Advanced Data Analytics and Visualization Techniques
( 25 Modules )

Module #1
Introduction to Advanced Data Analytics
Overview of the course, importance of data analytics, and introduction to advanced techniques

Module #2
Data Preprocessing and Cleaning
Techniques for handling missing values, outliers, and data transformation

Module #3
Data Visualization Fundamentals
Introduction to data visualization, types of plots, and best practices

Module #4
Working with Geospatial Data
Introduction to geospatial data, working with shapefiles, and mapping libraries

Module #5
Time Series Analysis
Introduction to time series analysis, time series components, and forecasting techniques

Module #6
Machine Learning for Data Analytics
Introduction to machine learning, supervised and unsupervised learning, and model evaluation metrics

Module #7
Clustering and Segmentation
Introduction to clustering, k-means, hierarchical clustering, and segmentation techniques

Module #8
Dimensionality Reduction
Introduction to dimensionality reduction, PCA, t-SNE, and feature selection

Module #9
Network Analysis
Introduction to network analysis, graph theory, and network visualization

Module #10
Text Analytics
Introduction to text analytics, text preprocessing, and topic modeling

Module #11
Advanced Data Visualization Techniques
Interactive visualizations, animations, and 3D visualizations

Module #12
Working with Big Data
Introduction to big data, Hadoop, and Spark

Module #13
Data Mining and Pattern Discovery
Introduction to data mining, pattern discovery, and association rule mining

Module #14
Survival Analysis
Introduction to survival analysis, Kaplan-Meier estimates, and Cox proportional hazards model

Module #15
Machine Learning for Predictive Modeling
Advanced machine learning techniques for predictive modeling, including gradient boosting and neural networks

Module #16
Advanced Regression Techniques
Advanced regression techniques, including generalized linear models and regression trees

Module #17
Decision Trees and Random Forests
Introduction to decision trees, random forests, and ensemble methods

Module #18
Unsupervised Learning Methods
Introduction to unsupervised learning methods, including k-means, hierarchical clustering, and DBSCAN

Module #19
Working with Sensor Data
Introduction to working with sensor data, including IoT devices and sensor fusion

Module #20
Advanced Data Visualization Tools
Introduction to advanced data visualization tools, including Tableau, Power BI, and D3.js

Module #21
Data Analytics for Social Media
Introduction to data analytics for social media, including sentiment analysis and social network analysis

Module #22
Data Analytics for Healthcare
Introduction to data analytics for healthcare, including medical imaging and clinical decision support systems

Module #23
Data Analytics for Finance
Introduction to data analytics for finance, including risk management and portfolio optimization

Module #24
Data Analytics for Marketing
Introduction to data analytics for marketing, including customer segmentation and campaign optimization

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Advanced Data Analytics and Visualization Techniques career

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Advanced Data Analytics and Visualization Techniques ( 25 Modules )

Advanced Data Analytics and Visualization Techniques
( 25 Modules )