and growth, understanding the principles of machine learning and data science is no longer optional—it's essential. "Machine Learning and Data Science Basics" demystifies these disciplines, making them accessible to beginners while providing valuable insights for those looking to expand their knowledge. Key Features: Foundation Building: Start your journey by grasping the core concepts of data science, machine learning, and their intersection. Understand how data drives insights and empowers informed decisions. Data Exploration: Dive into data exploration techniques, learning how to clean, transform, and prepare data for analysis. Discover the crucial role data quality plays in obtaining accurate results. Machine Learning Essentials: Uncover the basics of machine learning algorithms, including supervised and unsupervised learning. Explore how algorithms learn patterns from data and make predictions or classifications. Feature Engineering: Learn the art of feature engineering—the process of selecting and transforming relevant data attributes to improve model performance and accuracy. Model Evaluation: Delve into model evaluation techniques to assess the performance of machine learning models. Understand metrics such as accuracy, precision, recall, and F1 score. Introduction to Data Science Tools: Familiarize yourself with essential data science tools and libraries, such as Python, NumPy, pandas, and scikit-learn. Gain hands-on experience with practical examples. Real-World Applications: Explore case studies showcasing how machine learning and data science are applied across industries. From recommendation systems to fraud detection, understand their impact on diverse domains. Why This Book Matters: In a landscape driven by data, proficiency in machine learning and data science is a competitive advantage. "Machine Learning and Data Science Basics" empowers individuals, students, and professionals to build a strong foundation in these fields, enabling them to contribute meaningfully to data-driven projects. Who Should Read This Book: Students and Beginners: Build a solid understanding of the principles underlying machine learning and data science. Professionals Seeking Knowledge: Enhance your expertise by familiarizing yourself with foundational concepts. Business Leaders: Grasp the potential of data-driven technologies to make informed strategic decisions. Embark on Your Data Journey: The era of data-driven decision-making is here to stay. "Machine Learning and Data Science Basics" equips you with the knowledge needed to embark on this exciting journey. Whether you're a novice eager to understand the basics or a professional looking to enhance your skill set, this book will guide you through the transformative landscape of machine learning and data science, setting the stage for continued learning and growth. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
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
Hbr Guide To Data Analytics Basics For Managers Hbr Guide Series
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Author : Harvard Business Review
language : en
Publisher: Harvard Business Press
Release Date : 2018-03-13
Hbr Guide To Data Analytics Basics For Managers Hbr Guide Series written by Harvard Business Review and has been published by Harvard Business Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-13 with Business & Economics categories.
Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes
R For Data Science
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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12
R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Foundations Of Data Science
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Author : Dr. Talluri Sunil Kumar, Dr. Radhika Pathi, Mr. Venu Aluri, K. Balasubramanian
language : en
Publisher: RK Publication
Release Date : 2025-05-02
Foundations Of Data Science written by Dr. Talluri Sunil Kumar, Dr. Radhika Pathi, Mr. Venu Aluri, K. Balasubramanian and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-02 with Computers categories.
Foundations of Data Science offers a comprehensive introduction to data analysis, statistical modeling, machine learning, and computational techniques. Designed for students and professionals, it blends theory with practical applications, emphasizing critical thinking and data-driven decision-making across disciplines. The book equips readers to solve real-world problems using modern data science tools.