Advanced Data Science Methods
( 25 Modules )

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

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Advanced Data Science Methods ( 25 Modules )

Advanced Data Science Methods
( 25 Modules )