Data Analytics
1st Edition
9364447980
·
9789364447980
© 2025 | Published: November 30, 2024
Overview:This book guides readers through data science and analytics. It covers foundational principles, machine learning algorithms, and essential tools like Hadoop and SparkStreaming for real-time big data. Topics include regression analysis and ti…
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Chapter 1 Introduction to Data Science
Chapter 2 Introduction to Hadoop
Chapter 3 Introduction to Business Analytics
Chapter 4 Data Visualization
Chapter 5 Summary Measures
Chapter 6 Statistical Inference
Chapter 7 Chi-Square Tests
Chapter 8 Regression Analysis
Chapter 9 More Topics in Regression Analysis
Chapter 10 Logistic Regression
Chapter 11 Forecasting with Time Series Data
Chapter 12 Machine Learning Algorithms for Big Data Analytics
Chapter 13 Data Stream Mining and Real-Time Analytics Platform— SparkStreaming
Chapter 2 Introduction to Hadoop
Chapter 3 Introduction to Business Analytics
Chapter 4 Data Visualization
Chapter 5 Summary Measures
Chapter 6 Statistical Inference
Chapter 7 Chi-Square Tests
Chapter 8 Regression Analysis
Chapter 9 More Topics in Regression Analysis
Chapter 10 Logistic Regression
Chapter 11 Forecasting with Time Series Data
Chapter 12 Machine Learning Algorithms for Big Data Analytics
Chapter 13 Data Stream Mining and Real-Time Analytics Platform— SparkStreaming
Overview:
This book guides readers through data science and analytics. It covers foundational principles, machine learning algorithms, and essential tools like Hadoop and SparkStreaming for real-time big data. Topics include regression analysis and time series forecasting. Practical examples and hands-on exercises are provided. Readers gain both theoretical knowledge and valuable, applicable skills for today’s dynamic data landscape.
This book guides readers through data science and analytics. It covers foundational principles, machine learning algorithms, and essential tools like Hadoop and SparkStreaming for real-time big data. Topics include regression analysis and time series forecasting. Practical examples and hands-on exercises are provided. Readers gain both theoretical knowledge and valuable, applicable skills for today’s dynamic data landscape.