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title: Best Towards Data Science posts — August 2025 | daily.dev
description: The most upvoted Towards Data Science posts from August 2025, curated by the daily.dev community.
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og:description: The most upvoted Towards Data Science posts from August 2025, curated by the daily.dev community.
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# Best of Towards Data Science — August 2025

1. 1  
[](https://daily.dev/posts/langgraph-101-let-s-build-a-deep-research-agent-iobyemaeb "LangGraph 101: Let’s Build A Deep Research Agent")  
Article  
![Avatar of tds](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds)Towards Data Science · 1y  
LangGraph 101: Let’s Build A Deep Research Agent  
A comprehensive tutorial on building AI research agents using LangGraph, Google's open-source framework. Covers core concepts including graph-based workflow modeling with nodes and edges, state management for agent memory, structured outputs for reliable LLM responses, tool calling for web searches, conditional routing for decision-making, and parallel processing for concurrent operations. Uses Google's Deep Research Agent implementation as a practical example, demonstrating how to create agents that can autonomously search the web, evaluate results, and generate comprehensive reports with citations.  
29
2. 2  
[](https://daily.dev/posts/langgraph-scipy-building-an-ai-that-reads-documentation-and-makes-decisions-bhrpmcfzw "LangGraph + SciPy: Building an AI That Reads Documentation and Makes Decisions")  
Article  
![Avatar of tds](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds)Towards Data Science · 1y  
LangGraph + SciPy: Building an AI That Reads Documentation and Makes Decisions  
A comprehensive tutorial on building an AI agent that helps users choose appropriate statistical tests by combining LangGraph for multi-step decision making with RAG (Retrieval-Augmented Generation) using SciPy documentation. The agent classifies user questions, searches embedded documentation when needed, provides recommendations, and generates sample Python code. The implementation includes ChromaDB for vector storage, OpenAI GPT-4 for language processing, and a Streamlit frontend for user interaction.  
19  
1
3. 3  
[](https://daily.dev/posts/context-engineering-a-comprehensive-hands-on-tutorial-with-dspy-8zvxm4pgu "Context Engineering — A Comprehensive Hands-On Tutorial with DSPy")  
Article  
![Avatar of tds](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds)Towards Data Science · 1y  
Context Engineering — A Comprehensive Hands-On Tutorial with DSPy  
Context Engineering is a systematic approach to building production-ready LLM applications by breaking complex problems into modular subproblems handled by specialized agents. The tutorial demonstrates using DSPy framework to implement structured outputs, multi-step workflows, tool calling, and RAG systems. Key concepts include sequential processing, iterative refinement, conditional branching, and advanced techniques like query rewriting, HYDE, and multi-hop search. Production considerations cover evaluation design, monitoring, structured outputs, and failure handling with tools like MLflow and Langfuse for observability.  
12
4. 4  
[](https://daily.dev/posts/building-a-modern-dashboard-with-python-and-tkinter-19bjwg99e "Building a Modern Dashboard with Python and Tkinter")  
Article  
![Avatar of tds](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds)Towards Data Science · 1y  
Building a Modern Dashboard with Python and Tkinter  
Tkinter remains a powerful and lightweight option for creating modern desktop GUI applications and data dashboards in Python. The article demonstrates building a comprehensive car sales dashboard using Tkinter with ttkbootstrap for modern styling, matplotlib for interactive charts, and pandas for data processing. Key features include dynamic filtering, clickable visualizations, tabbed interfaces, and real-time data updates. The tutorial covers fundamental Tkinter concepts like widgets, geometry managers, and event loops, then progresses to a complex multi-tab dashboard with correlation heatmaps, scatter plots, and data tables.  
10

[See all Towards Data Science archives](/sources/tds/best-of)

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