Data Analysis with Python, Pandas and Numpy Training Course
Python is a versatile programming language renowned for its simplicity and readability. Pandas is a Python library that offers data structures for working with structured data (tabular, multidimensional, potentially heterogeneous) and time series data. NumPy provides foundational support for numerical computing through its array operations. Together, they create a robust ecosystem for efficient data handling and analysis in Python.
This instructor-led, live training (available online or onsite) is designed for intermediate-level Python developers and data analysts looking to improve their data analysis and manipulation skills using Pandas and NumPy.
Upon completing this training, participants will be able to:
- Set up a development environment comprising Python, Pandas, and NumPy.
- Develop a data analysis application utilizing Pandas and NumPy.
- Execute advanced data wrangling, sorting, and filtering operations.
- Perform aggregate operations and analyze time series data.
- Visualize data using Matplotlib and other visualization libraries.
- Debug and optimize their data analysis code.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request customized training for this course, please contact us to arrange.
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
- Utilizing vectorized data in pandas
- Data wrangling
- Sorting and filtering data
- Aggregate operations
- Analyzing time series
Data Visualization with Matplotlib
- Plotting diagrams with Matplotlib
- Using Matplotlib from within pandas
- Creating quality diagrams
- Visualizing data in Jupyter notebooks
- Other visualization libraries in Python
Day 2:
Other Python Libraries for Data Analysis
- scikit-learn
- Scipy
- statsmodel
- RPy2
Summary and Next Steps
Requirements
- Basic Python and data analysis skills
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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