Data science

Overview

Data science is the practice of extracting useful insights and predictions from data by combining statistics, programming, and domain knowledge. Typical workflows include framing a problem, collecting and preparing data, building and evaluating models, deploying them to production, and monitoring performance over time.

Core components

• Problem definition and success metrics
• Data acquisition and exploratory data analysis
• Feature engineering and selection
• Model training, tuning, and evaluation
• Deployment, monitoring, and governance

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Oracle Cloud Infrastructure Data Science Professional

You’re completing the Oracle Cloud Infrastructure (OCI) Data Science Professional course. This focuses on building, operationalizing, and managing ML solutions on OCI.

What you’ll practice

• Managed notebooks with OCI Data Science
• Conda environments and reproducibility
• Data flow and feature engineering at scale
• Training with Accelerated Data Science (ADS) SDK
• Experiment tracking, model catalogs, and model evaluation
• Model deployment with OCI Data Science Model Deployment
• MLOps on OCI: pipelines, monitoring, and governance

Key skills and topics

• Supervised and unsupervised learning fundamentals
• Model selection, cross‑validation, and hyperparameter tuning
• Metrics: classification, regression, and ranking
• Handling imbalance, leakage, and drift
• Responsible AI: fairness, explainability, and privacy

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Quick study plan

• Week 1: Refresh Python, statistics, and EDA. Set up OCI tenancy and notebooks.
• Week 2: Build baseline models. Track experiments with ADS. Document results.
• Week 3: Feature engineering at scale. Tune models. Evaluate with robust metrics.
• Week 4: Package and deploy to OCI Model Deployment. Add monitoring and alerts.
• Week 5: End‑to‑end project: data to deployment with readme and handoff notes.

Checklist

• OCI tenancy configured and access keys stored securely
• Notebook environment created with required Conda pack
• Data source connected and documented
• Baseline model and benchmark metric recorded
• Tracked experiments with ADS or MLflow equivalent
• Deployed model endpoint with versioning
• Monitoring dashboards and drift checks in place

Data Science Study Roadmap

Foundation (1-3 months)

• Mathematics & Statistics
  • Linear algebra fundamentals
  • Probability theory
  • Statistical inference
  • Hypothesis testing
• Programming
  • Python basics
  • Data structures and algorithms
  • NumPy, Pandas for data manipulation
  • Matplotlib, Seaborn for visualization

Data Analysis & Machine Learning (3-6 months)

• Exploratory Data Analysis
  • Data cleaning and preprocessing
  • Feature engineering techniques
  • Advanced visualization
• Machine Learning Fundamentals
  • Supervised learning (regression, classification)
  • Unsupervised learning (clustering, dimensionality reduction)
  • Model evaluation and validation
  • Scikit-learn workflows

Advanced Topics (6-12 months)

• Deep Learning
  • Neural networks architecture
  • TensorFlow or PyTorch
  • Computer vision and NLP basics
• Big Data Technologies
  • SQL and NoSQL databases
  • Apache Spark
  • Cloud computing platforms
• MLOps
  • Model deployment and serving
  • CI/CD for ML pipelines
  • Model monitoring and maintenance

Specialization & Projects (Ongoing)

• Domain-specific applications
  • Choose an industry focus (healthcare, finance, etc.)
  • Study relevant use cases and challenges
• Portfolio development
  • Build 3-5 comprehensive projects
  • Contribute to open-source
  • Participate in competitions (Kaggle)

Recommended Resources

• Books
  • "Python for Data Analysis" by Wes McKinney
  • "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron
  • "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
• Online Courses
  • Andrew Ng's Machine Learning courses on Coursera
  • Fast.ai's Practical Deep Learning for Coders
  • DataCamp or Codecademy for interactive programming practice
• Communities
  • Kaggle
  • Stack Overflow
  • Reddit's r/datascience and r/machinelearning

Remember: Data science is a continuous learning journey. Focus on building practical skills through projects rather than just consuming theoretical content.