Data Science Portfolio Projects
A strong portfolio demonstrates end-to-end capability. Here are 10 project ideas with architecture, datasets, and presentation guidance.
Portfolio Principles
Architecture Diagram
1. End-to-end projects > toy notebooks
2. Show the full stack: data β model β deployment β monitoring
3. Document your thinking, not just your code
4. Include business context and impact
5. Make it reproducible
Project 1: Real-Time Fraud Detection
# Architecture
"""
Data Sources: Transaction API, User profiles, Merchant data
Processing: Kafka β Spark Streaming β Feature Store
Model: XGBoost + Isolation Forest ensemble
Serving: FastAPI with <100ms latency
Monitoring: Drift detection, alert pipeline
"""
# Key Components
fraud_architecture = {
"data_ingestion": ["Kafka", "Spark Streaming"],
"feature_store": ["Feast", "Redis"],
"model": ["XGBoost", "Isolation Forest", "Autoencoder"],
"serving": ["FastAPI", "Docker", "Kubernetes"],
"monitoring": ["Prometheus", "Grafana", "Evidently AI"]
}
# Dataset options
datasets = {
"kaggle_fraud": "https://www.kaggle.com/mlg-ulb/creditcardfraud",
"synthetic": "Generate with SDV (Synthetic Data Vault)",
"private": "Partner with fintech company"
}
# Evaluation metrics
metrics = {
"primary": "AUPRC (Area Under Precision-Recall Curve)",
"secondary": ["F1-score at 1% FPR", "Latency P99", "False positive rate"],
"business": "Dollar amount of fraud prevented"
}
Project 2: Recommendation System
# Architecture
"""
Data: User interactions, item metadata, context
Approach: Two-tower model + collaborative filtering
Features: Real-time user behavior + batch item features
Evaluation: Offline (NDCG@10) + Online (A/B test)
"""
import torch
import torch.nn as nn
class TwoTowerRecommender(nn.Module):
def __init__(self, num_users, num_items, embed_dim=64):
super().__init__()
self.user_tower = nn.Sequential(
nn.Embedding(num_users, embed_dim),
nn.Linear(embed_dim, 128),
nn.ReLU(),
nn.Linear(128, 64)
)
self.item_tower = nn.Sequential(
nn.Embedding(num_items, embed_dim),
nn.Linear(embed_dim, 128),
nn.ReLU(),
nn.Linear(128, 64)
)
def forward(self, user_ids, item_ids):
user_emb = self.user_tower(user_ids)
item_emb = self.item_tower(item_ids)
return torch.cosine_similarity(user_emb, item_emb)
# Presentation tips
portfolio_tips = {
"show": [
"Architecture diagram",
"Feature importance analysis",
"A/B test results",
"Cold-start handling strategy",
"Business impact estimate"
],
"highlight": "End-to-end: from data collection to production deployment"
}
Project 3: NLP Text Classification Pipeline
# Multi-label text classification for support tickets
"""
Architecture:
1. Text preprocessing (cleaning, normalization)
2. Fine-tuned BERT for classification
3. Active learning loop for labeling
4. Deployment with confidence thresholds
"""
from transformers import AutoModelForSequenceClassification
import torch
class TicketClassifier:
def __init__(self, num_labels=15):
self.model = AutoModelForSequenceClassification.from_pretrained(
"bert-base-uncased", num_labels=num_labels
)
self.label_names = [
"billing", "technical_issue", "account_access",
"feature_request", "bug_report", "refund",
"shipping", "returns", "password_reset",
"cancellation", "upgrade", "downgrade",
"data_export", "compliance", "other"
]
def classify_with_confidence(self, text, threshold=0.7):
"""Classify with confidence-based routing"""
# Route high-confidence to automated system
# Route low-confidence to human agents
pass
# Dataset options
nlp_datasets = {
"customer_support": "Kaggle Customer Support on Twitter",
"ag_news": "AG News Classification",
"custom": "Scrape and label your own data"
}
Project 4: Computer Vision Quality Inspection
# Manufacturing quality inspection system
"""
Pipeline:
1. Image acquisition (cameras, IoT sensors)
2. Object detection for defect localization
3. Classification for defect type
4. Integration with manufacturing MES
"""
class DefectDetector:
def __init__(self):
self.detector = self._load_yolo()
self.classifier = self._load_classifier()
def inspect(self, image):
# Detect defects
detections = self.detector(image)
results = []
for det in detections:
# Classify defect type
crop = image[det['bbox']]
defect_type = self.classifier(crop)
results.append({
'bbox': det['bbox'],
'type': defect_type,
'confidence': det['confidence'],
'severity': self._assess_severity(defect_type, det['confidence'])
})
return results
# Business impact
impact_metrics = {
"defect_detection_rate": ">99% (vs 95% human baseline)",
"inspection_speed": "<100ms per image",
"cost_savings": "$500K/year in quality costs",
"false_positive_rate": "<1%"
}
Project 5: Time Series Forecasting Platform
# Demand forecasting for retail
"""
Components:
1. Data pipeline (sales, promotions, weather, holidays)
2. Multiple models (Prophet, N-BEATS, TFT)
3. Ensemble with stacking
4. Automated retraining pipeline
5. Dashboard for merchandisers
"""
from prophet import Prophet
import pandas as pd
class DemandForecaster:
def __init__(self):
self.models = {
'prophet': Prophet(),
'lightgbm': self._create_lgbm(),
'nbeats': self._create_nbeats()
}
def train(self, sales_df):
for name, model in self.models.items():
if name == 'prophet':
prophet_df = sales_df[['date', 'sales']].rename(
columns={'date': 'ds', 'sales': 'y'}
)
model.fit(prophet_df)
else:
model.fit(sales_df)
def predict(self, future_dates):
predictions = {}
for name, model in self.models.items():
predictions[name] = model.predict(future_dates)
# Ensemble
return pd.DataFrame(predictions).mean(axis=1)
# Key features to engineer
feature_ideas = [
"Day of week, month, quarter",
"Holiday indicators (local and national)",
"Promotional events",
"Weather data (temperature, precipitation)",
"Competitor promotions",
"Social media sentiment",
"Inventory levels"
]
Project 6: A/B Testing Framework
# Build your own experimentation platform
"""
Components:
1. Feature flag system
2. Statistical engine (frequentist + Bayesian)
3. Sample ratio mismatch detection
4. Sequential testing
5. Multi-metric analysis
6. Guardrail monitoring
"""
class ExperimentationPlatform:
def __init__(self):
self.experiments = {}
self.flags = {}
def create_experiment(self, name, variants, traffic_split):
self.experiments[name] = {
'variants': variants,
'traffic': traffic_split,
'start_date': datetime.now(),
'status': 'running'
}
def analyze(self, experiment_name, control_data, treatment_data):
# Implement proper statistical analysis
pass
def check_srm(self, control_count, treatment_count, expected_ratio):
# Sample ratio mismatch check
pass
# What to show
portfolio_items = [
"Statistical methodology document",
"Power analysis calculator",
"Real experiment results (anonymized)",
"Dashboard for experiment monitoring",
"Write-up of business impact"
]
Project 7: MLOps Pipeline
# Full MLOps pipeline with monitoring
"""
Stack:
- Training: Kubeflow Pipelines
- Registry: MLflow
- Serving: Seldon Core
- Monitoring: Evidently AI
- Orchestration: Airflow
"""
# Pipeline components
mlops_components = {
"data_validation": "Great Expectations",
"feature_engineering": "Feast Feature Store",
"training": "Kubeflow with GPU support",
"evaluation": "Custom metrics + bias detection",
"deployment": "Canary release with auto-rollback",
"monitoring": "Data drift + model performance",
"retraining": "Triggered by drift detection"
}
Project 8: Knowledge Graph
# Build a domain-specific knowledge graph
"""
Use Cases:
- Drug interaction discovery
- Fraud ring detection
- Recommendation enrichment
- Document understanding
"""
import networkx as nx
from rdflib import Graph
class KnowledgeGraphBuilder:
def __init__(self):
self.graph = nx.DiGraph()
def extract_entities(self, text):
# Use spaCy or custom NER
pass
def extract_relations(self, text, entities):
# Relation extraction
pass
def build_graph(self, documents):
for doc in documents:
entities = self.extract_entities(doc)
relations = self.extract_relations(doc, entities)
for entity in entities:
self.graph.add_node(entity['text'], type=entity['type'])
for rel in relations:
self.graph.add_edge(rel['subject'], rel['object'],
relation=rel['predicate'])
def query(self, start_entity, relation_type, max_hops=2):
# Graph traversal query
pass
Project 9: Automated ML Pipeline
# AutoML with custom feature engineering
"""
Components:
1. Automated feature engineering (Featuretools)
2. Model selection and hyperparameter tuning
3. Ensemble generation
4. Interpretability (SHAP)
5. Deployment
"""
import featuretools as ft
import shap
class AutoMLPipeline:
def __init__(self):
self.feature_engine = ft.FeaturePrimitives()
self.models = {}
def auto_feature(self, entityset, target_dataframe_name):
# Automated feature engineering
feature_matrix, features = ft.dfs(
entityset=entityset,
target_dataframe_name=target_dataframe_name,
max_depth=2
)
return feature_matrix
def explain_prediction(self, model, X, instance_idx):
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X.iloc[instance_idx])
return shap_values
Project 10: End-to-End ML System Case Study
# Document a complete ML system from business problem to impact
"""
Structure:
1. Business Context and Problem Definition
2. Data Exploration and Insights
3. Solution Design
4. Implementation Details
5. Results and Impact
6. Lessons Learned
"""
case_study_template = {
"business_problem": "Reduce customer churn by 15%",
"data_sources": ["CRM", "Product usage", "Support tickets", "Billing"],
"approach": "Churn prediction with intervention recommendations",
"models": ["Logistic Regression (baseline)", "XGBoost", "Neural Network"],
"features": [
"Usage trends (7d, 30d, 90d)",
"Support interaction frequency",
"Payment history",
"Feature adoption rate",
"NPS scores"
],
"results": {
"auc_roc": 0.89,
"precision_at_10pct": 0.72,
"estimated_impact": "$2.3M annual retention improvement"
}
}
Presentation Tips
# Portfolio presentation checklist
checklist = {
"github_repo": [
"Clean README with project overview",
"Requirements.txt or pyproject.toml",
"Jupyter notebook with analysis",
"Production code in src/ directory",
"Dockerfile for reproducibility",
"CI/CD pipeline (GitHub Actions)"
],
"blog_post": [
"Problem statement",
"Approach and reasoning",
"Challenges and solutions",
"Results and business impact",
"Lessons learned"
],
"demo": [
"Working web application or API",
"Interactive dashboard",
"Video walkthrough"
]
}
Key Takeaways
- Quality over quantity β 3 polished projects beat 10 half-done notebooks
- Tell a story β Connect technical work to business impact
- Show iteration β Document failures and what you learned
- Make it reproducible β Clean code, clear instructions, Docker
- Get feedback β Share with peers before going public