Cambridge
Academy
In London

Introduction

AI provides powerful tools to analyze large, complex, and unstructured data sets. This course prepares analysts to handle big data challenges with AI algorithms.

Course Objectives

By the end of this course, participants will be able to:

·        Understand AI’s role in big data analysis.

·        Apply AI for structured and unstructured data.

·        Use AI for real-time big data processing.

·        Explore AI cloud platforms for big data analytics.

Target Audience

This course is designed for:

·        Big data engineers

·        Data analysts

·        IT architects

·        Data scientists

Course Outlines

Day 1: AI & Big Data Overview

·        AI in big data ecosystems

·        Challenges in big data analysis

·        Tools: Hadoop + AI, Spark MLlib, AWS AI Services

·        Case study: AI in financial big data

·        Discussion: AI vs. traditional big data

Day 2: AI for Structured & Unstructured Data

·        AI for structured transactional data

·        AI for text analytics (NLP)

·        AI for image & video analysis

·        Workshop: Unstructured data processing with AI

·        Peer feedback session

Day 3: Real-Time Big Data Analytics with AI

·        AI for stream data analytics

·        Predictive AI in real-time monitoring

·        Tools: Apache Kafka, Spark Streaming, Azure AI

·        Case study: AI in IoT analytics

·        Exercise: Real-time AI pipeline simulation

Day 4: AI Cloud Platforms for Big Data

·        AWS, Azure, and Google Cloud AI platforms

·        AI for big data pipelines

·        Group project: AI big data solution design

·        Peer collaboration

·        Review session

Day 5: Future of AI in Big Data Analytics

·        AI for deep learning on big data

·        AI ethics and compliance in big data

·        Group presentations: Big data AI projects

·        Expert feedback

·        Wrap-up & certification