LLM

Can LLMs Replace Data Scientists? DeepAnalyze Lets You Say Goodbye to Manual Data Analysis

Honghao Wang

— 3 min read
Can LLMs Replace Data Scientists? DeepAnalyze Lets You Say Goodbye to Manual Data Analysis

DeepAnalyze — Your Personal AI “Data Scientist”

Tired of Wrestling with Complex Data?

Do massive, messy datasets slow you down?

Do you wish insights could be extracted automatically?

A joint research team from Renmin University and Tsinghua University has launched DeepAnalyze — your autonomous data scientist.

With a single instruction, this Agentic LLM can:

  • Automate data preparation, analysis, modeling, visualization, and insightful reporting
  • Perform deep research on unstructured, semi-structured, and structured data, producing clear research reports
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Key Differentiators:

  • Fully autonomous — no manual workflows required
  • All-in-one execution — handles multi-step data science tasks like a human expert
  • Fully open-source: paper, code, models, and datasets available
  • Already earned 1.1K+ GitHub stars

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Why DeepAnalyze Is Different

Learning in Real Environments

Data science tasks mimic human intelligence benchmarks (e.g., Kaggle).

Traditional data agents:

  • Depend on manually designed workflows for specific tasks
  • Deliver strong single-task performance but lack true autonomy

The challenge:

“How can we enable an LLM to independently complete complex data science tasks?”

DeepAnalyze’s answer:

  • Curriculum-based training
  • Data-grounded trajectory synthesis
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Curriculum-Based Agentic Training

LLMs struggle with complex data tasks early on, often receiving Sparse Rewards (minimal positive feedback), which stalls learning.

DeepAnalyze’s Approach: Train like humans — from simple to complex tasks.

Two Training Stages:

  • Single-Skill Fine-Tuning
  • Focus: Code generation, structured data understanding, logical reasoning
  • Multi-Skill Agentic Training
  • Real-world environments
  • Combining abilities to autonomously solve complex problems

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Data-Grounded Trajectory Synthesis

The Problem

Lack of complete reasoning paths for multi-step data problems leads to:

  • Blind searches
  • Inefficient trial-and-error
  • No intermediate guidance

The Solution

DeepAnalyze automatically generates 500,000 reasoning and interaction records.

Benefits:

  • Guides LLMs in large search spaces
  • Provides correct strategies and paths

Two Components:

  • Reasoning Trajectory Synthesis
  • Based on TableQA, structured knowledge tasks, and data science code generation with full reasoning chains
  • Interactive Trajectory Synthesis
  • Multi-agent systems synthesize interaction traces from datasets like Spider and BIRD
  • Comparable to real-world scenarios

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Reporting & Research Capabilities

DeepAnalyze excels in generating:

  • Analyst-level research reports
  • Deep content analysis with superior structure and depth compared to closed-source models

Example Analysis Output:

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Analysis Report:

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Team Profile — RUC-DataLab

RUC-DataLab

  • Part of the School of Information, Renmin University of China
  • Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education
  • Led by Professor Fan Ju
  • Focus: Data Systems + Artificial Intelligence (Data+AI)

Research Areas:

  • AI4DB — Improving database performance and autonomy with AI
  • DB4AI — Using data management tech to optimize model training and inference
  • AI4DS — Enhancing data science systems with reasoning LLMs, multimodal understanding, and intelligent agents

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Resources

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For creators and analysts wanting to monetize AI-driven insights, platforms like AiToEarn are powerful complements to DeepAnalyze.

Features:

  • Open-source global content monetization ecosystem
  • Simultaneous Publishing on Douyin, Kwai, WeChat, Bilibili, Rednote (Xiaohongshu), Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, and X (Twitter)
  • Integrated AI generation, cross-platform publishing, analytics, and AI Model Ranking

Use Case:

  • Generate reports and insights with DeepAnalyze
  • Distribute them instantly across platforms with AiToEarn
  • Track impact and revenue via built-in analytics

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In summary: DeepAnalyze represents a significant leap toward truly autonomous, expert-level AI data science — and with ecosystems like AiToEarn, those insights can be amplified and monetized globally.

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Do you want me to also rewrite this into a concise one-page pitch deck format so it’s presentation-ready? That would make it ideal for investors or collaborators.

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