Advanced Structured Data Analysis Techniques
( 25 Modules )

Module #1
Introduction to Advanced Structured Data Analysis
Overview of structured data analysis, importance, and applications

Module #2
Review of Statistical Fundamentals
Refresher on statistical concepts, hypothesis testing, and confidence intervals

Module #3
Data Preprocessing and Cleaning
Handling missing values, outliers, and data transformation techniques

Module #4
Data Visualization for Insight
Using visualization to explore and understand structured data

Module #5
Introduction to Machine Learning
Supervised and unsupervised learning, model evaluation metrics

Module #6
Linear Regression Analysis
Simple and multiple linear regression, model assumptions, and inference

Module #7
Logistic Regression Analysis
Binary and multi-class logistic regression, model interpretation, and evaluation

Module #8
Decision Trees and Random Forests
Tree-based models, ensemble methods, and feature importance

Module #9
Clustering Analysis
K-means, hierarchical clustering, and density-based clustering methods

Module #10
Principal Component Analysis (PCA)
Dimensionality reduction, feature extraction, and visualizing high-dimensional data

Module #11
Time Series Analysis
Autoregressive models, moving average models, and ARIMA models

Module #12
Survival Analysis
Kaplan-Meier estimator, Cox proportional hazards model, and survival curve analysis

Module #13
Feature Engineering and Selection
Overview of feature engineering techniques and feature selection methods

Module #14
Model Evaluation and Selection
Metrics for evaluating model performance, cross-validation, and model selection techniques

Module #15
Hyperparameter Tuning and Optimization
Grid search, random search, and Bayesian optimization methods

Module #16
Handling Imbalanced Datasets
Strategies for dealing with class imbalance, including oversampling, undersampling, and cost-sensitive learning

Module #17
Advanced Data Visualization Techniques
Interactive visualizations, visualization best practices, and visualization for storytelling

Module #18
Big Data Analytics with Structured Data
Using Hadoop, Spark, and NoSQL databases for large-scale structured data analysis

Module #19
Case Study:Customer Segmentation Analysis
Applying advanced structured data analysis techniques to a real-world case study

Module #20
Case Study:Predicting Customer Churn
Using logistic regression, decision trees, and ensemble methods to predict customer churn

Module #21
Case Study:Sales Forecasting
Applying time series analysis and machine learning techniques to sales forecasting

Module #22
Best Practices for Data Analysis
Guidelines for data analysis, reporting, and presenting insights to stakeholders

Module #23
Advanced Topics in Structured Data Analysis
Overview of advanced topics, including deep learning, reinforcement learning, and transfer learning

Module #24
Domain Knowledge and Context
Importance of domain knowledge and understanding context in structured data analysis

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Advanced Structured Data Analysis Techniques career

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Advanced Structured Data Analysis Techniques ( 25 Modules )

Advanced Structured Data Analysis Techniques
( 25 Modules )