The recommended approach for beginners to learn data science involves a combination of structured learning, practical application, and continuous improvement. Here’s a step-by-step guide:

1. Foundational Knowledge:

   • Mathematics and Statistics: Start with the basics of probability, statistics, linear algebra, and calculus.
   • Programming: Learn programming languages commonly used in data science, such as Python or R. Focus on basic syntax, data structures, and libraries like pandas, NumPy, and matplotlib.

2. Structured Learning:

   • Online Courses and Certifications: Enroll in online courses from platforms like Coursera, edX, Udacity, and SevenMentor’s Data Science course in Pune. Courses typically cover essential topics like data analysis, machine learning, and data visualization.
   • Books and Tutorials: Read foundational books like "Introduction to Statistical Learning" and follow online tutorials for hands-on practice.

3. Hands-On Practice:

   • Projects: Work on real-world projects to apply your knowledge. Use datasets from sources like Kaggle, UCI Machine Learning Repository, or government databases.
   • Competitions: Participate in data science competitions on platforms like Kaggle to solve real-world problems and improve your skills.

4. Specialized Skills:

   • Machine Learning: Dive deeper into machine learning algorithms and techniques. Use libraries like scikit-learn, TensorFlow, and Keras.
   • Data Visualization: Learn to create informative and compelling visualizations using tools like Tableau, Power BI, and libraries like seaborn and Plotly.