Career Direction

Key Learning Tracks & Highlights

Foundations

1. What is Data Science & AI, AI Goals: Logical Reasoning, NLP, Knowledge Representation
2. AI Software Development & Real-World Use Cases

AWS & Cloud

1. AWS Basics: EC2, S3, RDS, VPC, Cloud Market Overview & AWS Data Engineering

Data Science & AI Skills

1. Exploratory Data Analytics, Machine Learning & Deep Learning,
2. Amazon SageMaker & ML Services, ML Implementation, Window Functions & Ranking
3. AI/ML Services: Rekognition, Comprehend, Polly, Lex, Generative AI with Bedrock, Kendra, Lookout

Architecture & Technical Topics

1. Data Ingestion & Storage (S3, Kinesis, EFS), Data Transformation & Feature Engineering (EMR, Glue)
2. SageMaker Algorithms, Training & Tuning, MLOps, Security & Compliance
3. Real-Time Tools: Kinesis, Firehose, Lambda, Storage: S3 Policies, Classes & Encryption
4. Data Lakes vs. Warehouses vs. Lakehouses, Data Mesh & ETL Pipelines (Glue, Airflow)
5. Event-Driven Architecture (S3 Events, EventBridge)

Within 24 working hours, you will receive full access to the program from your enrollment time. For support, drop an email to [email protected]