› › A Textbook Of Data Science For Class 9

A Textbook of Data Science for Class 9

Author : Shweta Mittal

Publisher : Goyal Brothers Prakashan

Page : pages

File Size : 13,42 MB

Release : 2021-09-01

Category : Juvenile Nonfiction

ISBN : 9392530501

Get eBook

Book Description

Data sCIenCe is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science or data-driven science enables better decision-making, predictive analysis, and pattern discovery. It lets you find the leading cause of a problem by asking the right questions and performing an exploratory study on the data. It models the data using various algorithms and communicates and visualizes the results via graphs, dashboards, etc. This book is based on the latest CBSE syllabus. The book is divided into two sections: Part A and Part B. Part A includes the “Employability Skills” and Part B covers the “Subject-specific Skills”. This book presents the concepts in a very simple language with easy-to-understand examples adapted from day-to-day utilization of Data science technology. The chapters are supplemented with figures and additional information in the form of “DID yoU knoW”. In between the chapters, the students are given a chance to revise and challenge their understanding with the help of “CheCk yoUr knoWleDGe” and fun activities. At the end of every chapter, Multiple Choice Questions, Short and Long answer questions are given. It includes HOTS (Higher Order Thinking Skills) questions and Applied Projects for advanced and practical kinds of questions.



R for Data Science

Author : Hadley Wickham

Publisher : "O'Reilly Media, Inc."

Page : 521 pages

File Size : 42,87 MB

Release : 2016-12-12

Category : Computers

ISBN : 1491910364

Get eBook

Book Description

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



Introduction to Data Science

Author : Rafael A. Irizarry

Publisher : CRC Press

Page : 836 pages

File Size : 18,19 MB

Release : 2019-11-20

Category : Mathematics

ISBN : 1000708039

Get eBook

Book Description

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.



Data Science and Machine Learning

Author : Dirk P. Kroese

Publisher : CRC Press

Page : 538 pages

File Size : 41,73 MB

Release : 2019-11-20

Category : Business & Economics

ISBN : 1000730778

Get eBook

Book Description

Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code



Data Science Class 9

Author : Shalini Harisukh

Publisher : Orange Education Pvt Ltd

Page : 482 pages

File Size : 11,61 MB

Release : 2021-10-11

Category : Computers

ISBN : 9391246370

Get eBook

Book Description

Data Science is a multidisciplinary field that also interacts with various other technologies like Artificial Intelligence, Machine Learning, Deep Learning, Internet of Things, etc. KEY FEATURES ● National Education Policy 2020 ● Activity: This section contains a topic based practical activity for the students to explore and learn. ● Higher Order Thinking Skills: This section contains the questions that are out of the box and helps the learner to think differently. ● Glossary: This section contains definition of common data science terms. ● Applied Project: This section contains an activity that applies the concepts of the chapter in real-life. ● Digital Solutions DESCRIPTION “Touchpad” Data Science book is designed as per the latest CBSE curriculum with an inter-disciplinary approach towards Mathematics, Statistics and Information Technology. The book inculcates real-life scenarios to explain the concepts and helps the students become better Data Science literates and pursue future endeavours confidently. To enrich the subject, this book contains different types of exercises like Objective Type Questions, Standard Questions and Higher Order Thinking Skills (HOTS). This book also includes Do You Know? and Activity which helps the students to learn and think outside the box. It helps the students to think and not just memorize, at the same time improving their cognitive ability. WHAT WILL YOU LEARN You will learn about: ● Communication Skills ● Self Management Skills ● ICT Skills ● Entrepreneurial Skills ● Green Skills ● Data ● Data Science ● Data Science Ethics ● Data Visualisation WHO THIS BOOK IS FOR Grade 9 TABLE OF CONTENTS 1. Part-A Employability Skills (a) Unit-1 Communication Skills-I (b) Unit-2 Self-Management Skills-I (c) Unit-3 ICT Skills-I (d) Unit-4 Entrepreneurial Skills-I (e) Unit-5 Green Skills-I 2. Part-B Subject Specific Skills (a) Unit-1 Introduction (b) Unit-2 Arranging and Collecting Data (c) Unit-3 Data Visualizations (d) Unit-4 Ethics in Data Science 3. Projects 4. Glossary 5. Model Test Paper



Foundations of Data Science

Author : Avrim Blum

Publisher : Cambridge University Press

Page : 433 pages

File Size : 21,44 MB

Release : 2020-01-23

Category : Computers

ISBN : 1108617360

Get eBook

Book Description

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.



Doing Data Science

Author : Cathy O'Neil

Publisher : "O'Reilly Media, Inc."

Page : 320 pages

File Size : 18,74 MB

Release : 2013-10-09

Category : Computers

ISBN : 144936389X

Get eBook

Book Description

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.



A Textbook of Data Science For Class 12 (A.Y. 2023-24)Onward

Author : Meenu Kohli

Publisher : Goyal Brothers Prakashan

Page : 154 pages

File Size : 40,52 MB

Release : 2023-05-20

Category : Juvenile Nonfiction

ISBN : 9357260072

Get eBook

Book Description

Computer technology, large number of people and dependence on Al has led to large transactions of data throughout the world. The need to study data science has never been greater. Various types of data have to be interacted with and various technical methods are used to open files received in these transactions. Data Science is itself very much grounded on Mathematics and Statistics, and cannot be understood without relying a lot on computer science. With these points in mind, the CBSE has suggested that the subject Data Science be added as a subject in Classes 11 and 12. We have come out with this book, "A Textbook of Data Science for class 11." The book is divided into two parts: Parts A (Employability Skills) and B (Subject Specific Skills). • Part A consists of the chapters on Communication Skills - Ill, Self Management Skills - Ill, ICT Skills - Ill, Entrepreneurial Skills - Ill and Green Skills - Ill. • Part B consists of data science related chapters. They are Ethics in Data Science, Assessing Data, Forecasting on Data, Randomization and Introduction to R Studio. The chapters have the following features: •:• Learning Objectives: It describes the goals to be achieved by the end of the chapter. •:• Chapter Contents: Concepts are explained to strengthen the knowledge base of the learners. •:• Check Your Knowledge: It gives question(s) to test the student's understanding of the chapter taught. •:• Test Yourself: It includes questions with headings such as Multiple choice questions, Match the following, Short Answer Type questions, Long answer type questions, Activity Zone and Group Discussion. This effort of ours will be of immense use for the students and teachers. Any feedback given regarding the quality of the material will be gracefully accepted and acknowledged. We intend to improve the book further, if possible. AUTHOR



Data Science

Author : Vijay Kotu

Publisher : Morgan Kaufmann

Page : 570 pages

File Size : 49,5 MB

Release : 2018-11-27

Category : Computers

ISBN : 0128147628

Get eBook

Book Description

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You'll be able to: - Gain the necessary knowledge of different data science techniques to extract value from data. - Master the concepts and inner workings of 30 commonly used powerful data science algorithms. - Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... - Contains fully updated content on data science, including tactics on how to mine business data for information - Presents simple explanations for over twenty powerful data science techniques - Enables the practical use of data science algorithms without the need for programming - Demonstrates processes with practical use cases - Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language - Describes the commonly used setup options for the open source tool RapidMiner



The Data Science Design Manual

Author : Steven S. Skiena

Publisher : Springer

Page : 456 pages

File Size : 19,98 MB

Release : 2017-07-01

Category : Computers

ISBN : 3319554441

Get eBook

Book Description

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)