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How to Learn Data Science from Scratch

How to Learn Data Science from Scratch

Learning data science is one of the boldest steps you can take in 2025. As the world evolves, so does technology, and there is a need for every individual to level up in the digital space. Data science, as it is known, is a field that uses scientific methods to analyze data and extract insights.

It combines techniques from mathematics, statistics, computer engineering, and artificial intelligence. The knowledge of data science and machine learning is essential in building the next generation of robotic systems and AI-powered software. Thus, selecting a trusted learning platform is crucial for successful learning.

Key Steps to Learning Data Science

As someone learning from scratch, you must set priorities and follow a structured approach:

1. Learn the Basics of Programming

  • Choose Python or R as your primary language.
  • Understand data structures, loops, functions, and libraries like NumPy and Pandas.
  • Recommended resource: Python Data Science Handbook.

2. Understand Mathematics & Statistics

  • Linear Algebra (vectors, matrices, eigenvalues).
  • Probability & Statistics (mean, variance, distributions, hypothesis testing).
  • Calculus (derivatives, integrals for machine learning applications).

3. Learn Data Wrangling & Visualization

  • Pandas (for data manipulation).
  • Matplotlib & Seaborn (for data visualization).

4. Study Machine Learning

  • Supervised & Unsupervised Learning.
  • Regression, classification, clustering, and neural networks.
  • Libraries: Scikit-learn, TensorFlow, PyTorch.

5. Work on Real-World Projects

  • Participate in Kaggle competitions.
  • Build projects like fraud detection systems.
  • Contribute to open-source projects.

6. Join a Community and Network

  • Engage in Kaggle, GitHub, LinkedIn, and local tech groups.
  • Follow top data scientists on Twitter and LinkedIn.

To stay accountable throughout your journey, enrolling in a trusted tech school that offers structured courses in data science is recommended. I suggest exploring ridot.org—a great place to advance your career in data science.