Software Development Roadmap
- Computer Science Basics: problem‑solving, data structures, algorithms
- HTML5 & CSS3: semantic markup, Flexbox, Grid, responsive design
- JavaScript: ES6+, async, modules, DOM, fetch API
- TypeScript fundamentals
- Frontend Framework: React (components, hooks, state, routing)
- State/Data: context, React Query, forms, validation
- Backend: Node.js, Express, REST APIs, auth (JWT/cookies), security
- Databases: PostgreSQL/SQL, MongoDB/NoSQL, ORMs, indexing
- API Patterns: REST/GraphQL, pagination, caching
- Testing: Jest, React Testing Library, supertest
- DevOps Basics: Git/GitHub, CI/CD, envs, logging
- Cloud & Deploy: Netlify/Vercel, Render/Heroku, Docker basics
- Capstone: Full‑stack project and portfolio
Data Engineering Roadmap
- Python for DE: pandas, typing, packaging, virtualenvs
- Advanced SQL: window functions, CTEs, performance
- Data Modeling: star/snowflake, normalization, slowly changing dims
- ETL/ELT: batch vs streaming, ingestion patterns
- Orchestration: Airflow/Prefect, DAGs, scheduling, retries
- Big Data: Spark (PySpark), partitioning, optimization
- Warehouses/Lakehouse: BigQuery/Snowflake/Redshift, Delta/Iceberg
- Messaging/Streaming: Kafka, pub/sub, streaming ETL
- Files/Formats: Parquet, Avro, JSON, compression
- Cloud: S3/GCS/Azure Blob, IAM, networking basics
- Infra: Docker, IaC basics (Terraform)
- Reliability: data quality checks, lineage, monitoring
- Capstone: end‑to‑end data pipeline with reporting
Machine Learning / AI Roadmap
- Math Foundations: linear algebra, calculus (basics), probability
- Python Stack: NumPy, pandas, matplotlib, seaborn
- Classical ML: scikit‑learn, model selection, pipelines
- Evaluation: metrics, cross‑validation, bias/variance
- Feature Engineering: encoding, scaling, leakage prevention
- Deep Learning: PyTorch/TensorFlow, MLPs, CNNs, RNNs (basics)
- NLP/CV: tokenization, transformers overview, image pipelines
- MLOps: experiment tracking, model registry, monitoring
- Deployment: REST/Batch, FastAPI, Docker
- Responsible AI: fairness, privacy, data governance
- Capstone: ML/AI project with deployment
Data Science Roadmap
- Statistics: descriptive/inferential, sampling, confidence intervals
- Python & SQL for analysis
- EDA: cleaning, outliers, feature understanding
- Visualization: matplotlib, seaborn, storytelling
- Experiments: A/B testing, power, causal basics
- ML for DS: regression, classification, model interpretation
- BI/Dashboards: Power BI/Tableau/Metabase
- Communication: insights, presentations, stakeholder alignment
- Capstone: case studies and executive summary
DevOps / Cloud Roadmap
- Linux & Networking: shell, processes, TCP/IP, DNS, SSL
- Version Control & CI/CD: Git, GitHub Actions
- Containers: Docker, images, registries
- Orchestration: Kubernetes basics, deployments, services
- Infrastructure as Code: Terraform fundamentals
- Cloud Services: AWS/Azure/GCP core (compute, storage, IAM)
- Observability: logs, metrics, traces; Prometheus/Grafana
- Security: secrets, policies, least privilege, backups
- Reliability: SRE concepts, scaling, cost control
- Capstone: production‑style deployment pipeline
UI/UX Design Roadmap
- Design Principles: layout, color, typography, contrast
- UX Research: interviews, surveys, personas, journeys
- Information Architecture & navigation
- Wireframing & Prototyping in Figma
- Interaction Design & micro‑interactions
- Design Systems & components
- Accessibility (WCAG) & responsiveness
- Developer Handoff & assets
- Portfolio: case studies and presentation
Project Management (Tech) Roadmap
- Foundations: SDLC, Agile/Scrum, Kanban
- Planning: roadmaps, scope, requirements, estimation
- Execution: backlog, sprints, standups, retros
- Tools: Jira/Trello, docs, diagrams
- Stakeholders: communication, negotiation, alignment
- Risk & Quality: RAID logs, testing strategies, UAT
- Metrics & Reporting: burndown, velocity, OKRs
- Capstone: manage a cross‑functional project