Data Science & Analytics Research

Core Data Science Research Areas

Core data science research areas encompass statistical analysis, machine learning algorithms, and the application of these techniques to real-world datasets.

Exploration of advanced analytical methods like time series forecasting and anomaly detection are also key components of this area.

Statistical Analysis & Modeling

This section focuses on developing and applying statistical models to understand data patterns, relationships, and trends.

Key activities include regression analysis, hypothesis testing, and the design of experiments for data collection.

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Frequently asked questions

What are counterfactual explanations?

Counterfactual explanations describe how a dataset point would need to change in order to result in a different outcome.

What are causal inference methods?

Causal inference methods aim to determine cause-and-effect relationships from observational data, going beyond simple correlations.

How can feature importance be analyzed?

Feature importance analysis identifies the variables within a dataset that have the greatest influence on a model's predictions.

What are local interpretable models?

Local interpretable model-agnostic explanations (LIME) provide explanations for individual predictions by approximating the behavior of a complex model locally.

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Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation