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Data Analysis with Python, Pandas and Numpy

 14 Hours
This instructor-led, live training in Ireland (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance 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 analyze time series data.
  • Visualize data using Matplotlib and other visualization libraries.
  • Debug and optimize their data analysis code.
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Accelerating Python Pandas Workflows with Modin

 14 Hours
This instructor-led, live training in Ireland (online or onsite) is aimed at data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis. By the end of this training, participants will be able to:
  • Set up the necessary environment to start developing Pandas workflows at scale with Modin.
  • Understand the features, architecture, and advantages of Modin.
  • Know the differences between Modin, Dask, and Ray.
  • Perform Pandas operations faster with Modin.
  • Implement the entire Pandas API and functions.
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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

Provisonal Upcoming Courses (Contact Us For More Information)

Data Analysis with Python, Pandas and Numpy

2025-05-08 09:30
14 hours
Online

Accelerating Python Pandas Workflows with Modin

2025-05-22 09:30
14 hours
Online

Accelerating Python Pandas Workflows with Modin

2025-06-05 09:30
14 hours
Online

Data Analysis with Python, Pandas and Numpy

2025-06-19 09:30
14 hours
Online

Data Analysis with Python, Pandas and Numpy

2025-07-03 09:30
14 hours
Online

Accelerating Python Pandas Workflows with Modin

2025-07-17 09:30
14 hours
Online

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