Pandas with Python for Data Science

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Pandas with Python for Data Science provided by Udemy is a comprehensive online course, which lasts for 6 hours worth of material. Pandas with Python for Data Science is taught by Exam Turf. Upon completion of the course, you can receive an e-certificate from Udemy. The course is taught in Englishand is Paid Course. Visit the course page at Udemy for detailed price information.

Overview
  • Learn how to get you up and running with data analysis and visualization using Pandas

    What you'll learn:

    • You will get to learn about the basics of pandas and python libraries, what it can offer, and what kind of problems could be solved using these libraries.
    • Data analysis and visualization using Pandas.

    The goal of this course is to make the trainees expert on working with Pandas 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 on Pandas. 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. In this unit, you will get to learn about the basics of these libraries, what it can offer, and what kind of problems could be solved using these libraries. The initial hour in this unit has been given to explain the introduction while the rest of the time has been devoted to explaining the main concepts.

    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. The training will include the following:

    • Installing Jupyter

      • Jupyter Environment

      • Read data using Pandas

      • Series vs Data Frame

      • Basic Operations in Pandas

      • Analyze the imported data

      • Renaming Columns

      • Sorting

      • Filtering Data

      • Filtering Function

      • Read Selective Columns & Rows