MARC details
| 000 -LEADER |
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| fixed length control field |
05857cam a2200505 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
1348285935wcmSPRnew |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OCoLC |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251120142426.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION |
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m o d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr cnu|||unuua |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
221021s2022 sz a ob 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783031489556 |
| Qualifying information |
(paperback) |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(OCoLC)1348285935 |
| Canceled/invalid control number |
(OCoLC)1348485397 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
YDX |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
YDX |
| Modifying agency |
GW5XE |
| -- |
EBLCP |
| -- |
OCLCF |
| -- |
UKAHL |
| -- |
OCLCQ |
| -- |
N$T |
| -- |
OCLCO |
| -- |
AU |
| 049 ## - LOCAL HOLDINGS (OCLC) |
|---|
| Holding library |
Alfaisal Main Library |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q180.55 |
| Item number |
.Q36I48 2024 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ghosh, Chandril, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
INTRODUCTION TO DATA SCIENCE |
| Remainder of title |
A PYTHON APPROACH TO CONCEPTS, TECHNIQUES AND APPLICATIONS |
| Statement of responsibility, etc |
IGUAL, LAURA |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE STATEMENTS |
|---|
| Place of production, publication, distribution, manufacture |
Cham : |
| Name of producer, publisher, distributor, manufacturer |
Springer, |
| Date of production, publication, distribution, manufacture |
[2022] |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE STATEMENTS |
|---|
| Date of production, publication, distribution, manufacture |
©2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
246 pages |
| Other physical details |
illustrations (chiefly color) |
| 336 ## - CONTENT TYPE |
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| Content Type Term |
text |
| Content Type Code |
txt |
| Source |
rdacontent. |
| 337 ## - MEDIA TYPE |
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| Media Type Term |
computer |
| Media Type Code |
c |
| Source |
rdamedia. |
| 338 ## - CARRIER TYPE |
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| Carrier Type Term |
online resource |
| Carrier Type Code |
cr |
| Source |
rdacarrier. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Intro -- Acknowledgement -- Contents -- About the Author -- Chapter 1: Introduction -- 1.1 Before Getting Started -- 1.1.1 Overview (of Old Ways to Analyse Data and Some Problems Related to Them) -- 1.1.2 Who Am I? -- 1.1.3 How Did I Get There? -- 1.1.4 Who Is This Book For? -- 1.1.5 Who Is This Book NOT For? -- 1.1.6 What's Special You Get in This Book? -- 1.1.7 So, What Does This Book Have? -- 1.1.8 How to Best Make Use of This Book? -- 1.2 Types of Research Studies -- 1.2.1 Explanatory Research -- 1.2.2 Predictive Research -- 1.2.3 Exploratory Research -- 1.3 Data. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1.3.1 To Collect or Not Collect Your Own Data -- 1.3.2 Where to Get the Data From? -- 1.3.3 Ways in Which Data Is Divided -- 1.3.4 Five Lessons -- 1.4 Statistics: A Refresher Before Getting into Machine Learning -- References -- Chapter 2: Python Programming -- 2.1 But Do I Have to Learn to Code for Data Analysis? -- 2.2 How to Install Python? -- 2.3 Variables -- 2.4 Operators -- 2.4.1 Arithmetic Operators -- 2.4.2 Comparison Operators -- 2.5 Statements -- 2.6 Loops -- 2.7 Data Structure -- 2.8 Methods and Functions (Built-Ins) in Python -- 2.8.1 Methods -- 2.8.2 Function -- 2.9 Error Resolution. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
2.10 Last Words -- Chapter 3: Data Pre-processing -- 3.1 Introduction -- 3.2 Data Cleaning -- 3.2.1 Problem 1: Duplicate Columns and Categorical Variables -- 3.2.2 Problem 2: Outliers -- 3.2.3 Problem 3: Missing Values -- 3.3 Data Transformation -- 3.3.1 Converting Categorical Variables into Numeric Variables -- 3.3.2 Converting Continuous Variables into Categorical Variables -- 3.3.3 Feature Scaling -- 3.4 Data Reduction -- 3.4.1 Strategy 1 -- 3.4.2 Strategy 2 -- 3.4.3 Strategy 3 -- 3.4.4 Strategy 4 -- 3.4.5 Strategy 5 -- 3.5 Final Words -- References -- Chapter 4: Machine Learning. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
4.1 Introduction -- 4.2 Classification -- 4.2.1 Getting Started with Supervised Machine Learning -- 4.2.2 Machine Learning (Classifier): The Leak-Proof Approach -- 4.2.3 Confidence Interval -- 4.2.4 Choosing the Best Model for Classification -- 4.2.5 Optimising the Predictive Accuracies of the Model with Hyperparameter Tuning -- 4.3 Regression -- 4.3.1 Regression Using Machine Learning and How to Interpret the Results -- 4.3.2 Feature Importance -- 4.3.3 Exploratory Research Using Unsupervised Machine Learning -- 4.4 Clustering -- 4.4.1 Hierarchical Clustering -- 4.4.2 K-Means Clustering. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
4.5 Principal Component Analysis (PCA) -- 4.6 Rule Mining -- References -- Chapter 5: End Note -- Index. |
| 506 1# - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Access limited to UNC Chapel Hill-authenticated users. |
| Standardized terminology for access restriction |
Unlimited simultaneous users. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
This textbook presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or sentiment analysis. Topics and features: Provides numerous practical case studies using real-world data throughout the book Supports understanding through hands-on experience of solving data science problems using Python Describes concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data science Reviews a range of applications of data science, including recommender systems and sentiment analysis of text data Provides supplementary code resources and data at an associated website This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses. Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Associate Professor at the same institution. The authors wish to mention that some chapters were co-written by Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera |
| 590 0# - LOCAL NOTE (RLIN) |
|---|
| Local note |
Content provider: SpringerLink. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Psychology |
| General subdivision |
Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Psychology |
| General subdivision |
Research |
| -- |
Methodology. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| Source of heading or term |
fast. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Psychology |
| General subdivision |
Data processing |
| Source of heading or term |
fast. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Psychology |
| General subdivision |
Research |
| -- |
Methodology |
| Source of heading or term |
fast. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| Source of heading or term |
fast. |
| 655 #0 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Print books. |
| Source of term |
local |
| 9 (RLIN) |
4 |
| 773 1# - HOST ITEM ENTRY |
|---|
| Title |
OCLC WorldShare Collection Manager managed collection. SPRnew. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
BOOKS |
