Data Analysis with Python, Pandas and Numpy Training Course
Python is a versatile programming language known for its simplicity and readability. Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data. Numpy provides fundamental support for numerical computing with its array operations. Together, they form a robust ecosystem for efficient data handling and analysis in Python.
This instructor-led, live training (online or on-site) is designed for intermediate-level Python developers and data analysts who aim to strengthen their skills in data analysis and manipulation using Pandas and NumPy.
By the end of this training, participants will be able to:
- Set up a development environment that includes Python, Pandas, and NumPy.
- Create a data analysis application using Pandas and NumPy.
- Perform advanced data wrangling, sorting, and filtering operations.
- Conduct aggregate operations and analyse time series data.
- Visualise data using Matplotlib and other visualisation libraries.
- Debug and optimise their data analysis code.
Course Format
- Interactive lecture and discussion.
- Abundant exercises and practical practice.
- Hands-on implementation in a live-lab environment.
Course Customisation Options
- To request a customised training session 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
- Optimising performance by avoiding loops
- Optimising performance with cProfile
Data Analysis with Pandas
- Using vectorised data in pandas
- Data wrangling
- Sorting and filtering data
- Aggregate operations
- Analysing time series
Data Visualisation with Matplotlib
- Plotting diagrams with Matplotlib
- Using Matplotlib from within pandas
- Creating quality diagrams
- Visualising data in Jupyter notebooks
- Other visualisation 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
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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