Data & AI Masters Curriculum

Phase 1: Blended Program (First 18 Months)

In the first 18 months, students gain exposure to advanced coding techniques, data science, AI concepts, and cross-disciplinary topics from game design, cloud computing, leadership, and more.

Year 1: Advanced Coding + Data Science & AI Foundations (Months 1–12)

Months 1–6: Advanced Coding, Data Science, and AI Fundamentals

• LeetCode and Interview Preparation:

  • Weekly coding challenges based on LeetCode, Google, Tesla, OpenAI interview-style questions.

  • Key topics: Sorting algorithms, dynamic programming, recursion, graph theory, and complexity analysis (Big O).

  • Mock coding interviews and weekly feedback.

• Introduction to Data Science:

  • Data Wrangling and Exploration: Pandas, NumPy, and exploratory data analysis (EDA).

  • Data Visualization: Using Matplotlib, Seaborn, and Plotly to create insightful visualizations.

  • Real-world project: Analyze a dataset (e.g., from Kaggle or a public API) to discover trends and create a dashboard.

• AI and Machine Learning Basics:

  • Introduction to supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning.

  • Libraries: Scikit-learn, TensorFlow, Keras.

  • Real-world project: Build a simple machine learning model (e.g., a classifier to predict customer churn or sentiment analysis from text).

• Blending with Other Masters Programs:

  • Coding Masters: Algorithms in data science, including tree traversal, hashing, and dynamic programming in AI.

  • Cloud Masters: Introduction to cloud-based AI workflows (Google Cloud AI, AWS SageMaker, Azure AI).

  • Real-world project: Build a machine learning model and deploy it on a cloud platform.

Months 7–12: Data Science, AI Models, and Cross-Disciplinary Applications

• Advanced Machine Learning & Deep Learning:

  • Deep dive into neural networks, including convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for time series and NLP.

  • Transfer learning and fine-tuning pre-trained models (e.g., BERT, GPT).

  • Real-world project: Develop an image classification model using CNN or an NLP model to analyze text data (e.g., sentiment analysis or text generation).

• Big Data Technologies:

  • Introduction to big data tools: Hadoop, Apache Spark, and distributed data processing.

  • Working with large-scale datasets, data pipelines, and real-time data streaming.

  • Real-world project: Build a big data pipeline for processing and analyzing large datasets (e.g., social media data or sensor data from IoT).

• Blending with Game Design, Virtual Reality, and Leadership:

  • AI in Game Design: Using AI for NPC behavior, pathfinding, and dynamic content creation.

  • Leadership in Data-Driven Projects: Managing data science teams, building scalable AI solutions.

  • Real-world project: Lead a team to develop a data-driven project (e.g., a recommendation system or predictive model for game behavior).

Year 2: Specialization in Data Science & AI (Months 13–18)

Months 13–18: Specialization in Data Science and AI Applications

• Advanced Data Science Techniques:

  • Feature engineering, data preprocessing, and model evaluation techniques.

  • Hyperparameter tuning, cross-validation, and model selection.

  • Real-world project: Build and evaluate a complex machine learning model using real-world data (e.g., fraud detection or predictive analytics for healthcare).

• Deep Learning and Neural Networks:

  • Advanced deep learning architectures (e.g., GANs, Autoencoders).

  • Applications of deep learning in image generation, anomaly detection, and recommendation systems.

  • Real-world project: Develop a deep learning application (e.g., a GAN to generate images or a recommendation engine for personalized content).

• Natural Language Processing (NLP):

  • Advanced NLP techniques: Named entity recognition (NER), language models, transformers, and BERT/GPT architecture.

  • Applications in chatbot development, text summarization, and machine translation.

  • Real-world project: Create an NLP-based chatbot or sentiment analysis tool, integrating it into a web or mobile application.

• Data Engineering & Scalable AI:

  • Building scalable data pipelines using Apache Airflow, Apache Kafka, and Google Cloud Dataflow.

  • Implementing MLOps (Machine Learning Operations) for continuous deployment and monitoring of machine learning models.

  • Real-world project: Develop and deploy a scalable data pipeline to automate the training and updating of an AI model.

• Blending with Cloud, Startup, and Leadership:

  • AI and Cloud: Deploying machine learning models in production using cloud platforms (AWS, GCP, Azure).

  • Startup and AI Product Development: Turning AI models into products, creating MVPs for AI-driven solutions.

  • Real-world project: Develop an AI-driven product or MVP, integrating cloud services for real-time data processing.

Phase 2: Specialization and Real-World Applications (Months 19–36)

Months 19–30: Specialization in Data Science and AI

Students can choose an area of specialization based on their interests and career goals. Each specialization focuses on real-world applications and large-scale projects.

Specialization Option 1: Advanced AI and Deep Learning

• Advanced neural networks, including GANs, transformers, and reinforcement learning.

• Applications of AI in autonomous systems, robotics, and healthcare.

• Real-world project: Build an AI system for autonomous decision-making (e.g., self-driving car simulation or a robotic process automation system).

Specialization Option 2: Big Data and Distributed Systems

• Working with large datasets and distributed systems (e.g., Apache Spark, Hadoop).

• Real-time data processing and stream processing architectures (e.g., Apache Kafka, Flink).

• Real-world project: Build a real-time data pipeline to analyze streaming data (e.g., financial market data, IoT sensor streams).

Specialization Option 3: Data Engineering and MLOps

• Building and maintaining data infrastructure for machine learning models.

• Automating machine learning workflows (CI/CD for AI, model monitoring, and retraining).

• Real-world project: Implement an MLOps pipeline to automate the deployment and scaling of a machine learning model in production.

Specialization Option 4: NLP and Text Analytics

• Advanced natural language processing, including transformers, language models, and deep learning for NLP.

• Building language-driven applications (e.g., chatbots, text summarization, machine translation).

• Real-world project: Develop an NLP-based product, such as a multilingual chatbot or automatic text summarizer, and deploy it in a real-world environment.

Phase 3: 6-Month Live Project (Months 31–36)

Live Capstone Project (6 Months)

• Real-World Data & AI Project: Students will work on a live project, either with an industry partner or their own AI-driven startup concept.

• Team Collaboration: Students will work in teams to design, build, and deploy AI or data science solutions for real-world problems.

• Project Examples:

  • Building a predictive analytics tool for a healthcare system, using real-time patient data to predict outcomes and improve care.

  • Developing a recommendation engine for e-commerce platforms to suggest products based on user behavior and preferences.

  • Creating a real-time AI-driven solution for traffic management or smart city applications.

• Project Phases:

  • Phase 1 (Months 31–32): Research, data collection, and AI model design.

  • Phase 2 (Months 33–34): Development, model training, and iterative improvement.

  • Phase 3 (Months 35–36): Final deployment, monitoring, and presentation to industry stakeholders or investors.

Program Outcomes:

• Mastery in data science and artificial intelligence techniques, including machine learning, deep learning, and NLP.

• Hands-on experience with big data tools, distributed systems, and cloud-based AI workflows.

• Ability to build and deploy scalable AI solutions, integrating them into real-world applications across various industries.

• Leadership skills developed through team projects and real-world AI product development.

• A completed live project that showcases the student’s expertise in data science and AI, making them ready for industry roles or AI-driven entrepreneurship.