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Time Series Analysis with Python Cookbook : (Record no. 34609)

MARC details
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN	978-1801075541
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	519.55
Author Mark	ATW-T
100 ## - MAIN ENTRY--AUTHOR
Author Name	Atwan, Tarek A.
245 ## - TITLE STATEMENT
Title	Time Series Analysis with Python Cookbook :
Sub-Title	Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of Publication	Birmingham, UK
Name of publisher, distributor, etc.	Packt Publishing
Date of publication, distribution, etc.	2022
300 ## - PHYSICAL DESCRIPTION
Pages	604p.
500 ## - GENERAL NOTE
General note	"There are so many use cases when we need to deal with time series data—demand forecasting, predictive maintenance, and energy consumption, to name a few – and so every data scientist must be skilled in time series analysis.<br/><br/>Time series analysis is very difficult to master. That’s why I enjoyed Tarek A. Atwan’s book. I believe that by reading it, every data scientist will learn something new from the Python snippets dealing with time and date in Python (which is never as easy as it seems), running EDA, handling missing values, detecting outliers, and forecasting with statistical, machine learning, and deep learning models."<br/><br/>Adam Votava, Interim Chief Data and Analytics Officer at DataDiligence<br/><br/>"The book covers all the necessary details for time series data preparation, analysis, and forecasting, including how time series data is different from other data, how to ingest data from various sources and databases, how to deal with different time zones and custom business days, how to detect anomalies using statistical methods and visualizations, followed by developing advanced deep learning models for forecasting."<br/><br/>Overall, this is a great reference book for data science practitioners to get up to speed quickly on state-of-the-art time series data analysis and forecasting techniques!<br/><br/>Sadid Hasan, AI Lead at Microsoft<br/><br/>"I recommend reading this book to anyone at any level. The book has extensive chapters on how to read and write time series data using various technologies. This is a gap for most academically trained or MOOC-trained data analysts/ scientists. The book then describes statistical methodologies to handle time series forecasting. What I enjoyed is the pace at which the author gives enough background on a topic while also showing the reader how things are done practically. Later chapters describe the ML-based modeling of time series data.<br/><br/>I found this book to be a treasure trove of information on a set of very diverse approaches and topics. The more curious reader can later pick up a book on any of these chapters’ topics.<br/><br/>Highly recommended for newbies and veterans alike."<br/><br/>Shobeir Seddington, Principal Data Scientist at Gopuff & Harvard Business Review Advisor at Harvard Business Review<br/><br/>
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note	Include Index.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	Book

Holdings
Withdrawn status	Not for loan	Collection code	Permanent Location	Current Location	Shelving Location	Date acquired	Source of acquisition	Price	Inventory number	Full Call Number	Accession No./Barcode	Date last seen	Koha item type
		Computer Science	Air University Kharian Campus Library	Air University Kharian Campus Library	Computer Science	02/17/2025	Ali Book Service	3220.00	AU-KH 0356	519.55 ATW-T	AUKHP0450	03/13/2025	Book