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Duration 14 hours
Course Outline
Day 1:
Refresher on Basic Python and Data Analysis Skills
Getting Started with NumPy
- Generating NumPy arrays
- Executing common matrix operations
- Leveraging ufuncs
- Applying views and broadcasting to NumPy arrays
- Enhancing performance by eliminating loops
- Improving performance using cProfile
Data Analysis using Pandas
- Utilizing vectorized data in pandas
- Techniques for data wrangling
- Sorting and filtering datasets
- Executing aggregate operations
- Analyzing time series data
Data Visualization with Matplotlib
- Creating diagrams using Matplotlib
- Integrating Matplotlib within pandas
- Producing high-quality visualizations
- Visualizing data within Jupyter notebooks
- Exploring alternative visualization libraries in Python
Day 2:
Exploring Additional Python Libraries for Data Analysis
- scikit-learn
- Scipy
- statsmodel
- RPy2
Summary and Recommendations for Next Steps
Requirements
- Foundational proficiency in Python and data analysis
Target Audience
- Python developers
- Data analysts
Testimonials (1)
Trainer develops training based on participant's pace
