Core Concepts in Data Science
Course 1: Advanced Statistical Methods for Data Science
- Topics:
- Probability distributions
- Bayesian inference
- Hypothesis testing and confidence intervals
- Linear models (ANOVA, GLMs)
- Multivariate statistics
Course 2: Data Mining and Predictive Modeling
- Topics:
- Classification (Decision Trees, k-NN, SVM)
- Association Rule Mining (Apriori, FP-Growth)
- Clustering (k-Means, DBSCAN, hierarchical)
- Model evaluation (cross-validation, ROC/AUC)
Course 3: Big Data Technologies
- Topics:
- Distributed computing principles
- Hadoop Ecosystem (HDFS, MapReduce, Pig, Hive)
- Spark for data processing
- Scalable machine learning with MLlib
- Data lakes and architectures for big data
- Hands-on Labs: Implementing big data solutions with Hadoop/Spark
Course 4: Programming for Data Science (Python & R)
- Topics:
- Advanced Python (functional programming, generators)
- Data handling with Pandas, NumPy
- Advanced R for data manipulation
- Integration with big data frameworks (PySpark, SparkR)
Specialized Techniques and Tools
Course 5: Machine Learning and Optimization
- Topics:
- Advanced supervised learning (Ensemble methods: Random Forest, Gradient Boosting)
- Support Vector Machines and kernel methods
- Hyperparameter tuning and optimization (GridSearch, Bayesian Optimization)
- Reinforcement learning basics
- Model interpretability (SHAP, LIME)
Course 6: Deep Learning and Neural Networks
- Topics:
- Neural network architectures (CNN, RNN, LSTM, GRU)
- Deep learning frameworks (TensorFlow, PyTorch, Keras)
- Transfer learning and fine-tuning
- Generative models (GANs, VAEs)
- NLP applications (transformers, BERT, GPT)
- Hands-on Labs: Implementing deep learning models on real datasets
Course 7: Data Engineering and Data Pipelines
- Topics:
- Data ingestion and ETL processes
- Workflow orchestration (Airflow, Luigi)
- Data storage (SQL vs NoSQL)
- Stream processing (Kafka, Flink)
- Data pipeline design for large-scale systems
Course 8: Research Methods in Data Science
- Topics:
- Formulating a research question
- Literature review techniques
- Quantitative and qualitative research methodologies
- Ethics in data science research
- Writing a research proposal
Advanced Topics and Electives
Course 9: Advanced Natural Language Processing (NLP)
- Topics:
- Word embeddings (Word2Vec, GloVe, FastText)
- Language models (BERT, GPT, T5)
- Text classification, sentiment analysis
- Named entity recognition (NER), part-of-speech tagging
- Sequence-to-sequence models for translation
Course 10: Probabilistic Graphical Models
- Topics:
- Bayesian networks
- Hidden Markov Models (HMMs)
- Markov Random Fields (MRFs)
- Inference and learning in graphical models
- Applications in image recognition, NLP
Elective Courses :
- Time Series Analysis and Forecasting
- ARIMA, SARIMA, Prophet
- Seasonal decomposition of time series
- GARCH models for financial data
- Reinforcement Learning and Multi-agent Systems
- Q-learning, SARSA, Deep Q-networks (DQN)
- Policy gradients, Actor-Critic methods
- Applications in robotics, games, and self-driving cars
- Data Visualization and Storytelling
- Principles of visual perception
- Designing interactive dashboards (Tableau, PowerBI)
- Advanced plotting libraries (Plotly, D3.js)
- Causal Inference and Experimental Design
- Causality vs correlation
- A/B testing, randomized controlled trials
- Difference-in-differences, instrumental variables
Capstone Project & Thesis
Course 11: Capstone Project / Thesis
- Deliverables:
- Project proposal
- Data analysis and modeling
- Final project report and code submission
- Oral defense or presentation
- Deliverables:
Research Thesis Option:
- Components:
- Literature review
- Experimentation and data collection
- Thesis writing and submission
- Oral defense in front of a committee
- Components: