DATA SCIENTIST ROADMAP

You’ll receive a structured development roadmap that outlines skills, timelines, courses, and practical tasks. Follow the steps and reach the level employers require.

  • Understand the field’s scope and key concepts (data vs. information, analytics vs. data science).

  • Learn about data collection, storage, processing, and the roles of DS, DA, engineers, and business teams.

  • Clarify how a Data Scientist turns raw data into actionable insights and predictions.

  • Master structured problem‑solving, experimentation, and data mining approaches.

  • Build core coding skills for data manipulation, analysis, and automation.

  • Learn to preprocess raw data: handle missing values, outliers, transformations, feature engineering.

  • Understand distributions, hypothesis testing, confidence intervals, and statistical inference.

  • Get introduced to models (regression, classification, clustering) and evaluation metrics (accuracy, RMSE).

  • Learn to query relational databases: SELECT, JOIN, GROUP BY for data retrieval and aggregation.

  • Practice communicating insights visually via charts, dashboards, and narrative storytelling.

  • Understand frameworks for large‑scale data processing and storage.

  • Get hands‑on with libraries for training, evaluating, and deploying models.

  • Gain structured training and credentials (e.g., Coursera/edX Data Science Specializations).

  • Apply skills to real datasets: compete in competitions, build end‑to‑end data pipelines.

  • Acquire real‑world experience by working on live projects and collaborating with peers.

  • Document your work—projects, GitHub repos, results, and impact metrics—to showcase your expertise.

  • Dive deeper into specific domains, advanced techniques, and productionizing models at scale.

  • Stay current on new algorithms, tools, research papers, and community best practices.