Lecture Notes on Python for Data Science

Introduction to Python in Data Science

• Python is the most widely used programming language for data science tasks.
• Key benefits of Python include:
  • Easy to learn and debug
  • Object-oriented and open-source
  • High performance
  • Extensive libraries for data science
• Data Science Professional Certificate Program (in partnership with Purdue University and IBM) includes:
  • Master classes by experts
  • Exclusive hackathons and sessions
  • Covers tools such as NumPy, Pandas, SciPy, etc.
  • Industry projects (e.g., Uber, Amazon, Walmart)
  • Potential for hiring in major companies (Netflix, Amazon, Facebook, Adobe)
  • Average salary hike of 70%

Understanding Libraries in Python

• Definition: A library is a collection of code scripts that can be used iteratively to save time.
• Python libraries are not context-specific.
• Libraries can be installed using package managers like pip.

Key Python Libraries for Data Science

1. NumPy

• Open-source library for scientific computing and data analysis.
• Key features:
  • Multi-dimensional arrays
  • Mathematical functions (linear algebra, Fourier transforms, random number generation)
• Widely used in machine learning and image processing.

2. Pandas

• Open-source library for data manipulation and analysis.
• Main data structures:
  • Series: One-dimensional labeled array
  • DataFrame: Two-dimensional labeled data structure
• Key features:
  • Data cleaning and filtering
  • Data manipulation (grouping, merging, reshaping)
  • Integration with libraries like NumPy and SciPy.

3. Matplotlib

• Library for data visualization.
• Offers customizable tools for graphs, plots, charts, etc.
• Types of plots include line, scatter, bar, histogram, pie charts, etc.
• Built on NumPy for easy numerical data handling.

4. Scikit-learn

• Popular machine learning library.
• Comprehensive set of tools for:
  • Classification, regression, clustering
  • Model selection and preprocessing
• Consistent API for various algorithms.

5. Scrapy

• Fast open-source web crawling framework.
• Used for extracting data from web pages (supports XPath selectors).
• Helps gather data from APIs and follow DRY principles.

6. Keras

• High-level neural networks library that supports TensorFlow and Theano.
• Features:
  • Vast pre-labeled datasets
  • Layer and parameter implementation for building networks.

7. PyTorch

• Scientific computing package for deep learning.
• Features:
  • Tensor computations with strong GPU support
  • Flexible building of neural networks.

8. Beautiful Soup

• Library for web scraping.
• Helps collect and format data from web pages without APIs or CSVs.

9. Pygame

• Set of modules for writing video games.
• Features:
  • 2D graphics, sound, user input handling, event management.
• Popular for small to medium game development.

10. Theano

• Library for numerical computation in deep learning.
• Efficient computations on CPU and GPU.
• Features automatic differentiation for gradient computation.

Conclusion

• Many other helpful libraries exist for mastering data science with Python.
• Recommendations to explore frequently asked data science interview questions.
• Encourage enrollment in the data science professional certificate program.

Additional Resources

• Link to the course is available in the description.
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