10-week plan to learn Python for data science and data analysis, along with hands-on projects for each week.

Week 1: Introduction to Python and Data Manipulation

• Resources:
  • Python Crash Course by Eric Matthes: book
  • Codecademy Python Course: codecademy.com
• Hands-on Project: Perform basic Python exercises to get familiar with the language and practice data manipulation using Python lists.

Week 2: Data Analysis with Pandas

• Resources:
  • Pandas User Guide: pandas.pydata.org
  • "Python for Data Analysis" by Wes McKinney: book
• Hands-on Project: Analyze a real-world dataset using Pandas. Practice loading data, performing data cleaning, filtering, and basic exploratory data analysis (EDA).

Week 3: Data Visualization with Matplotlib and Seaborn

• Resources:
  • Matplotlib Documentation: matplotlib.org
  • Seaborn Documentation: seaborn.pydata.org
• Hands-on Project: Create various visualizations (line plots, bar charts, scatter plots, etc.) using Matplotlib and Seaborn with a dataset of your choice.

Week 4: Statistical Analysis with SciPy and Statsmodels

• Resources:
  • SciPy Documentation: scipy.org
  • Statsmodels Documentation: statsmodels.org
• Hands-on Project: Perform statistical analysis on a dataset using SciPy and Statsmodels. Explore hypothesis testing, confidence intervals, and regression analysis.

Week 5: Machine Learning Fundamentals with Scikit-learn

• Resources:
  • Scikit-learn Documentation: scikit-learn.org
  • "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron: book
• Hands-on Project: Build a simple machine learning model (e.g., linear regression or classification) using Scikit-learn with a dataset of your choice.

Week 6: Unsupervised Learning and Clustering

• Resources:
  • "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili: book
  • Scikit-learn Clustering Documentation: scikit-learn.org
• Hands-on Project: Apply clustering algorithms (e.g., K-means or hierarchical clustering) to group similar data points in a dataset.

Week 7: Natural Language Processing (NLP) Basics

• Resources:
  • Natural Language Processing with Python (NLTK) Tutorial: www.nltk.org/book
  • "Applied Text Analysis with Python" by Benjamin Bengfort, Rebecca Bilbro, and Tony Ojeda: book
• Hands-on Project: Perform basic NLP tasks like tokenization, stemming, and sentiment analysis using NLTK and a text dataset.

Week 8: Deep Learning Fundamentals with TensorFlow

• Resources:
  • TensorFlow Tutorials: www.tensorflow.org/tutorials
  • "Deep Learning with Python" by François Chollet: book
• Hands-on Project: Build a simple neural network model using TensorFlow and apply it to a classification problem.

Week 9: Time Series Analysis

• Resources:
  • "Practical Time Series Analysis" by Aileen Nielsen: book
  • Statsmodels Time Series Analysis Documentation: statsmodels.org
• Hands-on Project: Analyze and forecast a time series dataset using techniques like ARIMA or LSTM.

Week 10: Final Project

• Choose a data science project that interests you (e.g., predicting house prices, sentiment analysis on social media, etc.) and apply the skills you've learned throughout the course. Use Python libraries like Pandas, Matplotlib, Scikit-learn, and any additional libraries relevant to your project.

Remember, learning Python for data science is a continuous process, and this plan is designed to give you a solid foundation. Adapt the pace to suit your learning style and feel free to explore additional resources and topics based on your interests. Good luck with your data science journey!