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Author: Shweta Mittal
Publisher: Goyal Brothers Prakashan
ISBN: 9392530501
Category : Juvenile Nonfiction
Languages : en
Pages :
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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.
Author: Shweta Mittal
Publisher: Goyal Brothers Prakashan
ISBN: 9392530501
Category : Juvenile Nonfiction
Languages : en
Pages :
Get Book Here
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.
Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521
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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
Author: Rafael A. Irizarry
Publisher: CRC Press
ISBN: 1000708039
Category : Mathematics
Languages : en
Pages : 836
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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.
Author: Shalini Harisukh
Publisher: Orange Education Pvt Ltd
ISBN: 9391246370
Category : Computers
Languages : en
Pages : 482
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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
Author: Dirk P. Kroese
Publisher: CRC Press
ISBN: 1000730778
Category : Business & Economics
Languages : en
Pages : 538
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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
Author: Manish Soni
Publisher:
ISBN:
Category : Reference
Languages : en
Pages : 42
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Book Description
Prepare for success in data science with Data Science Class 10 Previous Years Unsolved Questions Paper Book! This essential resource compiles unsolved questions from previous years' exams, tailored for Class 10 students to strengthen their understanding and problem-solving skills in data science. Each question is designed to challenge students and enhance their analytical thinking, covering key topics in data handling, statistics, probability, and more. Ideal for self-assessment and exam practice, this book is perfect for students aiming to build confidence and excel in their data science studies.
Author: Manish Soni
Publisher:
ISBN:
Category : Reference
Languages : en
Pages : 54
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Book Description
Prepare for success in data science with Data Science Class 12 Previous Years solved Questions Paper Book! This essential resource compiles unsolved questions from previous years' exams, tailored for Class 12 students to strengthen their understanding and problem-solving skills in data science. Each question is designed to challenge students and enhance their analytical thinking, covering key topics in data handling, statistics, probability, and more. Ideal for self-assessment and exam practice, this book is perfect for students aiming to build confidence and excel in their data science studies.
Author: Manish Soni
Publisher:
ISBN:
Category : Reference
Languages : en
Pages : 57
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Book Description
Prepare for success in data science with Data Science Class 10 Previous Years solved Questions Paper Book! This essential resource compiles unsolved questions from previous years' exams, tailored for Class 10 students to strengthen their understanding and problem-solving skills in data science. Each question is designed to challenge students and enhance their analytical thinking, covering key topics in data handling, statistics, probability, and more. Ideal for self-assessment and exam practice, this book is perfect for students aiming to build confidence and excel in their data science studies.
Author: Manish Soni
Publisher:
ISBN:
Category : Reference
Languages : en
Pages : 44
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Book Description
Prepare for success in data science with Data Science Class 12 Previous Years Unsolved Questions Paper Book! This essential resource compiles unsolved questions from previous years' exams, tailored for Class 12 students to strengthen their understanding and problem-solving skills in data science. Each question is designed to challenge students and enhance their analytical thinking, covering key topics in data handling, statistics, probability, and more. Ideal for self-assessment and exam practice, this book is perfect for students aiming to build confidence and excel in their data science studies.
Author: Cathy O'Neil
Publisher: "O'Reilly Media, Inc."
ISBN: 144936389X
Category : Computers
Languages : en
Pages : 320
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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.
