BOOKS - OS AND DB - Data Mining and Data Warehousing Principles and Practical Techniq...
Data Mining and Data Warehousing Principles and Practical Techniques - Parteek Bhatia 2019 PDF Cambridge University Press BOOKS OS AND DB
2 TON

Views
90020

Telegram
 
Data Mining and Data Warehousing Principles and Practical Techniques
Author: Parteek Bhatia
Year: 2019
Format: PDF
File size: 39.2 MB
Language: ENG



Pay with Telegram STARS
Book Description: Data Mining and Data Warehousing Principles and Practical Techniques In today's fast-paced digital world, data has become an essential part of our lives. From social media platforms to e-commerce websites, we generate vast amounts of data every day. This data can be used to gain insights into human behavior patterns, preferences, and trends. However, analyzing and making sense of this data requires specialized techniques and technologies known as data mining and data warehousing. In his book, "Data Mining and Data Warehousing Principles and Practical Techniques author [Author Name] provides a comprehensive guide to these two interconnected fields, offering readers a deep understanding of their fundamental concepts and practical applications. The book begins by exploring the evolution of technology and its impact on modern knowledge development. The author emphasizes the need for a personal paradigm that can help us understand the technological process of developing modern knowledge and its basis for the survival of humanity and the unification of people in a warring state. With this perspective, the reader is introduced to the concept of data mining and data warehousing, which are critical components of modern data analysis. Chapter 1: Introduction to Data Mining and Data Warehousing The first chapter provides an overview of data mining and data warehousing, explaining how they differ from traditional data processing methods. The author discusses the importance of data mining and data warehousing in today's digital world, highlighting their role in helping organizations make informed decisions based on large datasets. The chapter concludes with a discussion on the future of data mining and data warehousing, outlining the challenges and opportunities that lie ahead.
''

You may also be interested in:

The 3-D Global Spatial Data Model Principles and Applications, Second Edition
Data Science from Scratch First Principles with Python, 2nd Edition
Text Data Management and Analysis A Practical Introduction to Information Retrieval and Text Mining
Ultimate Salesforce Data Cloud for Customer Experience Explore, Implement, and Elevate B2C Experiences Through Customer Data Innovations Using Salesforce Data Cloud
Ultimate Salesforce Data Cloud for Customer Experience Explore, Implement, and Elevate B2C Experiences Through Customer Data Innovations Using Salesforce Data Cloud
Networks Attack Detection on 5G Networks using Data Mining Techniques (Wireless Communications and Networking Technologies)
Networks Attack Detection on 5G Networks using Data Mining Techniques
Networks Attack Detection on 5G Networks using Data Mining Techniques
Principles of Data Transfer Through Communications Networks, the Internet and Autonomous Mobiles
Environmental Data Analysis with MatLab or Python Principles, Applications, and Prospects
Feature Engineering for Machine Learning Principles and Techniques for Data Scientists
Learn Data Mining Through Excel A Step-by-Step Approach for Understanding Machine Learning Methods, 2nd Edition
Learn Data Mining Through Excel A Step-by-Step Approach for Understanding Machine Learning Methods, 2nd Edition
Hypothesis Generation and Interpretation Design Principles and Patterns for Big Data Applications
Big data Principles and best practices of scalable realtime data systems
Big Data and Edge Intelligence for Enhanced Cyber Defense Principles and Research
Hypothesis Generation and Interpretation Design Principles and Patterns for Big Data Applications
Data Mining and Analytics in Healthcare Management: Applications and Tools (International Series in Operations Research and Management Science, 341)
Hands On With Google Data Studio A Data Citizen|s Survival Guide
Integrity Constraints on Rich Data Types (Synthesis Lectures on Data Management)
Unifying Business, Data, and Code Designing Data Products With JSON Schema
Confident Data Skills Master the Fundamentals of Working with Data and Supercharge Your Career
Cloud Data Center Network Architectures and Technologies (Data Communication Series)
Azure Data Factory by Example Practical Implementation for Data Engineers, 2nd Edition
Becoming a Data Head How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning
Streaming Data Mesh: A Model for Optimizing Real-Time Data Services
Data in Context: Models as Enablers for Managing and Using Data (The Enterprise Engineering Series)
Controlling Privacy and the Use of Data Assets - Volume 2 What is the New World Currency – Data or Trust?
SQL for Data Analysis Advanced Techniques for Transforming Data into Insights (Final)
Introducing Data Science Big data, machine learning, and more, using Python tools
Explainable Machine Learning for Geospatial Data Analysis A Data-Centric Approach
Practical Synthetic Data Generation Balancing Privacy and the Broad Availability of Data
Data Analytics for Absolute Beginners A Deconstructed Guide to Data Literacy, Second Edition
Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things
Unifying Business, Data, and Code: Designing Data Products With Json Schema
The Functional Approach to Data Management: Modeling, Analyzing and Integrating Heterogeneous Data
Data Engineering with AWS: A Comprehensive Guide to Building Robust Data Pipelines
From Data To Profit: How Businesses Leverage Data to Grow Their Top and Bottom Lines
Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python
Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning