Essentials Of Data Science
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Python Data Science Essentials
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Author : Alberto Boschetti
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-09-28
Python Data Science Essentials written by Alberto Boschetti and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-28 with Computers categories.
Gain useful insights from your data using popular data science tools Key FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is for If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
45 Essential Concepts In Data Science In 7 Minutes Each
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Author : Nietsnie Trebla
language : en
Publisher: Shelf Indulgence
Release Date :
45 Essential Concepts In Data Science In 7 Minutes Each written by Nietsnie Trebla and has been published by Shelf Indulgence this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
### Book Description: '45 Essential Concepts in Data Science in 7 Minutes Each' Unlock the world of data science with *45 Essential Concepts in Data Science in 7 Minutes Each*, an engaging and insightful guide designed for professionals and enthusiasts alike. This book offers succinct overviews of key topics that shape the data science landscape, allowing readers to grasp fundamental concepts quickly and effectively. #### Why This Book? In today's fast-paced environment, understanding data science is essential, but time is often limited. Each chapter is crafted to be read in just seven minutes, making it the perfect resource for busy individuals seeking to enhance their knowledge without the overwhelm of lengthy texts. From foundational theories to cutting-edge techniques, this book covers a comprehensive range of topics that are pivotal for success in the data-driven world. #### What You'll Learn: - **Introduction to Data Science**: Discover the basics and the significance of data science in various industries. - **Data Types and Data Structures**: Understand how data is categorized and organized. - **Data Collection Methods**: Explore the different ways to gather data effectively. - **Data Cleaning and Preprocessing**: Learn the essential steps to prepare data for analysis. - **Exploratory Data Analysis (EDA)**: Uncover patterns and insights through initial investigations. - **Descriptive Statistics**: Get to know the tools for summarizing and describing data sets. - **Data Visualization Techniques**: Master the art of making data accessible and interpretable through visuals. - **Probability Basics & Statistical Inference**: Dive into the concepts that form the backbone of data analysis. - **Hypothesis Testing & Confidence Intervals**: Develop your skills in making data-driven decisions. - **Regression Analysis, Classification Techniques, and Clustering Methods**: Familiarize yourself with essential modeling techniques. - **Machine Learning Algorithms**: Gain insight into Decision Trees, Support Vector Machines, Neural Networks, and more. - **Natural Language Processing (NLP)**: Explore the intersection of language and data. - **Feature Engineering & Dimensionality Reduction**: Learn to enhance and streamline your datasets. - **Model Evaluation Metrics, Overfitting and Underfitting**: Understand how to measure the success of your models. - **Big Data Technologies & Distributed Computing**: Navigate the complexities of modern data environments. - **Data Ethics and Privacy**: Reflect on the moral responsibilities of data scientists in today's world. - **Deployment of Data Science Models & Data Pipelines**: Understand how to implement models in real-world settings. - **Cloud Computing in Data Science & Working with APIs**: Learn how the cloud empowers data science infrastructure. - **Data Governance & Interpretation**: Explore frameworks for maintaining data integrity and conveying results effectively. - **Industry Applications and Future Trends**: Gain insights into how data science is transforming various sectors. Each chapter concisely introduces you to the topic, breaking down complex concepts into digestible insights. Whether you are a student, a working professional, or just someone curious about the data science field, this book equips you with the essential knowledge to thrive in a data-centric world. ### Get ready to embark on your data journey and transform your understanding of data science in just seven minutes at a time!
Essentials Of Data Science And Analytics
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Author : Amar Sahay
language : en
Publisher:
Release Date : 2021
Essentials Of Data Science And Analytics written by Amar Sahay and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Business categories.
This book combines the key concepts of data science and analytics to help you gain a practical understanding of these fields.
The Essentials Of Data Science Knowledge Discovery Using R
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Author : Graham J. Williams
language : en
Publisher: CRC Press
Release Date : 2017-07-28
The Essentials Of Data Science Knowledge Discovery Using R written by Graham J. Williams and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-28 with Business & Economics categories.
The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
Data Science Essentials For Dummies
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Author : Lillian Pierson
language : en
Publisher: John Wiley & Sons
Release Date : 2024-11-13
Data Science Essentials For Dummies written by Lillian Pierson and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-13 with Computers categories.
Feel confident navigating the fundamentals of data science Data Science Essentials For Dummies is a quick reference on the core concepts of the exploding and in-demand data science field, which involves data collection and working on dataset cleaning, processing, and visualization. This direct and accessible resource helps you brush up on key topics and is right to the point—eliminating review material, wordy explanations, and fluff—so you get what you need, fast. Strengthen your understanding of data science basics Review what you've already learned or pick up key skills Effectively work with data and provide accessible materials to others Jog your memory on the essentials as you work and get clear answers to your questions Perfect for supplementing classroom learning, reviewing for a certification, or staying knowledgeable on the job, Data Science Essentials For Dummies is a reliable reference that's great to keep on hand as an everyday desk reference.
Python Data Science Essentials Tools Techniques And Applications
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Author : Dr.R.Kavitha
language : en
Publisher: SK Research Group of Companies
Release Date : 2024-11-22
Python Data Science Essentials Tools Techniques And Applications written by Dr.R.Kavitha 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 2024-11-22 with Language Arts & Disciplines categories.
Dr.R.Kavitha, Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India. Dr.S.Ponmaniraj, Professor, Department of Computational Intelligence, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India. Mrs.D.Poovizhi, Assistant Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India. Ms.R.Vinodharasi, Assistant Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India. Mrs.C.Ramya, Assistant Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India.
Learn Data Science From Scratch
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Author : Pratheerth Padman
language : en
Publisher: BPB Publications
Release Date : 2024-02-15
Learn Data Science From Scratch written by Pratheerth Padman and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-15 with Computers categories.
Turn raw data into meaningful solutions KEY FEATURES ● Complete guide to master data science basics. ● Practical and hands-on examples in ML, deep learning, and NLP. ● Drive innovation and improve decision making through the power of data. DESCRIPTION Learn Data Science from Scratch equips you with the essential tools and techniques, from Python libraries to machine learning algorithms, to tackle real-world problems and make informed decisions. This book provides a thorough exploration of essential data science concepts, tools, and techniques. Starting with the fundamentals of data science, you will progress through data collection, web scraping, data exploration and visualization, and data cleaning and pre-processing. You will build the required foundation in statistics and probability before diving into machine learning algorithms, deep learning, natural language processing, recommender systems, and data storage systems. With hands-on examples and practical advice, each chapter offers valuable insights and key takeaways, empowering you to master the art of data-driven decision making. By the end of this book, you will be well-equipped with the essential skills and knowledge to navigate the exciting world of data science. You will be able to collect, analyze, and interpret data, build and evaluate machine learning models, and effectively communicate your findings, making you a valuable asset in any data-driven environment. WHAT YOU WILL LEARN ● Master key data science tools like Python, NumPy, Pandas, and more. ● Build a strong foundation in statistics and probability for data analysis. ● Learn and apply machine learning, from regression to deep learning. ● Expertise in NLP and recommender systems for advanced analytics. ● End-to-end data project from data collection to model deployment, with planning and execution. WHO THIS BOOK IS FOR This book is ideal for beginners with a basic understanding of programming, particularly in Python, and a foundational knowledge of mathematics. It is well-suited for aspiring data scientists and analysts. TABLE OF CONTENTS 1. Unraveling the Data Science Universe: An Introduction 2. Essential Python Libraries and Tools for Data Science 3. Statistics and Probability Essentials for Data Science 4. Data Mining Expedition: Web Scraping and Data Collection Techniques 5. Painting with Data: Exploration and Visualization 6. Data Alchemy: Cleaning and Preprocessing Raw Data 7. Machine Learning Magic: An Introduction to Predictive Modeling 8. Exploring Regression: Linear, Logistic, and Advanced Methods 9. Unveiling Patterns with k-Nearest Neighbors and Naïve Bayes 10. Exploring Tree-Based Models: Decision Trees to Gradient Boosting 11. Support Vector Machines: Simplifying Complexity 12. Dimensionality Reduction: From PCA to Advanced Methods 13. Unlocking Unsupervised Learning 14. The Essence of Neural Networks and Deep Learning 15. Word Play: Text Analytics and Natural Language Processing 16. Crafting Recommender Systems 17. Data Storage Mastery: Databases and Efficient Data Management 18. Data Science in Action: A Comprehensive End-to-end Project
Nsca S Essentials Of Sport Science
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Author : Duncan N. French
language : en
Publisher: Human Kinetics Publishers
Release Date : 2022
Nsca S Essentials Of Sport Science written by Duncan N. French and has been published by Human Kinetics Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Health & Fitness categories.
NSCA's Essentials of Sport Science provides the most contemporary and comprehensive overview of the field of sport science and the role of the sport scientist. It is a primary preparation resource for the Certified Performance and Sport Scientist (CPSS) certification exam.
Essentials Of Business Analytics
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Author : Bhimasankaram Pochiraju
language : en
Publisher: Springer
Release Date : 2019-07-10
Essentials Of Business Analytics written by Bhimasankaram Pochiraju and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-10 with Business & Economics categories.
This comprehensive edited volume is the first of its kind, designed to serve as a textbook for long-duration business analytics programs. It can also be used as a guide to the field by practitioners. The book has contributions from experts in top universities and industry. The editors have taken extreme care to ensure continuity across the chapters. The material is organized into three parts: A) Tools, B) Models and C) Applications. In Part A, the tools used by business analysts are described in detail. In Part B, these tools are applied to construct models used to solve business problems. Part C contains detailed applications in various functional areas of business and several case studies. Supporting material can be found in the appendices that develop the pre-requisites for the main text. Every chapter has a business orientation. Typically, each chapter begins with the description of business problems that are transformed into data questions; and methodology is developed to solve these questions. Data analysis is conducted using widely used software, the output and results are clearly explained at each stage of development. These are finally transformed into a business solution. The companion website provides examples, data sets and sample code for each chapter.
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.