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<!DOCTYPE html>
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<h2>Introduction to Data Science</h2>
<p>Get your team up to speed with our zero to master Data Science training. Based on the materials lectured at top US universities.</p>
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Health insurance providers seek to control fraud perpetrated by healthcare providers, pharmacists, and patients, and manage investigations from detection to prosecution. Payers confront significant data challenges that block them from realizing optimal outcomes. The relevant information is typically locked in data silos and rigid data warehouses that cannot adapt quickly to new data sources. Analysis is frequently inaccessible to key decision makers within the enterprise. Researchers are often unable to investigate specific procedures, diagnoses, and patient profiles that contribute to macro-trends.
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Amidst these technical challenges, payers attempt to develop and refine predictive algorithms to eliminate waste, fraud, and abuse. They execute comparative effectiveness analyses and design and back-test plans through regression and strategy modeling. Palantir Insurance Analytics provides a unified solution for these workflows that overcomes technical obstacles and promotes better outcomes.
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Module 1: Exploratory Data Analysis and Data Visualization
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Module 2: Machine Learning for Classification and Regression
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<p>Payers handle many kinds of sensitive data, and Palantir Insurance Analytics provides unmatched data protection capabilities. Secure integrated data at the level of source records and implement regulations to ensure that investigators see only the data objects and attributes they are authorized to examine.</p>
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Module 3: Model Regularization, Feature Engineering and Selection
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<p>Payers handle many kinds of sensitive data, and Palantir Insurance Analytics provides unmatched data protection capabilities. Secure integrated data at the level of source records and implement regulations to ensure that investigators see only the data objects and attributes they are authorized to examine.</p>
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Module 4: Finding Hidden Relationships in Data with Dimensionality Reduction
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<p>Payers handle many kinds of sensitive data, and Palantir Insurance Analytics provides unmatched data protection capabilities. Secure integrated data at the level of source records and implement regulations to ensure that investigators see only the data objects and attributes they are authorized to examine.</p>
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Module 5: Data Clustering
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<p>Payers handle many kinds of sensitive data, and Palantir Insurance Analytics provides unmatched data protection capabilities. Secure integrated data at the level of source records and implement regulations to ensure that investigators see only the data objects and attributes they are authorized to examine.</p>
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Module 6: Modeling Sequential Events with Hidden Markov Models
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<p>Payers handle many kinds of sensitive data, and Palantir Insurance Analytics provides unmatched data protection capabilities. Secure integrated data at the level of source records and implement regulations to ensure that investigators see only the data objects and attributes they are authorized to examine.</p>
</div> <!-- /.panel-body -->
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Module 7: Deep Learning
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<p>Payers handle many kinds of sensitive data, and Palantir Insurance Analytics provides unmatched data protection capabilities. Secure integrated data at the level of source records and implement regulations to ensure that investigators see only the data objects and attributes they are authorized to examine.</p>
</div> <!-- /.panel-body -->
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Module 8: Hands-on Classification Pipeline Design and Implementation
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<p>Payers handle many kinds of sensitive data, and Palantir Insurance Analytics provides unmatched data protection capabilities. Secure integrated data at the level of source records and implement regulations to ensure that investigators see only the data objects and attributes they are authorized to examine.</p>
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