Data scientists are among the highest-paid professionals in tech. This roadmap takes you from Python basics to building ML models — personalized to your math background and career goals.
Last updated: March 2026 · 6 Months plan
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Data scientists extract insights from data to drive business decisions. The role combines statistics, programming, and domain expertise. In 2026, data scientists work with larger datasets, more sophisticated ML models, and increasingly use LLMs for analysis automation. Day-to-day work includes data cleaning (40% of the job), exploratory analysis, building predictive models, A/B test analysis, and presenting findings to stakeholders. Salaries range from $95,000 to $165,000, with senior roles at top companies exceeding $200,000.
Month 1: Python fundamentals and libraries — pandas, NumPy, and basic data manipulation. Month 2: Statistics and probability — descriptive stats, distributions, hypothesis testing, and confidence intervals. Month 3: Data visualization and SQL — matplotlib, seaborn, Plotly for visualization; SQL for database querying. Month 4: Machine learning fundamentals — regression, classification, decision trees, random forests, model evaluation. Month 5: Advanced ML and deep learning — ensemble methods, neural networks, NLP basics, and feature engineering. Month 6: Capstone project — end-to-end analysis from data collection to presentation, building your portfolio.
Data analysts focus on descriptive analytics — what happened and why — using SQL, Excel, and visualization tools. Data scientists build predictive models — what will happen — using Python, ML, and statistics. If you're new to data, starting as a data analyst is a smart stepping stone. Free Class AI helps you decide based on your math comfort level and career timeline, then builds the appropriate roadmap.
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