<aside> 💡 Note for Readers:

• Certain lines in this document contain comments that may appear unnecessary. These comments are included solely for my study purposes. Thank you for your understanding. </aside>

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Python : This python for python developers, but data science aspirants should also know more about it, so check it out

• Definition, importance, and real-world applications
• difference between data science, data analytics and machine learning
• Data collection, exploration, modeling, evaluation, and deployment.
• python, tablue, R, sql
• case study
• IBM Explanation
• python vs R
• jupyter notebook
• Data Preprocessing steps
• Test , train and validation data
• Modelling Approaches
• Deployment platforms
• deaplarning, machine learning , AI and data science