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You will get to learn about the approach that is used to develop the data management based solution


Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. This Python course will get you up and running with using Python for data analysis and visualization.


This course has a project that will be based on Data Analytics with Data Exploration Case Study. In this project, we will be using the concepts covered in the course to develop the solution. You will get to learn about various new concepts in this project and will also master the topics that revolve around data analytics. Data Management for Retail Dataset will be the next important project that has been added to this training. You will get to learn about the approach that is used to develop the data management-based solution. To complete the projects, you will be working using python and all the libraries that we got covered in this training.


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 to 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 an 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.


Who this course is for:

Anyone who wants to learn the basics and various functions of Pandas.

Data Engineers, Architects, Analysts, Software Engineers, IT operations, Technical Managers, Data Scientists

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