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Course Outline
Day 1:
Review of Basic Python and Data Analysis Skills
Introduction to NumPy
- Constructing NumPy arrays
- Executing common matrix operations
- Utilizing ufuncs
- Applying views and broadcasting to NumPy arrays
- Enhancing performance by eliminating unnecessary loops
- Optimizing execution speed with cProfile
Data Analysis using Pandas
- Leveraging vectorized data in pandas
- Techniques for data wrangling
- Sorting and filtering datasets
- Performing aggregate operations
- Interpreting time-series data
Data Visualization with Matplotlib
- Generating diagrams using Matplotlib
- Integrating Matplotlib with pandas
- Producing high-quality visualizations
- Displaying data within Jupyter notebooks
- Exploring alternative visualization libraries in Python
Day 2:
Additional Python Libraries for Data Analysis
- scikit-learn
- Scipy
- statsmodel
- RPy2
Summary and Future Directions
Requirements
- Fundamental knowledge of Python and data analysis principles
Target Audience
- Python developers
- Data analysts
14 Hours
Testimonials (1)
Trainer develops training based on participant's pace
