1. Set up the Python environment
- Install python and make sure the version is 3.6 or higher.
- Install the necessary libraries: NumPy, pandas, scikit-learn, Matplotlib, Seaborn.
- Create a Jupyter Notebook or use your favorite IDE.
2. Data operation and exploration
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NumPy: Numerical calculations and operations Arrays.
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Pandas: Data structures and operations, such as DataFrame and Series.
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Data exploration: Use Pandas functions (such as head(), tail(), info()) and Matplotlib (Data visualization) to explore data.
3. Data cleaning and preparation
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Data Cleaning: Handle missing values, outliers and duplicates.
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Data preparation: Convert data into the required format for analysis.
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scikit-learn: Used for feature scaling, data standardization and data segmentation.
4. Data analysis technology
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Descriptive statistics: Calculate the mean, median, standard deviation and other indicators.
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Hypothesis testing: Test the statistical significance of data, such as t-test and ANOVA.
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Machine Learning: Extract patterns from data using supervised and unsupervised algorithms such as linear regression and K-means clustering.
5. Data visualization
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Matplotlib: Create a variety of charts and data visualizations.
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Seaborn: A more advanced data visualization library based on Matplotlib.
- **Create interactive visualizations using Pandas and Matplotlib/Seaborn.
6. Practical cases
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Data import: Import data from CSV, excel or sql database.
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Data preprocessing: Clean data, handle missing values and transform data.
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Data analysis: Analyze data using descriptive statistics, hypothesis testing, and machine learning techniques.
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Data Visualization: Create charts and data visualizations using Matplotlib/Seaborn.
7. Project deployment and collaboration
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Create and manage Python projects: Use virtual environments and version control systems.
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Deploy Python applications: Use cloud platforms or containerization technologies to deploy models and scripts to production environments.
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Team Collaboration: Use git and other collaboration tools to collaborate effectively in a team.
Conclusion
By following the steps in this guide, you will have a solid foundation to confidently perform data analysis using Python. Continuously practicing and exploring new data and techniques, you will become a skilled data analyst, able to unlock value from data and make informed decisions.
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