Python for Data Science – Study Plan

Here’s a suggested study plan to learn Python for data science with resources and timeline:

Week 1: Python Basics

• Learn basic Python syntax, data types, and control structures
• Learn how to work with strings, lists, and dictionaries
• Familiarize yourself with functions and modules in Python
• Resources:
  • Codecademy’s Python 3 Course (free)
  • Python for Data Analysis by Wes McKinney (book)

Week 2-3: Numpy and Pandas

• Learn how to use NumPy for numerical computing in Python
• Learn how to use Pandas for data manipulation and analysis
• Practice with sample datasets and perform basic operations on them
• Resources:
  • NumPy Tutorial by DataCamp (free)
  • Pandas Tutorial by DataCamp (free)
  • Python for Data Analysis by Wes McKinney (book)

 

Week 4-5: Data Visualization with Matplotlib and Seaborn

• Learn how to use Matplotlib and Seaborn for creating visualizations in Python
• Practice creating different types of charts and graphs
• Resources:
  • Matplotlib Tutorial by DataCamp (free)
  • Seaborn Tutorial by DataCamp (free)
  • Python for Data Analysis by Wes McKinney (book)

Week 6-7: Machine Learning with Scikit-Learn

• Learn how to use Scikit-Learn for machine learning in Python
• Practice with sample datasets and perform basic operations on them
• Learn about popular algorithms like linear regression, logistic regression, and k-nearest neighbors
• Resources:
  • Scikit-Learn Tutorial by DataCamp (free)
  • “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron (book)

Week 8-9: Deep Learning with TensorFlow and Keras

• Learn how to use TensorFlow and Keras for deep learning in Python
• Practice with sample datasets and perform basic operations on them
• Learn about popular deep learning architectures like convolutional neural networks and recurrent neural networks
• Resources:
  • TensorFlow Tutorial by DataCamp (free)
  • Keras Tutorial by DataCamp (free)
  • “Deep Learning with Python” by François Chollet (book)

Week 10: Capstone Project

• Apply your skills by working on a capstone project
• Use real-world data to perform analysis, visualization, and/or machine learning tasks
• Showcase your work by creating a report or presentation
• Resources:
  • Kaggle Datasets (free)
  • UCI Machine Learning Repository (free)
  • GitHub (for hosting and sharing your project)

Of course, this timeline is just a suggestion and can be adjusted based on your schedule and pace of learning. The resources mentioned are just a starting point and there are many other free and paid resources available online for learning Python and data science. Good luck!