Data Science Roadmap
A detailed roadmap for data science
Essential mathematics like linear algebra, calculus, statistics, probability required to understand data science algorithms.
Python is the primary language for data science, with libraries like NumPy, Pandas, Matplotlib.
Skills for cleaning, transforming, and analyzing data using libraries like Pandas and NumPy.
Git is a version control system for tracking changes in source code. GitHub is a platform for hosting repositories and collaborating on projects.
Understanding statistical concepts and methods for data analysis, hypothesis testing, etc.
Creating effective visualizations using libraries like Matplotlib, Seaborn, Plotly, etc.
Introduction to ML concepts like supervised/unsupervised learning, evaluation, etc.
Essential steps to prepare data before analysis or modeling.
Introduction to deep neural networks, TensorFlow/PyTorch, etc.
Handling and processing large datasets using distributed computing frameworks.
Using cloud services for data storage, processing, and machine learning.
Deploying models to production and managing the ML lifecycle.