Module #1 Introduction to Advanced Data Science Overview of advanced data science concepts and techniques, course objectives, and prerequisites.
Module #2 Review of Machine Learning Fundamentals Review of machine learning basics, including supervised and unsupervised learning, regression, classification, and clustering.
Module #3 Deep Learning with Neural Networks Introduction to deep learning, including neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Module #4 Natural Language Processing (NLP) with Deep Learning Application of deep learning to NLP tasks, including text classification, sentiment analysis, and language modeling.
Module #5 Advanced Feature Engineering Techniques Methods for extracting and selecting relevant features from data, including feature scaling, encoding, and selection.
Module #6 Unsupervised Learning with Dimensionality Reduction Techniques for dimensionality reduction, including PCA, t-SNE, and autoencoders.
Module #7 Time Series Analysis and Forecasting Introduction to time series analysis, including ARIMA, prophet, and LSTM models for forecasting.
Module #8 Working with Big Data:Hadoop and Spark Introduction to big data processing using Hadoop and Spark, including MapReduce and data pipelines.
Module #9 NoSQL Databases and Data Storage Overview of NoSQL databases, including MongoDB, Cassandra, and Redis, and data storage options for big data.
Module #10 Data Visualization with Python Techniques for data visualization using Python, including Matplotlib, Seaborn, and Plotly.
Module #11 Survival Analysis and Competing Risks Introduction to survival analysis, including Kaplan-Meier estimates, Cox models, and competing risks.
Module #12 Causal Inference and Instrumental Variables Methods for causal inference, including instrumental variables, regression discontinuity, and matching estimators.
Module #13 Recommendation Systems Introduction to recommendation systems, including content-based, collaborative filtering, and hybrid approaches.
Module #14 Graph Neural Networks and Network Analysis Application of deep learning to graph-structured data, including graph neural networks and network analysis.
Module #15 Explainable AI (XAI) and Model Interpretability Techniques for explaining and interpreting machine learning models, including LIME, SHAP, and TreeExplainer.
Module #16 Reinforcement Learning and Optimal Control Introduction to reinforcement learning, including Q-learning, policy gradients, and optimal control methods.
Module #17 Generative Adversarial Networks (GANs) and Generative Models Introduction to GANs and other generative models, including variational autoencoders (VAEs) and normalizing flows.
Module #18 Transfer Learning and Domain Adaptation Methods for transfer learning and domain adaptation, including fine-tuning and domain-invariant feature learning.
Module #19 Advanced Statistical Models:Bayesian and Frequentist Overview of advanced statistical models, including Bayesian modeling, generalized linear models, and generalized additive models.
Module #20 Computational Complexity and Scalability Analysis of computational complexity and scalability of machine learning algorithms, including big O notation and parallel processing.
Module #21 Data Quality and Data Wrangling Best practices for data quality and data wrangling, including data cleaning, preprocessing, and feature engineering.
Module #22 Ethics and Fairness in Machine Learning Discussion of ethical considerations and fairness in machine learning, including bias, transparency, and accountability.
Module #23 Advanced Topics in Computer Vision Advanced topics in computer vision, including object detection, segmentation, and tracking.
Module #24 Specialized AI Applications:Healthcare, Finance, and More Applications of AI and machine learning in various domains, including healthcare, finance, and more.
Module #25 Course Wrap-Up & Conclusion Planning next steps in Advanced Data Science Methods career
