Python for Data Science – Level 3 Syllabus

Posted on by Shakti Das

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 pandas and numpy
  • 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.

Would you like more details on any specific module?

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