BOOKS - Starting Data Analytics with Generative AI and Python
Starting Data Analytics with Generative AI and Python - Artur Guja, Marlena Siwiak, Marian Siwiak 2025 PDF Manning Publications BOOKS
ECO~15 kg CO²

1 TON

Views
16719

Telegram
 
Starting Data Analytics with Generative AI and Python
Author: Artur Guja, Marlena Siwiak, Marian Siwiak
Year: 2025
Pages: 362
Format: PDF
File size: 15.5 MB
Language: ENG



Pay with Telegram STARS
Book Description: Starting Data Analytics with Generative AI and Python is a comprehensive guide that covers the fundamentals of data analytics using generative AI and Python programming language. The book provides readers with a solid understanding of the concepts and techniques of data analysis and their practical applications in various industries. It begins by introducing the basics of data analytics and gradually moves towards more advanced topics such as machine learning, deep learning, and neural networks. The book also explores the use of generative AI in data analytics and its potential applications in fields like image and video processing, natural language processing, and predictive modeling. The book is divided into four parts: Part 1 - Introduction to Data Analytics, Part 2 - Generative AI and Python Programming, Part 3 - Advanced Topics in Data Analytics, and Part 4 - Case Studies and Applications. Each part builds upon the previous one, providing a thorough understanding of the subject matter. The book concludes with a discussion on the future of data analytics and its potential impact on society. Throughout the book, the author uses real-world examples and case studies to illustrate the concepts and techniques discussed. The book also includes exercises and projects to help readers practice and apply their knowledge. The author's writing style is clear and concise, making it accessible to both beginners and experienced professionals in the field.
Starting Data Analytics with Generative AI and Python - всеобъемлющее руководство, охватывающее основы аналитики данных с использованием генеративного AI и языка программирования Python. Книга дает читателям твердое понимание концепций и методов анализа данных и их практического применения в различных отраслях. Он начинается с введения основ аналитики данных и постепенно движется к более продвинутым темам, таким как машинное обучение, глубокое обучение и нейронные сети. В книге также рассматривается использование генеративного ИИ в аналитике данных и его потенциальное применение в таких областях, как обработка изображений и видео, обработка естественного языка и прогнозное моделирование. Книга разделена на четыре части: Часть 1 - Введение в аналитику данных, Часть 2 - Генеративный ИИ и программирование на Python, Часть 3 - Расширенные темы в аналитике данных и Часть 4 - Тематические исследования и приложения. Каждая часть основывается на предыдущей, обеспечивая полное понимание предмета. Завершает книгу дискуссия о будущем аналитики данных и ее потенциальном влиянии на общество. На протяжении всей книги автор использует реальные примеры и тематические исследования для иллюстрации обсуждаемых концепций и методов. В книгу также вошли упражнения и проекты, которые помогут читателям потренироваться и применить свои знания. Авторский стиль письма ясен и лаконичен, что делает его доступным как для начинающих, так и для опытных специалистов в данной области.
Starting Data Analytics with Generative AI and Python è una guida completa che comprende le basi degli analisti dei dati utilizzando l'AI generativo e il linguaggio di programmazione Python. Il libro fornisce ai lettori una chiara comprensione dei concetti e dei metodi di analisi dei dati e delle loro applicazioni pratiche in diversi settori. Inizia con l'introduzione di basi di analisi dei dati e si muove gradualmente verso temi più avanzati come l'apprendimento automatico, l'apprendimento approfondito e le reti neurali. Il libro descrive anche l'uso dell'IA generale nell'analisi dei dati e la sua potenziale applicazione in settori quali l'elaborazione di immagini e video, l'elaborazione del linguaggio naturale e la simulazione predittiva. Il libro è suddiviso in quattro parti: Parte 1 - Introduzione all'analisi dei dati, Parte 2 - Generale IA e programmazione su Python, Parte 3 - Argomenti avanzati nell'analisi dei dati e Parte 4 - Studi di caso e applicazioni. Ogni parte si basa sulla parte precedente, garantendo una piena comprensione dell'oggetto. Completa la discussione sul futuro degli analisti dei dati e sul suo potenziale impatto sulla società. Durante tutto il libro, l'autore utilizza esempi reali e studi di caso per illustrare i concetti e i metodi discussi. Il libro include anche esercizi e progetti che aiuteranno i lettori ad esercitarsi e ad applicare le loro conoscenze. Lo stile di scrittura degli autori è chiaro e conciso, rendendolo accessibile sia ai principianti che ai professionisti esperti in questo campo.
''

You may also be interested in:

Intelligent Techniques for Predictive Data Analytics
Internet of Things and Data Analytics Handbook
Big Data Analytics Using Artificial Intelligence
Big Data Analytics Made Easy
Data Analytics & Visualization All-in-One For Dummies
Big Data Analytics for Sustainable Computing
Ultimate Java for Data Analytics and Machine Learning: Unlock Java|s Ecosystem for Data Analysis and Machine Learning Using WEKA, JavaML, JFreeChart, and Deeplearning4j (English Edition)
Ultimate Java for Data Analytics and Machine Learning Unlock Java|s Ecosystem for Data Analysis and Machine Learning Using WEKA, JavaML, JFreeChart, and Deeplearning4j
Ultimate Java for Data Analytics and Machine Learning Unlock Java|s Ecosystem for Data Analysis and Machine Learning Using WEKA, JavaML, JFreeChart, and Deeplearning4j
Data Mining and Predictive Analytics, 2nd Edition
Audit Analytics: Data Science for the Accounting Profession (Use R!)
Big Data Analytics A Social Network Approach
Big Data Analytics Applications in Business and Marketing
Marketing Strategy: Based on First Principles and Data Analytics
Interaction Data Analytics Methods, Tools, and Applications
Spatiotemporal Data Analytics and Modeling Techniques and Applications
Introduction to Data Analytics for Accounting, 2nd Edition
Modern Business Analytics Increasing the Value of Your Data with Python and R
Financial Data Analytics with R: Monte-Carlo Validation
Data Analytics in Bioinformatics A Machine Learning Perspective
Recent Trends and Future Direction for Data Analytics
DuckDB Up and Running Fast Data Analytics and Reporting
Spatiotemporal Data Analytics and Modeling Techniques and Applications
Analytics, Data Science, & Artificial Intelligence
High Performance Python for Data Analytics (MEAP)
Big Data Analytics A Practical Guide for Managers
Business Statistics Using Excel A Complete Course in Data Analytics
Data Analytics and Visualization in Quality Analysis using Tableau
Data Analytics for Intelligent Systems Techniques and solutions
AI-Based Data Analytics Applications for Business Management
Interaction Data Analytics Methods, Tools, and Applications
Data Analytics for Intelligent Systems Techniques and solutions
Recent Trends and Future Direction for Data Analytics
Data Science and Risk Analytics in Finance and Insurance
Introduction to Data Analytics for Accounting, 2nd Edition
Fundamentals of Data Analytics: With a View to Machine Learning
Big Data Analytics in Future Power Systems
Data Science and Risk Analytics in Finance and Insurance
Data Analysis in Medicine and Health using R (Analytics and AI for Healthcare)
Business Intelligence Guidebook: From Data Integration to Analytics