Big Data and Predictive Analytics
( 25 Modules )

Module #1
Introduction to Big Data
Overview of Big Data, its characteristics, and importance in business decision-making

Module #2
Big Data Technologies
Introduction to Hadoop, Spark, NoSQL databases, and other big data technologies

Module #3
Data Ingestion and Processing
Methods for ingesting and processing large datasets, including batch and real-time processing

Module #4
Data Storage and Management
Overview of data storage options, including HDFS, NoSQL databases, and data warehouses

Module #5
Data Quality and Governance
Best practices for data quality, data governance, and data security

Module #6
Introduction to Predictive Analytics
Overview of predictive analytics, its applications, and importance in business decision-making

Module #7
Supervised Learning
Introduction to supervised learning, including regression, classification, and model evaluation

Module #8
Unsupervised Learning
Introduction to unsupervised learning, including clustering, dimensionality reduction, and density estimation

Module #9
Model Evaluation and Selection
Metrics and techniques for evaluating and selecting predictive models

Module #10
Data Preparation for Modeling
Best practices for data preparation, feature engineering, and data transformation

Module #11
Decision Trees and Random Forests
Introduction to decision trees and random forests, including model interpretation and hyperparameter tuning

Module #12
Gradient Boosting and XGBoost
Introduction to gradient boosting and XGBoost, including model interpretation and hyperparameter tuning

Module #13
Neural Networks and Deep Learning
Introduction to neural networks and deep learning, including model interpretation and hyperparameter tuning

Module #14
Text Analytics and Natural Language Processing
Introduction to text analytics and natural language processing, including topic modeling and sentiment analysis

Module #15
Big Data Analytics Tools
Overview of big data analytics tools, including Apache Spark, Apache Flink, and Apache Hive

Module #16
Predictive Modeling with Big Data
Best practices for building predictive models with big data, including data sampling and distributed computing

Module #17
Data Visualization for Big Data
Best practices for data visualization with big data, including interactive visualization and storytelling

Module #18
Big Data and Predictive Analytics in Industry
Case studies of big data and predictive analytics applications in various industries

Module #19
Ethics and Responsible AI
Ethical considerations and responsible AI principles for big data and predictive analytics

Module #20
Scalability and Performance Optimization
Best practices for scalability and performance optimization in big data and predictive analytics

Module #21
Real-time Analytics and Streaming Data
Overview of real-time analytics and streaming data, including Apache Kafka and Apache Storm

Module #22
Cloud Computing for Big Data
Overview of cloud computing for big data, including Amazon Web Services, Microsoft Azure, and Google Cloud

Module #23
Big Data and Predictive Analytics Project
Guided project to apply big data and predictive analytics concepts to a real-world problem

Module #24
Advanced Topics in Big Data and Predictive Analytics
Advanced topics, including Explainable AI, Transfer Learning, and Graph Analytics

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Big Data and Predictive Analytics career

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Big Data and Predictive Analytics ( 25 Modules )

Big Data and Predictive Analytics
( 25 Modules )