Data Analysis with Python, Pandas and Numpy Training Course
Python is a versatile programming language renowned for its clarity and ease of use. Pandas is a Python library that offers data structures designed for working with structured (tabular, multidimensional, and potentially heterogeneous) data as well as time series data. NumPy provides foundational support for numerical computing through its array operations. Together, they create a powerful ecosystem for efficient data handling and analysis within Python.
This instructor-led, live training (available online or on-site) is designed for intermediate-level Python developers and data analysts looking to strengthen their skills in data analysis and manipulation using Pandas and NumPy.
Upon completion of this training, participants will be able to:
- Configure a development environment that includes Python, Pandas, and NumPy.
- Develop a data analysis application using Pandas and NumPy.
- Execute advanced data wrangling, sorting, and filtering operations.
- Perform aggregate operations and analyse time series data.
- Create data visualizations using Matplotlib and other visualization libraries.
- Debug and optimize their data analysis code.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live laboratory environment.
Course Customization Options
- To request customized training for this course, please contact us to make arrangements.
Course Outline
Day 1:
Review of Basic Python and Data Analysis Skills
Introduction to NumPy
- Creating NumPy arrays
- Common operations on matrices
- Using ufuncs
- Views and broadcasting on NumPy arrays
- Optimizing performance by avoiding loops
- Optimizing performance with cProfile
Data Analysis with Pandas
- Using vectorized data in pandas
- Data wrangling
- Sorting and filtering data
- Aggregate operations
- Analyzing time series
Data Visualization with Matplotlib
- Creating plots with Matplotlib
- Using Matplotlib from within pandas
- Creating high-quality plots
- Visualizing data in Jupyter notebooks
- Other visualization libraries in Python
Day 2:
Additional Python Libraries for Data Analysis
- scikit-learn
- Scipy
- statsmodel
- RPy2
Summary and Next Steps
Requirements
- Basic Python and data analysis skills
Target Audience
- Python developers
- Data analysts
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Data Analysis with Python, Pandas and Numpy Training Course - Enquiry
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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