The first decade of your data science career is a journey of exploration, skill-building, and growth. Whether you're just starting out or midway through, having a roadmap helps you stay focused and confident.
Here’s a year-by-year breakdown of how to evolve from a beginner into a seasoned, high-impact data scientist.
Years 0–1: Build Your Foundation
Focus on learning the core technical skills and concepts:
Python & SQL (daily tools)
Pandas, NumPy, and scikit-learn
Data cleaning, EDA (Exploratory Data Analysis)
Basic statistics and probability
Intro to machine learning (regression, classification)
Version control with Git/GitHub
Goal: Be able to complete small, end-to-end projects.
Bonus:
Share your learning on LinkedIn or a blog
Contribute to one open-source project
Years 2–3: Get Real-World Experience
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