Data Scientist from Scratch: Free Program

Free Roadmap: Data Scientist from Scratch for Beginners

Data Scientist analyzes large volumes of data, identifies patterns and trends for making informed decisions. The profession requires knowledge of mathematics, programming, and working with data. We offer a structured program of free resources designed for 8–10 months of intensive training. It will allow you to master the stack from basics to junior level without paid courses.

Stage 1: Basic Understanding of the Profession

Start with an overview of the specialist's role. Watch introductory videos on YouTube:

• What is Data Science.
• A Day in the Life of a Data Scientist.
• Interviews with Data Scientists.

To reinforce, read an article about the duties and tasks of a Data Scientist. This stage takes 1–2 weeks and builds motivation.

Stage 2: Fundamental Computer Science Basics

Without understanding how computers work, further learning will be ineffective. Take the "Computer Science Basics" course. Study:

• Computer architecture.
• Algorithms and data structures.
• Operating systems basics.

This stage is critically important — skipping it will lead to gaps in understanding libraries and frameworks.

Stage 3: Core Data Science Stack

Move on to key tools. Master the following free courses:

• Mathematics in Data Science (channel "Programmer's Library").
• Data Science Lessons (channel "Masters of Code").
• Introduction to Machine Learning and Data Science (channel "Ave Coder").

Focus on linear algebra, statistics, probability, and basic ML models. Note: free materials may become outdated, so check library versions.

Stage 4: Supporting Technologies for Junior Level

To apply knowledge in practice, study production-ready tools:

• Python from Scratch — the base language for DS.
• TensorFlow Full Course 2026 — framework for deep learning.
• Tableau — data visualization.
• SQL Basics — queries to relational DBs.
• ClickHouse and Columnar DBMS — handling analytical queries.
• Introduction to AirFlow — DAG orchestration for data pipelines.

Supplement with your own searches for tutorials and documentation. Practice on real datasets from Kaggle.

Continuous Development: Channels and Repositories

Keep your knowledge sharp:

• Follow channels: Data Science, SQL hub, Data Science | Machinelearning [ru].
• Repository for interview preparation.

After completing the program, look for junior positions. Expect test tasks on SQL, Python, and ML models.

Key Points:

• The program focuses on free resources but requires 8–10 months of daily practice.
• CS fundamentals are essential for understanding the stack.
• Check the relevance of library versions in courses.
• Practice on Kaggle will speed up progress to junior.
• Interview preparation is the final step.

Practical Learning Recommendations

Break the program into weekly goals:

| Stage | Duration | Key Skills |

|-------|-------------|------------------|

| 1 | 1–2 weeks | Profession overview |

| 2 | 1–2 months | CS basics |

| 3 | 2–3 months | Math + ML intro |

| 4 | 3–4 months | Tools stack |

Integrate projects: dataset analysis, model building, pipeline deployment. This will boost your employment chances.

— Editorial Team