Data Analysis with Python, Pandas and Numpy Training Course
Python is a versatile programming language recognized for its clarity and ease of use. Pandas is a Python package that offers data structures for managing structured (tabular, multidimensional, and heterogeneous) and time series data. NumPy provides essential support for numerical computing through array operations. Collectively, these tools establish a robust ecosystem for efficient data handling and analysis in Python, particularly suited for government applications.
This instructor-led, live training session (delivered online or onsite) is designed for intermediate-level Python developers and data analysts seeking to refine their capabilities in data analysis and manipulation using Pandas and NumPy, with a focus on relevance for government operations.
Upon completion of this training, participants will be able to:
- Configure a development environment encompassing Python, Pandas, and NumPy.
- Develop a data analysis application leveraging Pandas and NumPy.
- Execute advanced data wrangling, sorting, and filtering procedures.
- Perform aggregate operations and analyze time series data.
- Visualize data utilizing Matplotlib and other visualization libraries.
- Debug and optimize data analysis code for improved efficiency.
Course Format
- Interactive lectures and facilitated discussions.
- Extensive exercises and practical applications.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- Requests for tailored training content for government use may be submitted by contacting the provider for arrangement.
Course Outline
Day 1:
Review of Fundamental Python and Data Analysis Competencies
Foundations of NumPy
- Initialization of NumPy arrays
- Standard matrix operations
- Application of ufuncs
- Utilization of views and broadcasting techniques
- Performance enhancement through loop avoidance
- Performance optimization via cProfile
Data Analysis Utilizing Pandas
- Implementation of vectorized data structures in pandas
- Data wrangling processes
- Sorting and filtering methodologies
- Execution of aggregate operations
- Time series analysis
Data Visualization Using Matplotlib
- Generation of diagrams with Matplotlib
- Integration of Matplotlib within pandas workflows
- Production of high-quality diagrams
- Data visualization within Jupyter notebooks
- Exploration of alternative Python visualization libraries
Day 2:
Supplementary Python Libraries for Data Analysis
- scikit-learn
- Scipy
- statsmodel
- RPy2
Summary and Recommended Next Steps
Requirements
- Foundational knowledge of Python and data analysis
Target Audience
- Python developers
- Data analysts
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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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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