Advanced Statistical Data Analysis
( 25 Modules )

Module #1
Introduction to Advanced Statistical Data Analysis
Overview of the course, importance of advanced statistical data analysis, and review of basic statistical concepts

Module #2
Exploratory Data Analysis (EDA) Techniques
Advanced EDA techniques, including data visualization, summary statistics, and data transformation

Module #3
Hypothesis Testing and Confidence Intervals
Review of hypothesis testing and confidence intervals, including types of errors, power, and sample size determination

Module #4
Regression Analysis:Review and Extensions
Review of simple and multiple regression, including assumptions, inference, and model building

Module #5
Generalized Linear Models (GLMs)
Introduction to GLMs, including logistic regression, Poisson regression, and log-linear models

Module #6
Time Series Analysis:Basics
Introduction to time series analysis, including time series components, stationarity, and autocorrelation

Module #7
Time Series Analysis:Modeling and Forecasting
ARIMA, exponential smoothing, and vector autoregression models for time series forecasting

Module #8
Survival Analysis:Basics
Introduction to survival analysis, including censoring, survival functions, and hazard rates

Module #9
Survival Analysis:Models and Applications
Cox proportional hazards model, Kaplan-Meier estimator, and survival regression models

Module #10
Cluster Analysis:Basics
Introduction to cluster analysis, including types of clustering, distance metrics, and cluster validation

Module #11
Cluster Analysis:Hierarchical and K-Means Clustering
Hierarchical clustering, K-means clustering, and clustering evaluation metrics

Module #12
Dimensionality Reduction:PCA and t-SNE
Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction

Module #13
Unsupervised Learning:Density-Based Clustering
Density-based clustering methods, including DBSCAN and OPTICS

Module #14
Text Analytics:Basics
Introduction to text analytics, including text preprocessing, tokenization, and sentiment analysis

Module #15
Text Analytics:Topic Modeling and Named Entity Recognition
Topic modeling using Latent Dirichlet Allocation (LDA) and named entity recognition using spaCy

Module #16
Big Data Analytics:Introduction to Hadoop and Spark
Introduction to big data analytics, Hadoop, and Spark for large-scale data processing

Module #17
Big Data Analytics:Data Processing and Analysis
Data processing and analysis using Hadoop and Spark, including data ingestion, transformation, and visualization

Module #18
Machine Learning:Introduction to Supervised Learning
Introduction to supervised learning, including regression, classification, and model evaluation metrics

Module #19
Machine Learning:Supervised Learning Models
Supervised learning models, including decision trees, random forests, and support vector machines

Module #20
Machine Learning:Model Selection and Hyperparameter Tuning
Model selection and hyperparameter tuning using cross-validation and grid search

Module #21
Machine Learning:Unsupervised Learning and Deep Learning
Unsupervised learning using K-means and hierarchical clustering, and introduction to deep learning using neural networks

Module #22
Advanced Statistical Modeling:Bayesian Methods
Introduction to Bayesian statistics, including Bayes theorem, prior and posterior distributions, and Markov chain Monte Carlo (MCMC) methods

Module #23
Advanced Statistical Modeling:Spatial and Spatial-Temporal Analysis
Spatial and spatial-temporal analysis using geostatistics, kriging, and spatial econometrics

Module #24
Case Studies in Advanced Statistical Data Analysis
Real-world case studies in advanced statistical data analysis, including applications in healthcare, finance, and marketing

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

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Advanced Statistical Data Analysis ( 25 Modules )

Advanced Statistical Data Analysis
( 25 Modules )