Introduction to Big Data Analytics
( 24 Modules )

Module #1
What is Big Data?
Introduction to the concept of Big Data, its characteristics, and importance

Module #2
Types of Big Data
Overview of structured, unstructured, and semi-structured data and their examples

Module #3
Big Data Tools and Technologies
Introduction to popular Big Data tools and technologies, such as Hadoop, Spark, and NoSQL databases

Module #4
Data Ingestion and Processing
Methods and tools for ingesting and processing large datasets, including batch and stream processing

Module #5
Hadoop Ecosystem
In-depth introduction to the Hadoop ecosystem, including HDFS, MapReduce, and YARN

Module #6
Spark Overview
Introduction to Apache Spark, its features, and advantages

Module #7
Data Storage and Management
Overview of data storage options, including relational databases, NoSQL databases, and data warehousing

Module #8
Data Preprocessing and Cleaning
Techniques for preprocessing and cleaning Big Data, including data quality, data transformation, and data normalization

Module #9
Data Visualization
Introduction to data visualization techniques and tools, including Matplotlib, Seaborn, and Tableau

Module #10
Descriptive Analytics
Introduction to descriptive analytics, including summarization, aggregation, and grouping

Module #11
Inferential Statistics
Introduction to inferential statistics, including hypothesis testing and confidence intervals

Module #12
Predictive Modeling
Introduction to predictive modeling, including regression, decision trees, and random forests

Module #13
Machine Learning with Big Data
Introduction to machine learning with Big Data, including supervised and unsupervised learning

Module #14
Deep Learning with Big Data
Introduction to deep learning with Big Data, including neural networks and convolutional neural networks

Module #15
Big Data Analytics Use Cases
Real-world use cases for Big Data analytics, including customer analytics, IoT analytics, and supply chain analytics

Module #16
Big Data Security and Governance
Importance of security and governance in Big Data analytics, including data masking, encryption, and access control

Module #17
Big Data Analytics Tools and Platforms
Overview of popular Big Data analytics tools and platforms, including Splunk, Datadog, and Databricks

Module #18
Cloud-Based Big Data Analytics
Introduction to cloud-based Big Data analytics, including AWS, Azure, and Google Cloud

Module #19
Real-time Big Data Analytics
Introduction to real-time Big Data analytics, including streaming data processing and event-driven architecture

Module #20
Big Data Analytics for Business
Introduction to the business aspects of Big Data analytics, including ROI, cost-benefit analysis, and decision-making

Module #21
Case Studies in Big Data Analytics
Real-world case studies in Big Data analytics, including success stories and lessons learned

Module #22
Big Data Analytics Project Development
Guided project development in Big Data analytics, including project planning, data acquisition, and model deployment

Module #23
Big Data Analytics Best Practices
Best practices for Big Data analytics, including data quality, model evaluation, and ethics

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Introduction to Big Data Analytics career

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Introduction to Big Data Analytics ( 24 Modules )

Introduction to Big Data Analytics
( 24 Modules )