Alias-DataScience is an autonomous, ready-to-use, intelligent assistant for real-world data science workflows. It transforms high-level analytical questions into executable plans, which can seamlessly handle data acquisition, cleaning, modeling, visualization, and narrative reporting, with minimal human intervention.
To handle massive data files commonly found in enterprise data lakes, Alias-DataScience combines parallelized grep operations with Retrieval-Augmented Generation (RAG) to build a low-latency, high-throughput file filtering pipeline. This preprocessing step enables accurate identification of relevant files, significantly expanding our scope and applicability.
Rather than relying on generic instructions, Alias-DataScience employs three specialized prompt templates, each fine-tuned for a dominant data science workflow:
- Exploratory Data Analysis (EDA): Surfaces trends, anomalies, and relationships to answer "what's happening?" and "why?"
- Predictive Modeling: Automates feature engineering, model selection, and optimization.
- Exact Data Computation: Delivers precise, auditable answers to quantitative queries (e.g., "What was the YoY revenue growth in Q3?").
An intelligent prompt selector routes tasks to the best template based on user intent.
Alias-DataScience parses irregular spreadsheets (merged cells, embedded notes, multi-level headers) and converts them into structured tables. For large files, it outputs a semantic-preserving JSON representation, enabling reliable analysis of human-crafted inputs.
- Image Understanding: Interprets charts, diagrams, and general images to extract numerical data, trends, and domain-specific entities
- Visual QA: Answers natural-language questions about visual elements (e.g., "What was the peak value in Q3?").
For EDA tasks, Alias-DataScience generates an interactive HTML report featuring:
- Actionable insights backed by statistics and visuals,
- Executable code snippets for transparency and reuse.
This bridges the gap between data scientists and stakeholders like business users or auditors.
Alias-DataScience achieves state-of-the-art (SOTA) across major data science agent benchmarks.
Realistic tasks from ModelOff & Kaggle; includes multimodal inputs, multi-source data, and large-scale modeling.
| Task Category | Framework | Model | Score |
|---|---|---|---|
| Data Analysis | Alias-DataScience | Qwen3-max-Preview | 55.58% 🏆 |
| AutoGen | GPT-4 | 30.69% | |
| AutoGen | GPT-4o | 34.12% | |
| CodeInterpreter | GPT-4 | 26.39% | |
| CodeInterpreter | GPT-4o | 23.82% | |
| Data Modeling | Alias-DataScience | Qwen3-max-Preview | 49.70% 🏆 |
| AutoGen | GPT-4 | 45.52% | |
| AutoGen | GPT-4o | 34.74% | |
| CodeInterpreter | GPT-4 | 26.14% | |
| CodeInterpreter | GPT-4o | 16.90% |
Open-ended comprehensive analytical tasks.
| Framework | Model | Score |
|---|---|---|
| Alias-DataScience | Qwen3-max-Preview | 43.29% 🏆 |
| AgentPoirot | Qwen3-max-Preview | 39.30% |
End-to-end data analysis from real-world CSVs.
| Framework | Model | Score |
|---|---|---|
| Alias-DataScience | Qwen3-max-Preview | 95.20% 🏆 |
| AutoGen | GPT-4 | 71.49% |
| Data Interpreter | GPT-4 | 73.55% |
| Data Interpreter | GPT-4o | 94.93% |
Some tables include data from published sources, used with gratitude to the original authors and cited in good faith. For accuracy, please refer to the original publications.
