Visual flowchart · 4 career paths · Stage-by-stage milestones
Career Roadmaps
Pick a path. Follow the flow. Build projects. Visual, stage-by-stage plans for Data Scientist, AI/ML Engineer, Data Analyst, and Gen AI Engineer.
1
1. Foundations (Month 1-2)
- Python (variables, data structures, OOP)
- Statistics basics (mean, median, std, distributions)
- Linear algebra (vectors, matrices, dot product)
- Probability (Bayes, conditional, distributions)
- SQL (joins, group by, window functions)
2
2. Data Handling (Month 3)
- pandas (loading, cleaning, merging)
- NumPy (vectorized operations)
- Matplotlib + Seaborn (EDA)
- Real-world dataset cleaning project on Kaggle
3
3. Classical ML (Month 4-5)
- Linear/Logistic Regression
- Decision Trees, Random Forest, XGBoost
- Clustering (K-means, DBSCAN)
- Model evaluation (CV, metrics, leakage)
- scikit-learn end-to-end project
4
4. Deep Learning Basics (Month 6-7)
- Neural network from scratch (NumPy)
- PyTorch or TensorFlow basics
- CNNs for image tasks
- RNNs / Transformers intro
- 1 DL project deployed
5
5. Business + Communication (Month 8)
- A/B testing
- Storytelling with data
- Dashboards (Tableau/Power BI)
- Stakeholder communication
- Case study practice
6
6. Job-Ready (Month 9-12)
- 3-5 portfolio projects on GitHub
- Resume + LinkedIn polished
- Mock interviews (SQL, stats, ML, behavioral)
- Apply + cold outreach
- Open-source / Kaggle competitions
🎯
Land the job
Apply, ace the interviews, negotiate, and ship. Re-scan your resume on ATS Scanner before every application.
1
1. Strong CS Fundamentals
- Data structures + algorithms
- System design basics
- Distributed systems intro
- Linux + Bash + Git mastery
2
2. ML Foundations
- Classical ML (same as DS)
- Deep learning theory + practice
- Math: linear algebra, calculus, prob
3
3. Software Engineering for ML
- Python testing (pytest)
- Type hints + linting
- FastAPI / Flask
- Docker + Docker Compose
- Async Python
4
4. MLOps Tooling
- MLflow / Weights & Biases
- DVC for data versioning
- Airflow / Prefect for orchestration
- Feature stores (Feast)
- Monitoring (Evidently, Prometheus)
5
5. Cloud + Deployment
- AWS SageMaker or GCP Vertex AI
- Kubernetes basics
- CI/CD with GitHub Actions
- Inference optimization (ONNX, TensorRT, quantization)
6
6. Job-Ready
- 1-2 deployed end-to-end ML projects
- Open-source contribution to ML lib
- System design + leetcode prep
- Interview practice with mock
🎯
Land the job
Apply, ace the interviews, negotiate, and ship. Re-scan your resume on ATS Scanner before every application.
1
1. Excel + Stats (Month 1)
- Excel: pivots, lookups, charts
- Descriptive stats (mean, median, variance)
- Probability basics
- Hypothesis testing (t-test, chi-square)
- Sampling + bias
2
2. SQL Mastery (Month 2)
- SELECT, joins, aggregations
- Window functions (RANK, LAG, ROW_NUMBER)
- CTEs + subqueries
- Query optimization
- 100 SQL questions on LeetCode / DataLemur
3
3. Python for Analytics (Month 3)
- pandas (groupby, merge, pivot)
- NumPy basics
- Matplotlib + Seaborn for EDA
- Jupyter notebooks workflow
- Cleaning a messy real-world dataset
4
4. BI Dashboards (Month 4)
- Tableau OR Power BI (pick one)
- DAX (for Power BI) / calculated fields
- Storytelling with dashboards
- Build 2 portfolio dashboards
- Publish to Tableau Public / Power BI Service
5
5. Business + Domain (Month 5)
- KPI design (DAU, retention, conversion)
- A/B testing fundamentals
- Cohort + funnel analysis
- Stakeholder communication
- Case study practice (Lewis Lin, Stratascratch)
6
6. Job-Ready (Month 6)
- 3 portfolio projects (SQL + dashboard + writeup)
- Resume + LinkedIn polished
- Mock interviews (SQL, case studies, behavioral)
- Cold outreach + apply
- Niche: marketing / product / finance analytics
🎯
Land the job
Apply, ace the interviews, negotiate, and ship. Re-scan your resume on ATS Scanner before every application.
1
1. LLM Fundamentals
- Transformer architecture (read Andrej Karpathy's series)
- Tokenization, embeddings, attention
- Prompt engineering basics
- OpenAI / Anthropic API
2
2. RAG Systems
- LangChain or LlamaIndex
- Vector DBs (Pinecone, Chroma, pgvector)
- Chunking strategies
- Hybrid search (BM25 + embeddings)
- Citations and grounding
3
3. Production LLM Apps
- FastAPI for LLM serving
- Streaming responses
- Async + concurrency
- Caching (semantic + exact)
- Cost optimization (prompt compression, model routing)
4
4. Agents + Tool Use
- LangGraph / CrewAI
- Function calling / tool use
- Multi-agent workflows
- Memory + state management
5
5. Evaluation + Observability
- LLM-as-judge
- RAGAS / DeepEval
- LangSmith / Helicone for tracing
- Human eval rubrics
- Regression testing for prompts
6
6. Advanced
- Fine-tuning (LoRA, QLoRA)
- Self-hosting open models (vLLM)
- Multimodal (vision + audio)
- Build 2-3 real applications
🎯
Land the job
Apply, ace the interviews, negotiate, and ship. Re-scan your resume on ATS Scanner before every application.