Data science / (Record no. 4720)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03531nam a22003017a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20241104144032.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220922b ph ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781119327639 |
| Qualifying information | paperback |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | PH-MaPCU |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 004 |
| Item number | P624 2017 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Pierson, Lillian |
| Relator term | author |
| 245 ## - TITLE STATEMENT | |
| Title | Data science / |
| Statement of responsibility, etc. | Lillian Pierson ; foreword by Jake Porway, founder and executive director of DataKind |
| 250 ## - EDITION STATEMENT | |
| Edition statement | Second edition |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Hoboken, New Jersey : |
| Name of producer, publisher, distributor, manufacturer | John Wiley and Sons, Inc., |
| Date of production, publication, distribution, manufacture, or copyright notice | 2017. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xvi, 364 pages : |
| Other physical details | illustrations ; |
| Dimensions | 23 cm |
| 490 1# - SERIES STATEMENT | |
| Series statement | For dummies |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Includes index. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Introduction -- Part 1: Getting Started with Data Science -- CHAPTER 1: Wrapping You Head around Data Science -- CHAPTER 2: Exploring Data Engineering Pipelines and Infrastructure -- CHAPTER 3: Applying Data-Driven Insights to Business and Industry -- Part 2: Using Data Science to Extract Meaning from Your Data -- CHAPTER 4: Machine Learning, Learning from Data with Your Machine -- CHAPTER 5: Math, Probability, and Statistical Method -- CHAPTER 6: Using Clustering to Subdivide Data -- CHAPTER 7: Modeling with Instances -- CHAPTER 8: Building Models That Operate Internet-of-Things Devices -- Part 3: Creating Data Visualizations That Clearly Communicate Meaning -- CHAPTER 9: Following the Principles of Data Visualization Design -- CHAPTER 10: Using D3.js for Data Visualization -- CHAPTER 11: Web-Based Applications for Visualization Design -- CHAPTER 12: Exploring Best Practices in Dashboard Design -- CHAPTER 13: Making Maps from Spatial Data -- Part 4: Computing for Data Science -- CHAPTER 14: Using Python for Data Science -- CHAPTER 15: Using Open Source R for Data Science -- CHAPTER 16: Using SQL in Data Science -- CHAPTER 17: Doing Data Science with Excel and Knime -- Part 5: Applying Domain Expertise to Solve Real-World Problems Using Data Science -- CHAPTER 18: Data Science in Journalism: Nailing Down the Five Ws (and an H) -- CHAPTER 19: Delving into Environmental Data Science -- CHAPTER 20: Data Science for Driving Growth in E-Commerce -- CHAPTER 21: Using Data Science to Describe and Predict Criminal Activity -- Part 6: The Parts of Tens -- CHAPTER 22: Ten Phenomenal Resources for Open Data -- CHAPTER 23: Ten Free Data Science Tools and Applications |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Begins by explaining large data sets and data formats, including sample Python code for manipulating data. The book explains how to work with relational databases and unstructured data, including NoSQL. The book then moves into preparing data for analysis by cleaning it up or "munging" it. From there the book explains data visualization techniques and types of data sets. Part II of the book is all about supervised machine learning, including regression techniques and model validation techniques. Part III explains unsupervised machine learning, including clustering and recommendation engines. Part IV overviews big data processing, including MapReduce, Hadoop, Dremel, Storm, and Spark. The book finishes up with real world applications of data science and how data science fits into organizations. |
| 524 ## - PREFERRED CITATION OF DESCRIBED MATERIALS NOTE | |
| Preferred citation of described materials note | Pierson, L. (2017). Data science (2nd ed.). John Wiley and Sons, Inc.. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Information retrieval |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Data mining |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Information technology |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Databases |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Shelving location | Date acquired | Full call number | Barcode | Date last seen | Copy number | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Junior High School | Philippine Christian University Manila | Circulation | 09/22/2022 | 004 P624 2017 | MJH12053 | 09/22/2022 | 1 | Books |