Haden Pelletier writes that data scientists can increase productivity by mastering four specific uses for Claude: Deep Research, HTML project briefs, Slide deck design via Claude Design, and README documentation using Claude Code. By leveraging these specialized "modes," professionals can automate repetitive tasks like comparative research or stakeholder reporting to focus on higher-level problem solving.
- Use the "Research" mode in Claude for complex queries requiring multi-step web searches and citations.
- Claude Design is a separate tool optimized specifically for visual structure, preventing text overlap found in standard chat modes.
- For HTML project briefs or slides, manual editing via the "Edit" button in Claude Design can fix minor layout issues more efficiently than re-prompting.
- The accuracy of README generation through Claude Code depends heavily on its ability to read and interpret existing codebase files like .py scripts and notebooks.
- Use the "Research" mode in Claude for complex queries requiring multi-step web searches and citations.
- Claude Design is a separate tool optimized specifically for visual structure, preventing text overlap found in standard chat modes.
- For HTML project briefs or slides, manual editing via the "Edit" button in Claude Design can fix minor layout issues more efficiently than re-prompting.
- The accuracy of README generation through Claude Code depends heavily on its ability to read and interpret existing codebase files like .py scripts and notebooks.
* Method chaining improves readability and reduces noise by replacing intermediate variables with a single sequence of transformations.
* The pipe() pattern allows you to integrate complex, custom functions into a chain while keeping code testable and self-documenting.
* Use the validate parameter in merge() to prevent unexpected row inflation from many-to-many joins and use indicator=True for easier debugging.
* Optimize groupby operations by using transform() to add group statistics without extra merges and observed=True to avoid unnecessary computations on empty categories.
* Replace slow apply() calls with vectorized NumPy functions like np.where() or np.select() for much faster conditional logic.
* Avoid performance pitfalls such as iterrows(), unoptimized object dtypes, and chained assignment by using built-in vectorized methods and .loc.
* The pipe() pattern allows you to integrate complex, custom functions into a chain while keeping code testable and self-documenting.
* Use the validate parameter in merge() to prevent unexpected row inflation from many-to-many joins and use indicator=True for easier debugging.
* Optimize groupby operations by using transform() to add group statistics without extra merges and observed=True to avoid unnecessary computations on empty categories.
* Replace slow apply() calls with vectorized NumPy functions like np.where() or np.select() for much faster conditional logic.
* Avoid performance pitfalls such as iterrows(), unoptimized object dtypes, and chained assignment by using built-in vectorized methods and .loc.
How to use AI skills—reusable packages of instructions and files—to automate repetitive data science workflows. By moving beyond simple prompting into structured skills, users can maintain shorter context windows while ensuring consistent, high-quality outputs for complex tasks like data visualization or metric investigation.
* A skill consists of a SKILL.md file with metadata and detailed instructions to guide an AI through specific recurring processes.
* Using skills helps keep the main LLM context lightweight by only loading detailed resources when they are relevant to the task.
* The author demonstrates this by automating a weekly visualization habit, reducing a one-hour manual process to less than ten minutes.
* Building effective skills requires iterative testing, incorporating personal domain knowledge, and researching external best practices.
* Combining skills with Model Context Protocol (MCP) allows AI to both follow specific procedural playbooks and access external data tools seamlessly.
* A skill consists of a SKILL.md file with metadata and detailed instructions to guide an AI through specific recurring processes.
* Using skills helps keep the main LLM context lightweight by only loading detailed resources when they are relevant to the task.
* The author demonstrates this by automating a weekly visualization habit, reducing a one-hour manual process to less than ten minutes.
* Building effective skills requires iterative testing, incorporating personal domain knowledge, and researching external best practices.
* Combining skills with Model Context Protocol (MCP) allows AI to both follow specific procedural playbooks and access external data tools seamlessly.
Write Pandas Like a Pro With Method Chaining Pipelines
Master method chaining, assign(), and pipe() to write cleaner, testable, production-ready Pandas code
Master method chaining, assign(), and pipe() to write cleaner, testable, production-ready Pandas code
This article explores five Python scripts designed to streamline and automate the process of feature selection in machine learning projects. Feature selection is crucial for improving model performance, reducing complexity, and identifying the most impactful variables.
The scripts cover techniques like filtering constant features, eliminating redundant features through correlation analysis, identifying significant features using statistical tests, ranking features with model-based importance scores, and optimizing feature subsets with recursive elimination. Each script is practical, minimal, and provides detailed reports to aid in understanding the selection process.
These tools are valuable for data scientists looking to systematically evaluate feature importance and build more efficient and accurate models.
The scripts cover techniques like filtering constant features, eliminating redundant features through correlation analysis, identifying significant features using statistical tests, ranking features with model-based importance scores, and optimizing feature subsets with recursive elimination. Each script is practical, minimal, and provides detailed reports to aid in understanding the selection process.
These tools are valuable for data scientists looking to systematically evaluate feature importance and build more efficient and accurate models.
This article provides a comprehensive overview of advanced causal inference methods, moving beyond traditional statistical approaches. It emphasizes the importance of understanding causal relationships rather than just correlations for effective decision-making. The playbook covers techniques like instrumental variables, regression discontinuity, difference-in-differences, and causal discovery algorithms.
It discusses the assumptions required for each method and how to validate them. The author stresses the need for careful consideration of confounding variables and potential biases when attempting to establish causality. Ultimately, the article aims to equip data scientists with the tools and knowledge to draw more meaningful and actionable insights from data.
It discusses the assumptions required for each method and how to validate them. The author stresses the need for careful consideration of confounding variables and potential biases when attempting to establish causality. Ultimately, the article aims to equip data scientists with the tools and knowledge to draw more meaningful and actionable insights from data.
CUDA 13.2 brings full support for CUDA Tile on Ampere, Ada, and Blackwell architectures, alongside enhancements to cuTile Python including recursive functions, closures, and custom reductions. Core updates include improved memory transfer APIs, reduced LMEM footprint in Windows, and a shift to MCDM for better compatibility. Math libraries gain experimental Grouped GEMM with MXFP8 and FP64-emulated cuSOLVERD. Developer tools see updates to Nsight Python, Nsight Compute, and Nsight Systems, alongside a modern C++ runtime in CCCL 3.2. CuPy also gains support for CUDA 13 and stream sharing.
The New Stack encourages its readers to contribute to Towards Data Science, a leading platform for data science and AI. Recognizing the increasing convergence of cloud infrastructure, DevOps, and AI engineering, the article invites practitioners to share their experiences with building and deploying AI systems. Successful TDS submissions are technically detailed, timely, and specific. Authors can also benefit from editorial support, promotion, and potential payment opportunities, while building their reputation within the AI community.
This tutorial explores how to use LLM embeddings as features in time series forecasting models. It covers generating embeddings from time series descriptions, preparing data, and evaluating the performance of models with and without LLM embeddings.
This course takes you from Python fundamentals to AI Agent development, covering core Python, NumPy, Pandas, SQL, Flask, FastAPI, LLMs, and open-source models via HuggingFace.
