Graph & Big Data Analytics — Applied Path

BUNDLE OBJECTIVE
This bundle is designed for Data Analysts, Data Scientists, Big Data Engineers, Python Developers, and Machine Learning Engineers who want to advance their skills in graph and big data analytics. Through hands-on projects with Python, Neo4j, and Hadoop, learners will gain practical expertise in modeling, analyzing, and deriving insights from interconnected and large-scale datasets. By the end of this learning path, readers will be equipped to tackle complex analytics problems and implement scalable solutions across diverse domains.

KEY FEATURES
Hands-on graph analytics using Python and Neo4j
Master big data workflows and processing with Apache Hadoop
Learn practical applications of graph and big data analytics
Build scalable pipelines for enterprise-grade data projects
Transition from intermediate to expert in data analytics

DESCRIPTION
The Graph & Big Data Analytics — Applied Path bundle is a 2-book collection designed to help intermediate professionals become experts in graph and big data analytics.

Graph Data Science with Python and Neo4j — Learn the fundamentals and advanced concepts of graph data science. This book combines Python programming with Neo4j's graph database capabilities to perform network analysis, graph algorithms, and machine learning tasks. Hands-on examples enable readers to translate interconnected data into actionable business insights.

Ultimate Big Data Analytics with Apache Hadoop — Build a strong foundation in big data analytics using Hadoop. From understanding data lakes and warehouses to mastering HDFS, YARN, MapReduce, and Apache Tez, this book provides a mix of theory and practical exercises. Readers will gain the skills to handle large-scale data and implement robust analytics workflows.

Together, these books equip learners with the expertise to analyze, model, and visualize complex datasets using graph and big data technologies, empowering them to drive data-driven decisions in real-world applications.

WHAT WILL YOU LEARN
Apply graph data science techniques with Python and Neo4j
Design, query, and analyze graph networks for actionable insights
Implement big data analytics workflows using Hadoop
Manage large datasets efficiently with HDFS, YARN, and MapReduce
Integrate graph and big data techniques for enterprise analytics

WHO THIS BUNDLE IS FOR
Ideal for data scientists, analysts, engineers, Python developers, and machine learning professionals seeking hands-on skills in graph and big data analytics. Basic knowledge of Python and data analytics is recommended.

TABLE OF CONTENTS
Graph Data Science with Python and Neo4j
Introduction to Graph Data Science
Python for Graph Analytics
Neo4j Fundamentals and Data Modeling
Graph Algorithms and Machine Learning
Real-World Use Cases and Projects

Ultimate Big Data Analytics with Apache Hadoop
Introduction to Big Data and Hadoop Ecosystem
Data Lakes, Warehouses, and Storage Concepts
HDFS, YARN, and MapReduce
Apache Tez and Advanced Hadoop Workflows
Practical Exercises and Enterprise Applications