Our Training
Data Science & AI Bootcamp
Duration
8 months (part-time) – Intensive, structured curriculum designed for working professionals.
Cost
€ 8,900 (incl. VAT) – Comprehensive training investment covering all modules and resources.
Start Dates
Multiple yearly intakes (next: February 2025) – Contact for upcoming schedule and enrollment deadlines.
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Core Modules
Module 1: Large-Scale Data Processing and Engineering
Dive into the engineering techniques that make handling big data feasible. In this module, you’ll master distributed computing, data lake storage, and real-time data pipelines, enabling you to manage and process data at scale with agility.
Detailed Focus:
Apache Spark & Hadoop: Understand the architecture and application of distributed frameworks.
Cloud Data Platforms (AWS/GCP): Explore data storage, processing, and scaling using leading cloud technologies.
Module 2: Artificial Intelligence & Advanced Deep Learning
This module focuses on the theoretical and practical aspects of deep learning models. You’ll learn how to implement and optimize neural networks, gaining skills in image recognition, voice-to-text applications, and automated decision systems.
Detailed Focus:
CNNs and RNNs: Develop expertise in image processing and sequential data handling.
GANs (Generative Adversarial Networks): Experiment with GANs for applications such as synthetic image creation.
Module 3: Advanced Natural Language Processing and LLMs
Explore the potential of large language models (LLMs) to reshape industries. This module teaches how to leverage transformer models for applications such as chatbots, automated summarization, and sentiment analysis.
Detailed Focus:
Transformer Models (BERT, GPT): Gain hands-on experience fine-tuning and deploying LLMs.
Prompt Engineering: Develop the skills to optimize model responses based on nuanced prompts.
Module 4: Model Deployment & MLOps Best Practices
Learn to transition from model development to deployment seamlessly. This module ensures your models are production-ready, incorporating scalability, reproducibility, and monitoring.
Detailed Focus:
Docker & Kubernetes: Containerize models for scalability and portability.
CI/CD Pipelines for ML: Establish automated workflows for model retraining and deployment.
Module 5: Machine Learning Strategy & Business Integration
Bridge the gap between technical expertise and business strategy by learning how to apply data science in strategic decision-making. This module covers model interpretability, experiment design, and KPI-driven modeling.
Detailed Focus:
Advanced Ensemble Methods: Improve accuracy with techniques like stacking and boosting.
Time-Series Forecasting: Predict trends using models suited to sequential data.
Final: Capstone Project
Apply your cumulative learning to a comprehensive, team-based project that addresses a real-world data science problem from end-to-end. Your project will include all stages: data ingestion, model training, deployment, and post-deployment monitoring.
Outcome:
Upon completion, you will have a portfolio-worthy project showcasing your ability to handle complex, high-impact problems, from problem definition to production deployment.
