Blog Tags: Data Science
The Data Scientist’s Guide to Product Thinking and Why it Matters
AI and data science are shifting from isolated projects to integrated, product-driven solutions that deliver real business impact. By embracing a product-thinking mindset, technologists can build scalable, adaptable AI platforms that evolve with user needs and market demands. Explore how this approach unlocks continuous innovation and drives smarter, more meaningful AI outcomes.
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How Data Science is Reimagining the Future of Insurance
Author: Minu Philip Until recently, Insurance has largely been perceived to be a traditional and slow-moving industry. However, the entire […]
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CECL in Loss Forecasting – Practical Approaches for Credit Cards
Discover how a combination of account-level forecasting, segmentation analysis, and rigorous model validation techniques can help credit card issuers address the unique challenges posed by CECL while reducing compliance costs and improving loss prediction accuracy.
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Enhancing Mental Healthcare: Machine Learning’s Role in Clinical Trials
Unveil the pivotal role of machine learning in revolutionizing mental health care through advanced clinical trials. Discover how innovative AI solutions, like speech analytics, enhance the evaluation of mental health treatments, contributing to more accurate and efficient healthcare outcomes.
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A Beginner’s Guide: Enter the World of Bayesian Belief Networks
Get to know what makes Bayesian Belief Networks (BBNs) a powerful tool in data science with an introduction to the concepts behind BBNs, their structure, applications, and benefits. Learn how BBNs use probabilistic reasoning to model complex systems and improve decision-making processes.
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How to Implement ML Models: Azure and Jupyter for Production
Learn how to implement Machine Learning models using Azure and Jupyter for production environments – from model development to deployment, including environment setup, training, and real-time predictions. Understand the advantages of using Azure’s robust infrastructure and Jupyter’s flexible interface to streamline the entire process.
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Demystifying Election Predictions: The Art and Science of Forecasting
Examine how data science techniques can predict election outcomes and explore the basics of Psephology, the science of election forecasting. Find out how the Tiger Analytics team has adapted and enhanced these techniques for the Indian context, including the innovative use of a ‘Poll of Polls’ approach.
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Decoding the 80/20 Rule: Data Science and the Pareto Principle
See how the Pareto Principle, or the 80-20 rule, can transform your data science practices and leverage this powerful tool for project prioritization, problem scoping, data planning, analysis, modeling, and business communication. Get information on how focusing on a few can optimize efforts and drive impactful results in analytics projects.
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AdTech for Dummies: Navigating the Advertising Technology Landscape
Discover why AdTech (Advertising Technology) plays a crucial role in digital advertising and why this is such a big deal for many industries. Get up close and personal to know all about ad networks, exchanges, DSPs, and SSPs and see how AdTech streamlines ad buying and enables precise audience targeting.
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KNIME: The Konstanz Information Miner
See how Gartner’s redefined Magic Quadrants outline the Enterprise Analytics pyramid, from foundational data warehouses to advanced analytics platforms. Know why KNIME stands out in the Advanced Analytics category and how it offers a powerful, intuitive alternative to coding-intensive tools that everyone should know about.
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