Data Visualization with Numpy and Pandas
The goal of this course is to make the trainees expert on working with Pandas and NumPy python libraries. This training will be helping folks to achieve proficiency in introducing the concept of data science with the help of libraries that we will be covering here. This course has been focused on training folks on Pandas and NumPy. All the concepts that revolve around these libraries will be detailed very precisely through this course. The sole objective of this course is to enrich the trainees with the entire set of skills that are required to work with these python-based libraries.
Panda and NumPy is a library for Python, where NumPy helps by contributing to numerical work lads and computation works. Panda, on the other hand, is preferred for data wrangling and data manipulation-related works.
Both the NumPy and Panda constitute Pythons being a scientific language. Its possibility to encounter Matrix and Vector manipulation is possible with NumPy and Panda’s library (rather we call an essential).
NumPy means Numerical Python and is an open-source structure for mathematical needs. A must-have array for high-level mathematical functions. NumPy is associated with Machine learning in ways like Scikit-learn, Pandas, Matplotlib, and TensorFlow.
Panda, on the other hand, offers similar features in Machine learning and is the most widely-used Python library. It is easy to use, easy to structure, delivers high performance, and is a great data analysis tool.
Our course on Panda and NumPy will be a very good investment for all the candidates in making their careers. No matter if you are a fresher or an experienced professional even if you are new to Python and related skills the course is just meant for you guys. The skill list is long for the candidates with our Pandas and NumPy Tutorial. The exposure to these skills with detailed discussion is an added advantage and acts as a cherry on the cake with the advance tool kits like Python, Azure, and techniques like Machine Learning and Data Analysis.
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