Module 1: Advanced Data Manipulation with Pandas and NumPy
- 1.1 Advanced DataFrame Operations
- MultiIndex and hierarchical indexing
- Advanced filtering, grouping, and aggregations
- Pivot tables and cross-tabulations
- Efficient use of
.apply(),.map(),.merge(), and.concat() - 1.2 Advanced Array Operations with NumPy
- Broadcasting and vectorization
- Memory layout of arrays and advanced slicing
- Linear algebra with NumPy
- Working with structured arrays
- 1.3 Performance Optimization
- Profiling and optimizing code using
pandasandnumpy - Memory management and reduction techniques
- Leveraging Cython and Numba for performance boosts
Module 2: Data Visualization and Exploration
- 2.1 Advanced Plotting with Matplotlib and Seaborn
- Creating complex multi-plot figures
- Customizing plots with advanced features (color maps, markers, annotations)
- 3D plotting with Matplotlib
- 2.2 Interactive Visualizations with Plotly and Bokeh
- Creating interactive dashboards and plots
- Working with geospatial data visualization
- Developing real-time data dashboards
- 2.3 Data Exploration and Feature Engineering
- Techniques for exploratory data analysis (EDA)
- Feature selection and dimensionality reduction techniques (PCA, LDA, t-SNE)
- Handling imbalanced data, outliers, and missing values
Module 3: Machine Learning with Scikit-Learn
- 3.1 Advanced Supervised Learning Techniques
- Ensemble methods (Bagging, Boosting, Random Forests, Gradient Boosting Machines, XGBoost, LightGBM)
- Hyperparameter tuning with Grid Search, Random Search, and Bayesian Optimization
- Model evaluation and cross-validation techniques
- 3.2 Unsupervised Learning and Clustering
- Clustering algorithms (K-means, DBSCAN, Agglomerative Clustering)
- Anomaly detection and outlier analysis
- Advanced dimensionality reduction techniques (Isomap, UMAP)
- 3.3 Model Interpretability and Explainability
- Feature importance and SHAP values
- Model-agnostic methods (LIME, partial dependence plots)
- Fairness and bias detection in machine learning models
Module 4: Deep Learning with TensorFlow and PyTorch
- 4.1 Neural Network Fundamentals
- Deep learning basics (Perceptrons, backpropagation, activation functions)
- Building and training neural networks with TensorFlow and PyTorch
- 4.2 Convolutional Neural Networks (CNNs)
- Fundamentals of CNNs for image classification and detection
- Transfer learning with pre-trained models (VGG, ResNet, EfficientNet)
- 4.3 Recurrent Neural Networks (RNNs) and Transformers
- RNNs and LSTM networks for sequential data (time series, NLP)
- Introduction to Transformer architectures (BERT, GPT)
- Attention mechanisms in neural networks
Module 5: Working with Big Data and Cloud Computing
- 5.1 Big Data Processing with Python
- Introduction to Apache Spark with PySpark
- Distributed computing concepts (MapReduce, Resilient Distributed Datasets)
- Working with Dask for parallel processing in Python
- 5.2 Cloud-Based Data Science
- Using cloud platforms (AWS, Azure, Google Cloud) for data science
- Working with managed ML services (AWS SageMaker, Azure ML, Google AI Platform)
- Scaling machine learning models in the cloud
Module 6: Natural Language Processing (NLP)
- 6.1 Advanced NLP Techniques
- Text preprocessing and feature extraction (TF-IDF, word embeddings)
- Deep learning for NLP (RNNs, LSTMs, Transformers)
- NLP applications: sentiment analysis, text generation, named entity recognition
- 6.2 Transfer Learning for NLP
- Using pre-trained language models (BERT, GPT, T5)
- Fine-tuning for specific NLP tasks
- Implementing attention mechanisms
Module 7: Time Series Analysis
- 7.1 Time Series Forecasting Techniques
- Traditional methods (ARIMA, SARIMA, Exponential Smoothing)
- Advanced models (LSTM, GRU, Prophet)
- Multivariate time series analysis and anomaly detection
Module 8: Advanced Topics and Specializations
- 8.1 Reinforcement Learning
- Basics of reinforcement learning (Q-learning, Policy Gradients)
- Application of RL in game development, robotics, and finance
- 8.2 AutoML and Model Deployment
- Automating the machine learning pipeline (AutoKeras, TPOT, H2O.ai)
- Deploying machine learning models using Flask, FastAPI, Docker, and Kubernetes
- 8.3 Ethical Considerations in Data Science
- Understanding data privacy, ethical AI, and responsible data handling
- Implementing privacy-preserving techniques (differential privacy, federated learning)
Assessment and Projects:
- Real-world data science project involving end-to-end pipeline creation (data acquisition, EDA, model building, deployment).
- Practical assignments on each module topic.
- Capstone project to consolidate all learning, potentially focusing on a novel domain or challenging problem.
This Level 3 syllabus is designed to cover both theoretical and practical aspects, allowing you to work on real-world data science problems and deepen your understanding of advanced concepts.
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