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Extract insights from data and build predictive models.
Data Scientists analyze complex raw data to solve business problems using machine learning, statistics, and programming. This roadmap guides you from basics to advanced AI/ML algorithms.
Learn Linear Algebra, Calculus, and Statistics which form the theoretical backbone of all ML algorithms.
Master Python. Use Pandas, NumPy, and Matplotlib to clean, transform, and visualize large datasets effectively.
Implement Regression, Classification, SVMs, and Random Forests with Scikit-Learn. Learn to evaluate models using metrics like F1-Score.
Move beyond traditional ML. Learn Neural Networks, TensorFlow/PyTorch, and Natural Language Processing (NLP) specifically for text.
Learn how to put your models into production. Understand SQL deeply, build data pipelines, and serve models via APIs.
Yes, a foundational understanding of Statistics, Probability, and Linear Algebra is crucial for Data Science.