Data science is one of the most in-demand and well-paid careers in technology. But the path from "interested beginner" to "employed data scientist" can feel overwhelming. Here is a clear roadmap. Phase 1: Programming Foundations (2-3 months) Learn Python well. Focus on: variables, functions, loops, file I/O, and object-oriented concepts. Libraries: pandas, NumPy, Matplotlib. Phase 2: Mathematics & Statistics (2 months) Linear algebra (vectors, matrices), calculus basics (derivatives, gradients), probability, and statistics (distributions, hypothesis testing, p-values). Phase 3: Machine Learning (3-4 months) Supervised learning: regression, classification, decision trees, SVMs, random forests. Unsupervised: clustering, PCA. Deep learning: neural networks, CNNs, RNNs. Phase 4: Tools & Engineering (2 months) SQL databases, Git version control, Jupyter notebooks, cloud platforms (AWS, GCP basics), REST APIs, Docker basics. Phase 5: Portfolio & Job Search Build 3-5 end-to-end projects on real datasets. Host on GitHub. Write about your work on LinkedIn or Medium. Apply for junior roles, internships, and Kaggle competitions. The total timeline: 12-18 months of consistent daily learning. It is achievable.