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Azure Data Scientist

Design, train, and deploy machine learning models on Azure using Python, Azure Machine Learning, and MLOps best practices.

⏱️ 3-5 months
📊 4 Phases
🎓 DP-100 Certification
💼 Mid-Level
🎯 Mid-Level Role

What Does an Azure Data Scientist Do?

Azure Data Scientists design, build, and deploy machine learning solutions on the Microsoft Azure platform. You'll work with Python and Azure Machine Learning to prepare data, train models, optimize hyperparameters, deploy ML endpoints, and implement responsible AI practices. You'll also build automated ML pipelines and monitor model performance in production.

Is This Roadmap For You?

📜 Recommended Certification Path

AI-900

Azure AI Fundamentals

Prerequisite (Optional)
→

DP-100

Azure Data Scientist

After Phase 4

📋 DP-100 Exam Syllabus Overview

The official Microsoft DP-100 exam tests your knowledge across four key skill areas:

35-40%
Manage Azure Machine Learning workspace and data
  • Manage Azure ML workspaces
  • Manage data for ML experiments
  • Manage compute resources
  • Configure datastores and datasets
20-25%
Run experiments and train models
  • Create and run ML experiments
  • Train models using Azure ML
  • Tune hyperparameters
  • Use AutoML for model training
25-30%
Deploy and operationalize machine learning solutions
  • Deploy models to managed endpoints
  • Create and manage ML pipelines
  • Monitor deployed models
  • Implement batch inference
5-10%
Implement responsible AI
  • Evaluate model fairness
  • Interpret and explain models
  • Describe considerations for privacy and security

🚀 Start Here

If you're new to Azure Data Science:

→Begin with Phase 1: Master Data Science Fundamentals (expand below)

→Ensure you have Python programming experience

→Review statistics and basic ML concepts

→Focus on one phase at a time — finish it completely before moving forward

Already have ML experience?

→Jump to the phase that matches your current skill level

1
Master Data Science Fundamentals
3-4 weeks
2-3 hrs/day
▼
✅ Core Skills = Must complete to move forward | ◻ Optional = Nice-to-have if time permits
CORE
🐍 Python

Python fundamentals, data structures, functions, object-oriented programming

CORE
📊 pandas & numpy

Data manipulation, DataFrames, arrays, data cleaning, transformations

CORE
🤖 scikit-learn

ML algorithms, model training, evaluation, preprocessing, pipelines

CORE
📈 ML Concepts

Supervised/unsupervised learning, regression, classification, overfitting

OPTIONAL
📉 matplotlib & seaborn

Data visualization, plotting, charts, exploratory data analysis

🎯 Learning Actions

📚 Learn
Microsoft Learn Python and ML paths
🛠️ Practice
Build ML models with scikit-learn
✅ Prove
Create a classification or regression model
2
Build with Azure ML
4-5 weeks
2-3 hrs/day
▼
✅ Core Skills = Must complete to move forward | ◻ Optional = Nice-to-have if time permits
CORE
🏢 Azure ML Workspace

Create and configure workspaces, manage resources, security, access control

CORE
💾 Datasets & Datastores

Register datasets, connect to data sources, data versioning, access patterns

CORE
⚙️ Compute Resources

Compute instances, clusters, attach compute, manage capacity, cost optimization

CORE
🧪 Experiments

Run experiments, log metrics, track runs, compare model performance

OPTIONAL
📓 Azure ML SDK v2

Python SDK, CLI, programmatic workspace management

🎯 Learning Actions

📚 Learn
Microsoft Learn Azure ML modules
🛠️ Practice
Create workspace and run experiments
✅ Prove
Train a model in Azure ML workspace
3
Train and Evaluate Models
3-4 weeks
2-3 hrs/day
▼
✅ Core Skills = Must complete to move forward | ◻ Optional = Nice-to-have if time permits
CORE
🎯 Model Training

Training scripts, logging, environments, distributed training, GPU compute

CORE
🔧 Hyperparameter Tuning

Grid search, random search, Bayesian optimization, early termination policies

CORE
🤖 AutoML

Automated machine learning, feature engineering, model selection, ensembles

CORE
🔄 ML Pipelines

Pipeline creation, steps, components, scheduling, parameterization

OPTIONAL
🧠 Deep Learning

PyTorch, TensorFlow, neural networks, transfer learning

🎯 Learning Actions

📚 Learn
Microsoft Learn model training modules
🛠️ Practice
Tune hyperparameters and use AutoML
✅ Prove
Build an ML pipeline with multiple steps
4
Deploy ML Solutions
2-3 weeks
2 hrs/day
▼
✅ Core Skills = Must complete to move forward | ◻ Optional = Nice-to-have if time permits
CORE
🚀 Model Deployment

Real-time endpoints, batch endpoints, online inference, deployment strategies

CORE
🔍 Monitoring

Model performance monitoring, data drift detection, application insights

CORE
⚖️ Responsible AI

Model interpretability, fairness evaluation, explainability, bias detection

CORE
📦 MLOps

Model versioning, CI/CD for ML, model registry, automated retraining

OPTIONAL
🔒 Security

Managed identities, Key Vault integration, network isolation, RBAC

🎯 Learning Actions

📚 Learn
Microsoft Learn deployment modules
🛠️ Practice
Deploy model and monitor performance
✅ Prove
Practice DP-100 questions

🎓 Target Certification

DP-100: Designing and Implementing a Data Science Solution on Azure

This certification validates your expertise in using Azure Machine Learning to train, evaluate, and deploy machine learning models. It demonstrates your ability to implement MLOps practices and is highly valued by employers in the AI/ML space.

Practice DP-100 Questions

🎯 You're Job-Ready When You Can:

✅ Manage Azure ML Workspace

Create and configure workspaces, datasets, and compute resources

✅ Train ML Models

Run experiments, tune hyperparameters, and use AutoML effectively

✅ Build ML Pipelines

Create automated pipelines for data processing and model training

✅ Deploy Models

Deploy real-time and batch endpoints for inference

✅ Implement Responsible AI

Evaluate model fairness, interpretability, and explain predictions

✅ Pass DP-100 Certification

Validate your knowledge with Microsoft's official Azure Data Scientist credential