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Analyticsindiamag
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75% of Consumers, 97% of Doctors in India Trust AI, Says ZS Report
- 75% of consumers and 97% of doctors in India are ready to adopt AI-driven solutions in healthcare, according to a report by ZS.
- The report highlights the shift towards AI-powered healthcare for disease prediction, risk assessment, and diagnosis.
- Over 60% of Indians are open to virtual healthcare, and 63% of consumers are interested in using AI-powered health apps.
- Collaboration among stakeholders and investment in digital health can help India build a future-ready healthcare system by 2030.
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Medium
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AI Meal Planning: Personalized Nutrition for Healthier, Smarter Eating
- AI meal planning offers personalized nutrition for healthier, smarter eating.
- AI can analyze dietary habits and health metrics to create tailored meal plans.
- It helps in making healthier choices without the hassle of meal planning.
- AI meal planning is like having a personal nutritionist at your fingertips.
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Analyticsindiamag
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India Ready to Steal the Chip Spotlight Amid Trump’s 25% Tariff Roll Out
- The United States recently imposed a 25% tariff on semiconductors, causing upheaval in the global tech industry.
- India, as a rising player in semiconductors, could benefit from this decision, being relatively shielded due to its import duty policies.
- The tariffs imposed by the US affect semiconductor imports and are likely to increase costs for US consumers.
- India's focus on domestic semiconductor consumption and manufacturing facilities positions it favorably amidst the tariff scenario.
- The US actions also impact global semiconductor trade relationships, potentially straining ties with key allies like Taiwan and South Korea.
- The move towards regionalisation in semiconductor supply chains may benefit countries like India, making them attractive locations for investments.
- India is actively working on improving its appeal for semiconductor manufacturing investments, although entry barriers and scaling challenges persist.
- The US tariff could violate the Information Technology Agreement and face resistance from major semiconductor companies relying on Asian facilities.
- While the tariff aligns with US national security objectives, it presents risks such as supply chain disruptions and increased consumer costs.
- The long-term implications could lead to a shift from globalisation to regionalised production hubs in the semiconductor industry.
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Towards Data Science
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7 Powerful DBeaver Tips and Tricks to Improve Your SQL Workflow
- DBeaver has a powerful Command Palette feature, accessible through hotkeys or the search option.
- DBeaver allows setting up a different SQL formatter, such as pg_formatter, for customized formatting preferences.
- The Content Assist feature in DBeaver helps expand SELECT * queries into explicit column names with the CTRL + Space hotkey.
- DBeaver provides useful features like the Calc tab for analyzing query results and the Groupings tab for creating group-by queries.
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Towards Data Science
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How to Switch from Data Analyst to Data Scientist
- Data analysts focus on structured data for business decisions, while data scientists use predictive modeling and automated decision-making.
- Senior analysts can excel without needing deep ML or statistical knowledge, and not everyone may enjoy data science work.
- Key skills for transitioning include statistics, advanced SQL and Python, machine learning fundamentals, and working with large datasets.
- Self-study with consistency and the right resources is a cost-effective way to transition to data science.
- Bootcamps offer structured learning and accountability, but quality varies, so research is crucial before enrollment.
- A Master's degree can provide a deep dive into data science with networking opportunities, especially for career changers.
- Mentorship is valuable for guidance and career navigation during the transition process.
- Building a portfolio with self-motivated projects and leveraging current role opportunities can showcase skills to potential employers.
- Positioning yourself online and networking strategically can aid in securing the first Data Science role.
- Consistent progress is key in the transition to data science, and leveraging strengths from an analytics background can be advantageous.
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Medium
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Forget the new Siri: Here’s the advanced AI I use on my iPhone instead
- The launch of ChatGPT sparked a generative AI craze, igniting a tech revolution that has forced companies to rapidly innovate.
- Apple confirms that the highly-anticipated Siri upgrades, such as a more personalized Siri with awareness of your personal context, will take longer than expected to be delivered to the public.
- The company anticipates rolling out the enhanced Siri features in the coming year.
- Apple Intelligence aims to put Siri at the center of the Apple ecosystem as a context-aware personal assistant.
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Nycdatascience
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Predicting Home Prices with Machine Learning
- Accurately predicting home prices is crucial for buyers, sellers, and real estate professionals, with machine learning providing a powerful tool for enhanced accuracy.
- The Ames Housing dataset, containing 2,580 home sales records from Ames, Iowa, is widely used for predictive modeling with 82 detailed property attributes.
- Exploratory Data Analysis revealed patterns and distributions in the dataset, including numerical, categorical, and ordinal features.
- Features like OverallQual, GrLivArea, and TotalBsmtSF showed strong correlations with sale price, illustrating the impact of various property attributes on pricing.
- Categorical variables such as neighborhood, house style, and foundation type play crucial roles in determining home prices.
- Ridge Regression and XGBoost Regression models were evaluated, with XGBoost demonstrating superior predictive performance due to its ability to capture complex relationships.
- Feature engineering techniques like log transformation, one-hot encoding, and creating new features further enhanced model performance.
- XGBoost was deemed the most effective model, with opportunities for improvement through outlier detection, feature selection, and exploring alternative ensemble methods.
- Incorporating macroeconomic indicators and deploying the model as a web-based application are suggested future directions to enhance real-time price predictions.
- Machine learning in real estate analytics offers objective insights into home valuations, with ongoing advancements expected to improve accuracy and market transparency.
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Medium
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Knowledge Graphs in AI 2025: Revolutionizing Transparency, Healthcare & Fraud Detection
- Knowledge graphs are reshaping AI by enhancing transparency, boosting performance, and enabling real-time decisions.
- They contribute to a new era of AI where data is not only collected but understood, and AI systems can explain their decisions.
- There are seven powerful types of knowledge graphs revolutionizing AI in 2025.
- Knowledge graphs have the potential to transform various industries including healthcare and fraud detection.
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Towards Data Science
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Experiments Illustrated: Can $1 Change Behavior More Than $100?
- IntelyCare, a healthcare staffing company, conducted experiments to determine the effectiveness of different referral incentive programs.
- They compared a $100 referral bonus to a $1/hr referral program, finding surprising results.
- The $1/hr program led to an 81% increase in referrals and lower costs compared to the $100 offer.
- Social incentives and timing of rewards were found to significantly impact referral behavior.
- The study highlighted the importance of understanding human behavior patterns in designing effective incentive programs.
- Behavioral science concepts such as present bias and social incentives played a role in the success of the $1/hr program.
- The results emphasized the need for continuous testing and adaptation in referral programs to optimize outcomes.
- Referral programs' effectiveness can vary based on company size, familiarity, and external factors like a global pandemic.
- The study showcased the value of leveraging behavioral science and experimentation to drive business growth and cost efficiency.
- Continuous testing and adaptation are crucial for maximizing the impact of referral programs in varying business contexts.
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Towards Data Science
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How to Develop Complex DAX Expressions
- Developing complex DAX expressions involves understanding requirements, defining calculations, and considering different scenarios.
- Flexibility in DAX measures is crucial to cater to various scenarios, as clients may require different reports based on the same logic.
- Understanding filter context is essential in DAX, as it determines the impact of filters on expression results.
- Intermediary calculations and step-by-step approach aid in comprehending and building complex DAX measures effectively.
- Starting with a table or matrix visualization helps in visualizing intermediary results before finalizing complex DAX expressions.
- Variables and defined base measures play a significant role in developing complex DAX expressions for accurate results.
- Calculating linear extrapolations requires meticulous handling of arithmetic operations and understanding of data context in DAX.
- The development process for calculated columns in DAX follows similar steps with considerations for physical storage and context transition.
- Following a structured development process for complex DAX expressions can enhance efficiency and understanding of filter context.
- Maintaining simplicity and minimizing complexity while handling complex DAX expressions is a hallmark of a proficient developer.
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Towards Data Science
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Heatmaps for Time Series
- The article discusses creating heatmaps for time series data using Matplotlib in Python.
- The data used in the article is related to measles cases from University of Pittsburgh’s Project Tycho.
- The article demonstrates how to visualize measles incidence data over time using pcolormesh() function in Matplotlib.
- Different heatmap functions like imshow() in Matplotlib are compared for creating visualizations.
- The article highlights the importance of color selection in creating informative and visually appealing heatmaps.
- It explains how color distribution in the heatmap can affect the interpretation of data.
- The process of creating a custom colormap in Matplotlib to match a specific heatmap design is detailed.
- The article discusses handling missing data and normalizing values for heatmap visualization.
- Heatmaps are described as effective tools for analyzing trends, temporal patterns, and communicating complex data effectively.
- They are valuable for comparative analysis, temporal trends, pattern recognition, and facilitating clear communication of data.
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Analyticsindiamag
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Microsoft Ports TypeScript to Go with 10x Speed Boost
- Microsoft announced a project to enhance TypeScript performance by porting the compiler and language tools to Go.
- The project, titled 'Corsa', promises a 10x speed boost and a reduction in memory usage for developers.
- The TypeScript compiler and toolset have been ported to Go to overcome JavaScript's performance limits.
- Microsoft expects to preview a native implementation of the TypeScript compiler by mid-2025.
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Towards Data Science
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How to Make Your LLM More Accurate with RAG & Fine-Tuning
- Large Language Models (LLMs) like ChatGPT can be enhanced with RAG and fine-tuning for improved accuracy and customization.
- RAG enables LLMs to access external knowledge sources during inference without changing internal weights, allowing for up-to-date information retrieval.
- On the other hand, fine-tuning involves training LLMs with specific data to internalize domain-specific knowledge, enhancing task-specific performance.
- RAG is useful for dynamic data retrieval and reducing computational requirements, while fine-tuning tailors LLMs for specific industries or companies.
- Combining RAG and fine-tuning in RAFT offers deep expertise and real-time adaptability by enriching LLMs with domain knowledge and external information.
- Both methods have distinct advantages: RAG for dynamic knowledge integration, and fine-tuning for stable, task-specific optimization.
- RAG and fine-tuning can be used together to extend LLM capabilities and are valuable tools in AI applications, serving complementary purposes.
- RAG requires fewer resources initially, but more during inference, while fine-tuning is resource-intensive during training but efficient in operation.
- The choice between RAG and fine-tuning depends on the level of dynamism in the data and the need for specific task optimization.
- Hybrid approaches like RAFT combine RAG and fine-tuning to leverage both methods' advantages for comprehensive LLM enhancement.
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Analyticsindiamag
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OpenAI Unveils New APIs and Tools for Agent Development
- OpenAI has launched a set of APIs and tools to simplify the development of AI agents and to help developers and enterprises build reliable and useful autonomous systems.
- The newly introduced tools include the Responses API, built-in tools for web search, file search, and computer use, an Agents SDK, and observability tools for workflow execution.
- The Responses API allows developers to leverage OpenAI's built-in tools such as web search, file search, and computer use to handle complex tasks.
- OpenAI also introduced the Agents SDK, which simplifies complex agent interactions and offers features like monitoring, tracing, and built-in guardrails.
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Analyticsindiamag
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OpenAI Unveils New APIs and Tools for Developers to Build Their Own Manus
- OpenAI has launched a set of APIs and tools to simplify the development of AI agents and help build reliable and useful autonomous systems.
- The newly introduced tools include the Responses API, built-in tools for web search, file search, and computer use, an Agents SDK, and observability tools for workflow execution.
- The Responses API allows developers to leverage OpenAI's built-in tools, such as web search, file search, and computer use, to solve complex tasks using multiple tools and model turns.
- OpenAI also introduced the Agents SDK to simplify complex agent interactions and enable quick prototype and deployment of AI agents.
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