Data Science Roadmap
From Python fundamentals to machine learning basics.
Level 1
Python Foundation
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Python Fundamentals
Master Python basics and core concepts
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NumPy Foundations
Learn array manipulation with NumPy
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Pandas Foundations
Data manipulation with Pandas
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Level 2
Math Foundation
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Probability
Understand probability theory
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Linear Algebra
Mathematical foundations for ML
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Statistics
Statistical analysis and inference
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Calculus Basics
Derivatives and gradients
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Level 3
Data Foundation
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SQL & Databases
Database fundamentals and SQL
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Data Cleaning
Techniques to clean and prepare data
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Exploratory Data Analysis
Analyze datasets to summarize their main characteristics
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Level 4
Machine Learning
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Introduction to Machine Learning
ML fundamentals and algorithms
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Advanced Supervised Learning
Advanced ML techniques
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Unsupervised Learning
Clustering and PCA
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Frequently Asked Questions
Everything you need to know about this roadmap and your learning journey.
This roadmap is designed for aspiring data scientists, students, software engineers transitioning to ML, or anyone who wants a solid, structured path from the absolute basics of Python to machine learning.
On average, if you study 10-15 hours a week, it takes about 6-9 months to build deep competence across all modules, solve all standard challenges, and work on real-world projects.
High-school mathematics is sufficient to start. As you reach the advanced levels, we explicitly guide you through college-level Linear Algebra, Calculus, and Statistics so you can understand ML mechanics.
Yes, we focus exclusively on Python as it is the industry standard for Data Science and Machine Learning. You will also learn SQL for database interactions.
Absolutely. Every single module contains multiple exercises, real datasets, and practical problems where you write code, test your models, and receive automated feedback.
Yes! The curriculum covers coding tests, statistical concepts, SQL querying, and ML model theory, which are the main pillars of typical Data Science and Machine Learning engineering interviews.