---

name: ds-data-collection compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Implements data gathering strategies including APIs, web scraping sensor data collection, and database queries for building machine learning datasets"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-data-ingestion, ds-data-quality, ds-data-versioning role: implementation scope: implementation triggers: data collection, web scraping, API integration, data gathering, data acquisition ETL, how do i collect data archetypes:
  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational version: "1.0.0"

---

Data Collection

Comprehensive guide to data collection in machine learning and data science workflows.

When to Use This Skill

• Solving real-world data collection & ingestion problems
• Building machine learning pipelines with data collection
• Implementing best practices for data collection
• Optimizing model performance using data collection techniques
• Learning industry-standard approaches to data collection

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require data collection rigor
• When domain expertise in specific problem requires different approach
• If your problem doesn't require the complexity this skill provides

Purpose and Key Concepts

Data Collection is a critical component of the machine learning workflow. This skill covers:

1. Theoretical foundations — Mathematical principles and statistical concepts
2. Practical implementation — Working code examples and patterns
3. Common pitfalls — Mistakes to avoid and how to recover from them
4. Best practices — Industry-standard approaches and optimization techniques

Core Workflow

1. Understand the problem — Clearly define what you're solving for
2. Select approach — Choose the right technique for your data and constraints
3. Implement solution — Write clean, tested code following best practices
4. Validate results — Verify your implementation with tests and validation
5. Optimize performance — Improve efficiency and accuracy incrementally

Implementation Patterns

Pattern 1: Basic Data Collection

import pandas as pd
import requests
import logging
from typing import Dict, Any

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def fetch_api_data(url: str, params: Dict[str, Any] = None) -> pd.DataFrame:
    """Fetch data from a REST API and convert to DataFrame."""
    try:
        response = requests.get(url, params=params, timeout=10)
        response.raise_for_status()
        data = response.json()
        
        # Handle different JSON structures
        if isinstance(data, list):
            df = pd.DataFrame(data)
        elif isinstance(data, dict) and 'results' in data:
            df = pd.DataFrame(data['results'])
        else:
            df = pd.DataFrame([data])
            
        logger.info(f"Successfully fetched {len(df)} records from {url}")
        return df
        
    except requests.exceptions.RequestException as e:
        logger.error(f"API request failed: {e}")
        raise

Pattern 2: Production-Ready Data Collection

import logging
import time
import requests
import pandas as pd
from typing import Any, Dict, Optional
from tenacity import retry, stop_after_attempt, wait_exponential

logger = logging.getLogger(__name__)

class ProductionDataCollector:
    """Production-grade data collection with retries, rate limiting, and validation."""
    
    def __init__(self, base_url: str, api_key: Optional[str] = None, 
                 max_retries: int = 3, timeout: int = 15):
        self.base_url = base_url
        self.api_key = api_key
        self.max_retries = max_retries
        self.timeout = timeout
        self.session = requests.Session()
        if api_key:
            self.session.headers.update({"Authorization": f"Bearer {api_key}"})
        
    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=10))
    def _fetch_with_retry(self, endpoint: str, params: Dict[str, Any] = None) -> Dict[str, Any]:
        url = f"{self.base_url}/{endpoint}"
        response = self.session.get(url, params=params, timeout=self.timeout)
        response.raise_for_status()
        return response.json()
        
    def execute(self, endpoint: str, params: Dict[str, Any] = None) -> Dict[str, Any]:
        """Execute data collection with full error handling and logging."""
        try:
            raw_data = self._fetch_with_retry(endpoint, params)
            df = pd.DataFrame(raw_data if isinstance(raw_data, list) else [raw_data])
            
            # Basic schema validation
            required_cols = ['id', 'timestamp', 'value']
            missing_cols = [c for c in required_cols if c not in df.columns]
            if missing_cols:
                raise ValueError(f"Missing required columns: {missing_cols}")
                
            df['timestamp'] = pd.to_datetime(df['timestamp'])
            df = df.dropna(subset=['value'])
            
            return {
                'status': 'success'
                'records_collected': len(df)
                'data': df
                'metadata': {'source': endpoint, 'columns': list(df.columns)}
            }
        except Exception as e:
            logger.error(f"Collection failed for {endpoint}: {e}")
            return {'status': 'failed', 'error': str(e), 'data': pd.DataFrame()}

Best Practices

• ✅ Always validate your implementation on test data
• ✅ Document your assumptions and methodology
• ✅ Use version control for reproducibility
• ✅ Monitor performance metrics in production
• ✅ Periodically review and update your approach
• ✅ Test with edge cases and outliers
• ✅ Log all significant operations for debugging

Common Pitfalls

| Pitfall | Problem | Solution | |

---

---

Constraints

MUST DO

• Validate all data preprocessing steps are fit-only on training data, never on validation or test sets
• Implement reproducible pipelines with fixed random seeds and deterministic operations where possible
• Report model performance with confidence intervals via bootstrapping or cross-validation across multiple runs
• Log all experiments with parameters, metrics, and artifacts using MLflow or equivalent tracking system

MUST NOT DO

• Do not evaluate a model on the same data used for training — always hold out a proper test set
• Avoid overfitting to the validation set by limiting hyperparameter search iterations
• Never use features that can only be computed at inference time (look-ahead bias)
• Do not report single-run accuracy without statistical significance testing or error bars

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Data Collection — Wikipedia
• NIST Guide to Data Quality
• Survey Research Methods (University of California)
• Web Scraping Best Practices (Scrapy docs)
• Data Collection Ethics — ACM Code of Ethics