Cravves

 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)

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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

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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

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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

Research Thesis Option:

• • Components:
    • Literature review
    • Experimentation and data collection
    • Thesis writing and submission
    • Oral defense in front of a committee