Data-Science-Roadmap

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Data-Science-Roadmap is an Open Source research and education tool that provides a free self-learning roadmap for data science. It offers curated resources from beginner to advanced levels, covering programming, statistics, machine learning, and deep learning.

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At a glance

Pricing

Free · Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Data-Science-Roadmap?

Data-Science-Roadmap is an open-source repository designed to guide individuals through a comprehensive self-learning journey in data science. It meticulously outlines a roadmap from foundational concepts to advanced topics, including programming languages like Python and R, statistical analysis, machine learning, and deep learning. The roadmap is structured into beginner, intermediate, and advanced phases, each providing a curated list of free resources such as videos, online articles, and books. It emphasizes practical skills like data cleaning, visualization, and SQL, and offers guidance on preparing a workspace and avoiding common learning pitfalls. This tool is ideal for anyone looking to break into the data science field without financial barriers, offering a structured path and valuable learning materials.

Best used for

Ideal for students and job seekers who need to learn data science from the ground up, prepare for interviews, and build a strong portfolio. Especially valuable for those seeking a structured, free, and comprehensive guide to navigate the complexities of data science without needing to pay for courses.

Common actions

learn data science

study machine learning

understand statistics

practice programming

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Capabilities

Key features

Structured learning roadmap
Curated free resources
Covers programming languages
Includes machine learning
Deep learning modules
Practical project suggestions
Data cleaning guidance

Target Audience

studentjob seeker

Integrations

Not yet documented

Pricing & Plans

Free · Open Source

Free

FAQs

What programming languages are covered in the Data Science Roadmap?

The roadmap primarily focuses on Python, with extensive resources for Pandas, NumPy, and SciPy. It also includes resources for R, highlighting its utility for visualization and statistical analysis. The guide helps users set up their development environment with tools like Anaconda, Jupyter Notebook, and PyCharm.

Does the roadmap include practical projects or exercises?

Yes, the roadmap encourages applying learned concepts through projects at the end of each major phase (Beginner, Intermediate). It also suggests practicing SQL with platforms like InterviewMaster, HackerRank, LeetCode, and DataLemur, and provides links to Kaggle notebooks for data cleaning and visualization practice.

What advanced topics are covered in the Data Science Roadmap?

The advanced phase delves into Deep Learning, covering frameworks like TensorFlow and PyTorch, and explores advanced machine learning topics such as Deep Multi-Task and Meta Learning, Algorithmic Aspects of Machine Learning, and ML-based Computer Vision. It also includes Natural Language Processing (NLP).

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