Data science is a multidisciplinary field that combines programming, statistics, and domain knowledge to extract insights from data. If you’re starting your journey into data science, a structured roadmap can help you build skills step-by-step without feeling overwhelmed.

Here’s a beginner-friendly roadmap:

1. Learn Python or R

Python is the most popular language in data science due to its simplicity and robust libraries (Pandas, NumPy, Matplotlib). R is more specialized for statistics but still widely used.

2. Understand Basic Statistics and Math

Focus on:

• Probability
• Descriptive statistics
• Linear algebra
• Calculus (for machine learning fundamentals)

3. Explore Data Manipulation and Analysis

Learn how to clean, transform, and analyze data using:

• Pandas (Python)
• Excel or SQL for querying datasets

4. Data Visualization

Start with Matplotlib, Seaborn, or Tableau to create visual representations of trends and patterns.

5. Learn SQL and Databases

Understand how to work with relational data, join tables, and run aggregate queries.

6. Intro to Machine Learning

Explore basic algorithms like linear regression, decision trees, and clustering using Scikit-learn.

7. Work on Projects

Apply your skills to real-world data sets (e.g., Kaggle, UCI Machine Learning Repository) to build a portfolio.

8. Understand Tools

Get familiar with Jupyter Notebooks, Git, and cloud tools like Google Colab or AWS Sagemaker.

Data science is a marathon, not a sprint. Be patient, stay curious, and build practical experience to stand out in this data-driven world.