Practical data science with Python : (Record no. 21985)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03768nam a2200229Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NULRC |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250520103033.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250520s9999 xx 000 0 und d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781801071970 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | NULRC |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA 76.73.P98 .G46 2021 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | George, Nathan |
| Relator term | author |
| 245 #0 - TITLE STATEMENT | |
| Title | Practical data science with Python : |
| Remainder of title | learn tools and techniques from hands-on examples to extract insights from data / |
| Statement of responsibility, etc. | Nathan George |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Birmingham, UK : |
| Name of publisher, distributor, etc. | Packt Publishing, Limited, |
| Date of publication, distribution, etc. | c2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxiii, 595 pages ; |
| Dimensions | 24 cm. |
| 365 ## - TRADE PRICE | |
| Price amount | USD52 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Includes index. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | An Introduction and the Basics -- Chapter 1: Introduction to Data Science -- The data science origin story -- The top data science tools and skills -- Python -- Other programming languages -- GUIs and platforms -- Cloud tools -- Statistical methods and math -- Collecting, organizing, and preparing data -- Software development -- Business understanding and communication -- Specializations in and around data science -- Machine learning -- Business intelligence -- Deep learning -- Data engineering -- Big data Statistical methods -- Natural Language Processing (NLP) -- Artificial Intelligence (AI) -- Choosing how to specialize -- Data science project methodologies -- Using data science in other fields -- CRISP-DM -- TDSP -- Further reading on data science project management strategies -- Other tools -- Test your knowledge -- Summary -- Chapter 2: Getting Started with Python -- Installing Python with Anaconda and getting started -- Installing Anaconda -- Running Python code -- The Python shell -- The IPython Shell -- Jupyter -- Why the command line? -- Command line basics Installing and using a code text editor -- VS Code -- Editing Python code with VS Code -- Running a Python file -- Installing Python packages and creating virtual environments -- Python basics -- Numbers -- Strings -- Variables -- Lists, tuples, sets, and dictionaries -- Lists -- Tuples -- Sets -- Dictionaries -- Loops and comprehensions -- Booleans and conditionals -- Packages and modules -- Functions -- Classes -- Multithreading and multiprocessing -- Software engineering best practices -- Debugging errors and utilizing documentation -- Debugging -- Documentation -- Version control with Git Code style -- Productivity tips -- Test your knowledge -- Summary -- Dealing with Data -- Chapter 3: SQL and Built-in File Handling Modules in Python -- Introduction -- Loading, reading, and writing files with base Python -- Opening a file and reading its contents -- Using the built-in JSON module -- Saving credentials or data in a Python file -- Saving Python objects with pickle -- Using SQLite and SQL -- Creating a SQLite database and storing data -- Using the SQLAlchemy package in Python -- Test your knowledge -- Summary -- Chapter 4: Loading and Wrangling Data with Pandas and NumPy Data wrangling and analyzing iTunes data -- Loading and saving data with Pandas -- Understanding the DataFrame structure and combining/concatenating multiple DataFrames -- Exploratory Data Analysis (EDA) and basic data cleaning with Pandas -- Examining the top and bottom of the data -- Examining the data's dimensions, datatypes, and missing values -- Investigating statistical properties of the data -- Plotting with DataFrames -- Cleaning data -- Filtering DataFrames -- Removing irrelevant data -- Dealing with missing values -- Dealing with outliers -- Dealing with duplicate values. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The book provides a one-stop solution for getting into data science with Python and teaches how to extract insights from data. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | BIG DATA |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Library of Congress Classification |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total checkouts | Full call number | Barcode | Date last seen | Copy number | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Library of Congress Classification | Machine Learning | LRC - Main | National University - Manila | General Circulation | 06/08/2024 | Purchased - Amazon | 52.00 | GC QA 76.73.P98 .G46 2021 | NULIB000019744 | 05/20/2025 | c.1 | 05/20/2025 | Books |