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Autor Tópico: Data Visualization with Numpy and Pandas  (Lida 83 vezes)

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Data Visualization with Numpy and Pandas
« em: 20 de Julho de 2021, 08:54 »
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 35 lectures (4 hour, 55 mins) | Size: 1.73 GB
Learn how to get you up and running with data analysis and visualization using NumPy and Pandas

What you'll learn

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.
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.

Requirements

Like we always say to candidates what makes the difference is to have a learning attitude, apart from this we will take care of everything.
Basic knowledge of Python and Mathematics (like Linear algebra understanding).
No prior information for machine learning is needed.
Basic computer programming terminologies.
Passion to learn new technology

Description

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, MatDescriptionlib, 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.
Who this course is for:

This Pandas and NumPy Tutorial Course is designed for professionals with different backgrounds who are willing to learn data science in simple and easy steps using Python as a programming language. If you are into any kind of data and analytics work on any platform then this course is very useful for you. Managers and seniors should look up to this course as the current market is at its transition stage where you must have a good understanding of data and analysis techniques. However, for the convenience of our readers, we have listed some probable profiles that should opt this Pandas and NumPy Tutorial for maximizing their job scoring possibilities.
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