Master's Programme · Industry-Embedded
Master's in AI Big Data & Advance MLOps
12 months · 10 modules · 275 sessions (5 sessions/week) — from AI orientation and Advanced Python to Big Data & Cloud Engineering, Advanced MLOps, Deep Learning, NLP, Generative & Agentic AI, and an enterprise Capstone.
Duration
12 months (275 sessions)
Level
Foundation → Advanced
Mode
5 sessions per week
Master's Programme · Industry-Embedded
12 months (275 sessions) · 5 sessions per week
Learning Roadmap
Your journey, phase by phase
A structured path from fundamentals to industry-ready expert.
15 Days · 15 Sessions · M1
AI Foundation / Orientation
- Big Data & AI industry landscape and market demand
- Career roadmap: Data Analyst → Data Scientist → ML/MLOps Engineer → AI Architect
- Walkthrough of all 10 modules, tools and technology ecosystem
- Overview of Python, Databases, Data Science, Visualization, Big Data & Cloud
- Overview of MLOps, Deep Learning, CV, NLP, GenAI & Agentic AI
- Environment setup — Python, IDE, Git, Cloud accounts · Orientation assessment
1
2 Months · 40 Sessions · M2
Advanced Python & Database Foundation
- Advanced Python — comprehensions, iterators, generators, decorators, context managers, concurrency
- OOP — inheritance, polymorphism, encapsulation, ABCs, dunder methods, operator overloading
- File & exception handling, Pickle serialization, logging and debugging
- Modules, packages, virtual environments, unittest/pytest, project structuring
- Database fundamentals — RDBMS vs NoSQL, design, keys, normalization, MongoDB basics
- Advanced SQL — joins, subqueries, views, indexes, stored procedures, transactions (ACID)
- Python connectivity — sqlite3, mysql-connector, PyMongo, CRUD apps, SQLAlchemy ORM
2
55 Days · 55 Sessions · M3
ML, Data Science & Data Visualization
- Data science lifecycle · NumPy arrays, broadcasting · Pandas Series & DataFrames
- Data cleaning & wrangling — missing values, outliers, merges, groupby, pivots
- Statistics — descriptive, probability distributions, hypothesis testing, correlation
- EDA — univariate/bivariate/multivariate analysis, feature engineering
- Visualization — Matplotlib, Seaborn, Plotly, data storytelling
- ML foundations — Linear/Logistic Regression, Decision Trees, Random Forest, K-Means
- Model evaluation, feature scaling, cross-validation, Scikit-learn pipelines
3
40 Days · 40 Sessions · M4
Big Data & Cloud Data Engineering
- 5 V's of Big Data · distributed computing concepts and architecture
- Hadoop & HDFS — NameNode/DataNode, commands, MapReduce, YARN
- Hive — architecture, HiveQL, partitions, bucketing, UDFs, optimization
- Apache Spark — RDDs, DataFrames, Spark SQL, PySpark, Streaming, MLlib, tuning
- Kafka — topics, partitions, producers/consumers, Kafka Connect, real-time streaming
- Data Lake & Lakehouse architecture (Delta Lake concepts)
- ETL/ELT design · AWS S3, EC2, IAM, Glue, Redshift, EMR · Azure & GCP services
- Airflow orchestration · data governance, security & compliance
4
25 Days · 25 Sessions · M5
Big Data Analytics — Tableau & Power BI
- Tableau — calculated fields, table calculations, filters, parameters, sets
- Advanced visuals — maps, dual-axis, treemaps · LOD expressions · story points
- Power BI — Power Query, data modeling and relationships
- DAX — calculated columns, measures, time intelligence, performance optimization
- KPI design, interactive dashboards and BI reporting standards
- Publishing to Tableau Server/Online and Power BI Service with scheduled refresh
5
1.5 Months · 30 Sessions · M6
Advanced MLOps
- Advanced ML — ensembles, bagging & boosting, XGBoost, LightGBM, Gradient Boosting
- Hyperparameter tuning — GridSearch, RandomSearch, Optuna · Interpretability with SHAP & LIME
- ML pipelines · MLflow experiment tracking, model registry & versioning · Feature stores & DVC
- Docker — images, containers, Dockerfile, Compose, registries, Dockerizing ML apps
- Kubernetes — pods, deployments, services, scaling, Helm, deploying ML models
- CI/CD for ML (GitHub Actions/Jenkins) · Blue-Green, Canary, A/B deployment strategies
- Model serving with FastAPI/Flask, TensorFlow Serving & TorchServe
- Drift detection, automated retraining, logging, alerting & MLOps governance
6
2 Months · 40 Sessions · M7
Advanced Deep Learning & Computer Vision
- Neural network foundations — perceptron, activations, backpropagation, optimizers
- Regularization — Dropout, Batch Normalization · TensorFlow, Keras & PyTorch
- CNNs — convolution & pooling, LeNet, AlexNet, VGG, ResNet, Inception, augmentation
- Transfer learning, fine-tuning and image classification workflows
- RNN, LSTM, GRU, sequence modeling and time-series forecasting
- Object detection — IoU, YOLO, Faster R-CNN, SSD, pretrained models
- Segmentation — U-Net, Mask R-CNN · Face recognition, OCR, quantization & pruning
7
2 Months · 40 Sessions · M8
NLP & AI Bots
- NLP fundamentals — preprocessing, tokenization, BoW, TF-IDF, Word2Vec, GloVe, NER, POS
- Transformers & LLMs — attention, GPT/BERT foundation models, prompt engineering, fine-tuning
- Advanced LLMs — LoRA, QLoRA, PEFT, chain-of-thought, function calling, multi-modal LLMs
- RAG & vector databases — Pinecone, ChromaDB, FAISS, embeddings, hybrid search & re-ranking
- Applications — summarization, question answering, translation, topic modeling
- AI bots — LangChain, LlamaIndex, OpenAI/Anthropic APIs, memory & context management
- Voice AI — speech-to-text, text-to-speech, responsible deployment & evaluation
8
15 Days · 15 Sessions · M9
Generative AI & Agentic AI
- Generative AI concepts, use cases and foundation models
- GANs, Diffusion models, text-to-image and multimodal GenAI
- Responsible and ethical Generative AI practices
- AI agents — planning, memory, tools · LangChain Agents & AutoGen
- Tool-calling, function integration, agent evaluation and guardrails
- Multi-agent collaboration, orchestration and enterprise AI workflows
9
15 Days · 15 Sessions · M10
AI Research & Architecture + Capstone
- AI research methodology — problem formulation, literature review, documentation standards
- Enterprise AI architecture design principles
- Scalability, security and cost considerations · Industry best practices
- Capstone — problem statement, solution architecture, data, model, pipeline, deployment
- Final capstone presentation to an industry panel
10
Tools & Tech Stack
Tools you'll master
Python 3 SQLAlchemy MySQL SQLite MongoDB PyMongo NumPy Pandas Matplotlib Seaborn Plotly Scikit-learn Hadoop HDFS Hive Apache Spark PySpark Kafka Delta Lake AWS S3/EMR/Glue/Redshift Azure GCP Apache Airflow Tableau Power BI DAX XGBoost LightGBM Optuna SHAP LIME MLflow DVC Docker Kubernetes Helm GitHub Actions FastAPI TensorFlow Serving TorchServe TensorFlow Keras PyTorch YOLO U-Net OpenCV Transformers LoRA / QLoRA / PEFT LangChain LlamaIndex Pinecone ChromaDB FAISS OpenAI API Anthropic API AutoGen
Career Roles Mapped to Modules
Where this programme takes you
Every role below maps to specific modules — the programme is designed so you can specialise as you graduate.
Data Analyst
M3, M5
EDA, statistics, Tableau & Power BI dashboards
Data Engineer
M2, M4
Python, SQL/NoSQL and production ETL/ELT pipelines
Big Data Engineer
M4
Hadoop, Hive, Spark and Kafka at scale
Cloud Data Engineer
M4
AWS, Azure and GCP data platforms with Airflow
Data Scientist
M3, M6
ML modeling, ensembles and interpretability
MLOps Engineer
M6
MLflow, Docker, Kubernetes, CI/CD and monitoring
Deep Learning / CV Engineer
M7
CNNs, object detection and segmentation
NLP / GenAI Engineer
M8, M9
LLMs, RAG, LangChain and AI agents
AI Architect
M10
Enterprise AI architecture, research and delivery
What you'll be able to do
Career Outcomes
Build production-grade software using Advanced Python and database systems
Clean, wrangle, analyze and visualize real-world datasets end-to-end
Design and deploy large-scale Big Data and Cloud Data Engineering pipelines
Build advanced, interactive Tableau and Power BI dashboards for enterprise use
Develop, automate and monitor ML models using Advanced MLOps practices
Build and deploy Deep Learning, Computer Vision and NLP solutions at an advanced level
Design and deploy Generative AI and Agentic AI enterprise applications
Independently research, architect, document and present a real-world enterprise AI solution
Ready to build your future?
Join the next cohort of Master's in AI Big Data & Advance MLOps.