Data Analysis with Python, Pandas and Numpy Training Course
Python is a highly adaptable programming language celebrated for its clarity and ease of use. Pandas serves as a specialized Python library that offers powerful data structures for managing structured (tabular, multidimensional, potentially heterogeneous) and time-series data. Meanwhile, NumPy delivers essential capabilities for numerical computing through its efficient array operations. Combined, these tools create a solid foundation for performing effective data processing and analysis within the Python environment.
This guided, live session (available online or in-person) is designed for intermediate Python developers and data analysts looking to refine their proficiency in data manipulation and analysis using Pandas and NumPy.
Upon completion, participants will be equipped to:
- Configure a development environment integrating Python, Pandas, and NumPy.
- Develop data analysis applications utilizing Pandas and NumPy.
- Execute complex data wrangling, sorting, and filtering tasks.
- Perform aggregation operations and examine time-series data.
- Generate data visualizations using Matplotlib and other relevant libraries.
- Troubleshoot and optimize data analysis code.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation in a live laboratory environment.
Course Customization
- For tailored training options, please contact us to discuss specific requirements.
Course Outline
Day 1:
Review of Basic Python and Data Analysis Skills
Introduction to NumPy
- Construction of NumPy arrays
- Standard matrix operations
- Application of ufuncs
- Views and broadcasting techniques in NumPy arrays
- Performance optimization through loop avoidance
- Performance optimization using cProfile
Data Analysis with Pandas
- Leveraging vectorized data in pandas
- Data wrangling techniques
- Data sorting and filtering
- Aggregate operations
- Time-series analysis
Data Visualization with Matplotlib
- Creating diagrams with Matplotlib
- Integrating Matplotlib within pandas
- Producing high-quality visualizations
- Data visualization in Jupyter notebooks
- Exploring other Python visualization libraries
Day 2:
Additional Python Libraries for Data Analysis
- scikit-learn
- Scipy
- statsmodels
- RPy2
Summary and Recommended Next Steps
Requirements
- Fundamental Python and data analysis capabilities
Audience
- Python Developers
- Data Analysts
Open Training Courses require 5+ participants.
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Testimonials (1)
Trainer develops training based on participant's pace
Farris Chua
Course - Data Analysis in Python using Pandas and Numpy
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