Pal avishek (19 risultati)
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Da: GreatBookPrices, Columbia, U.S.A.GreatBookPrices
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Da: BargainBookStores, Grand Rapids, U.S.A.BargainBookStores
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EUR 48,18
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Paperback or Softback. Condizione: New. Practical Time-Series Analysis: Master Time Series Data Processing, Visualization, and Modeling using Python. Book.
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Da: California Books, Miami, U.S.A.California Books
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EUR 49,65
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Da: GreatBookPrices, Columbia, U.S.A.GreatBookPrices
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EUR 48,45
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Condizione: As New. Unread book in perfect condition.
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Da: PBShop.store US, Wood Dale, U.S.A.PBShop.store US
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EUR 56,16
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UNK. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: Rarewaves.com USA, London, Regno UnitoRarewaves.com USA
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EUR 56,95
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Digital. Condizione: New. Step by Step guide filled with real world practical examples.About This Book. Get your first experience with data analysis with one of the most powerful types of analysis-time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and im…plementation of Time-series methods using this example-rich guideWho This Book Is ForThis book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods.What You Will Learn. Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project. Develop an understanding of loading, exploring, and visualizing time-series data. Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series. Take advantage of exponential smoothing to tackle noise in time series data. Learn how to use auto-regressive models to make predictions using time-series data. Build predictive models on time series using techniques based on auto-regressive moving averages. Discover recent advancements in deep learning to build accurate forecasting models for time series. Gain familiarity with the basics of Python as a powerful yet simple to write programming languageIn DetailTime Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python.The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python.Style and approachThis book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.
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Da: PBShop.store UK, Fairford, Regno UnitoPBShop.store UK
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EUR 52,21
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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EUR 49,16
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Condizione: New. In.
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Da: Books Puddle, New York, U.S.A.Books Puddle
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EUR 60,72
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Condizione: New. pp. 244.
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Da: Chiron Media, Wallingford, Regno UnitoChiron Media
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EUR 45,67
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Paperback. Condizione: New.
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Da: Chiron Media, Wallingford, Regno UnitoChiron Media
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 48,12
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Paperback. Condizione: New.
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 48,62
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Condizione: New.
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 55,23
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Condizione: As New. Unread book in perfect condition.
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Da: moluna, Greven, Germaniamoluna
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 54,68
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Condizione: New. Über den AutorrnrnDr. Avishek Pal, PhD, is a software engineer, data scientist, author, and an avid Kaggler living in Hyderabad, India. He achieved his Bachelor of Technology degree in industrial engineering from the Indian Institute of Tec.
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Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 52,95
EUR 75,15 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Digital. Condizione: New. Step by Step guide filled with real world practical examples.About This Book. Get your first experience with data analysis with one of the most powerful types of analysis-time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and im…plementation of Time-series methods using this example-rich guideWho This Book Is ForThis book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods.What You Will Learn. Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project. Develop an understanding of loading, exploring, and visualizing time-series data. Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series. Take advantage of exponential smoothing to tackle noise in time series data. Learn how to use auto-regressive models to make predictions using time-series data. Build predictive models on time series using techniques based on auto-regressive moving averages. Discover recent advancements in deep learning to build accurate forecasting models for time series. Gain familiarity with the basics of Python as a powerful yet simple to write programming languageIn DetailTime Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python.The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python.Style and approachThis book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.
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- Print on Demand
Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 57,91
EUR 7,51 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 4 disponibili
Condizione: New. Print on Demand pp. 244.
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- Print on Demand
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 57,01
EUR 11,56 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: Brand New. 244 pages. 9.25x7.50x0.55 inches. In Stock. This item is printed on demand.
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- Print on Demand
Da: Biblios, frankfurt am main, GermaniaBiblios
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EUR 58,76
EUR 9,95 spedizioneSpedito da Germania a U.S.A.Quantità: 4 disponibili
Condizione: New. PRINT ON DEMAND pp. 244.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 55,76
EUR 62,32 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Step by Step guide filled with real world practical examples. Key Features Get your first experience with data analysis with one of the most powerful types of analysis-time-series. Find patterns in your data and predict the future patte…rn based on historical data. Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide Book Description Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. What you will learn Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use auto-regressive models to make predictions using time-series data Build predictive models on time series using techniques based on auto-regressive moving averages Discover recent advancements in deep learning to build accurate forecasting models for time series Gain familiarity with the basics of Python as a powerful yet simple to write programming language.
