Data Analysis with Python, Pandas and Numpy Training Course
Python is a highly adaptable programming language renowned for its simplicity and clarity. Pandas is a Python library that offers data structures designed for handling structured (tabular, multidimensional, potentially heterogeneous) and time series data efficiently. Numpy supports numerical computing with powerful array operations. Together, these tools create a strong ecosystem for effective data management and analysis in Python.
This instructor-led training session (held either online or at your location) is tailored for intermediate-level Python developers and data analysts looking to refine their skills in data analysis and manipulation using Pandas and NumPy.
Upon completion of this course, participants will be able to:
- Establish a development environment incorporating Python, Pandas, and NumPy.
- Develop a data analysis application utilizing Pandas and NumPy.
- Execute advanced data manipulation tasks such as sorting and filtering.
- Perform aggregate operations and analyze time series datasets.
- Create visual representations of data using Matplotlib and other visualization tools.
- Debug and enhance the performance of their data analysis code.
Course Format
- Engaging lectures and discussions.
- A wealth of exercises and practical application opportunities.
- Hands-on implementation in a live-lab setting.
Customization Options for the Course
- To arrange a customized training session, please contact us to discuss your requirements.
Course Outline
Day 1:
Basic Python and Data Analysis Skills Review
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
- 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 developer
- Data analysts
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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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