What to learn for python data analysis

Learning Python data analysis needs: Python basic programming data structures: list, tuple, dictionary, NumPy array, Pandas data frame data processing: reading, writing, cleaning, exploration, visual statistical analysis: descriptive Statistics, hypothesis testing, correlation, regression Machine learning basics: supervised, unsupervised learning, model evaluation and tuning Data visualization tools: Matplotlib, Seaborn, Plotly Auxiliary tools and libraries: Pandas, scikit-learn, Jupyter Notebook

Knowledge required to learn Python data analysis

1. Python programming basics

• Variables, data types, operators
• Control flow (conditions, loops)
• Function, module, package

2. Data structure

• List, tuple, dictionary
• NumPy array, Pandas data frame

3. Data processing

• Data reading and writing
• Data cleaning and preparation
• Data exploration and visualization

4. Statistical analysis

• Descriptive statistics (mean, median, standard deviation)
• Hypothesis testing (t-test, ANOVA)
• Correlation and regression

5. Basics of machine learning

• Supervised learning (linear regression, logistic regression)
• Unsupervised learning (clustering, Principal component analysis)
• Model evaluation and tuning

6. Data visualization tools

• Matplotlib
• Seaborn
• Plotly

7. Other tools and libraries

• Pandas
• scikit-learn
• Jupyter Notebook

Learning resources

• Online courses: Coursera, Udemy, edX

• Books:

  • "Python Data Science Handbook"
  • "Python Data Analysis Practice"

• Tutorials and Documentation:

  • Official Python Documentation
  • Pandas Documentation
  • scikit-learn Documentation

##Tips

Learn step by step, starting from the basics.
• Practice is important, please try to solve real problems.
• Join an online community or forum to ask others for help and advice.

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