CBTNuggets – Python and pandas for data manipulation 2022-5 – Download

Descriptions

Python and Pandas for data manipulation. This intermediate training in Python and Pandas for data manipulation prepares data scientists to manipulate and analyze data coming from multiple sources, large and small, using Pandas. If this is your first time encountering the name of a Python library that is used in econometrics to transform multidimensional structured data sets, you should know that Pandas is a deceptively cute name for a truly powerful data analysis and manipulation tool. If you use Python for data analysis, Pandas is perhaps the only tool for data processing – transforming raw data into another format so that it is more useful. After this Pandas course, you’ll be able to quickly and easily select, filter, sort, clean, and combine your data. There is almost no time in your data science or data analytics career when it is too early to take on Pandas training. Of course, it’s important to learn the basics first without moving on to advanced tools. But as this Pandas course shows, manipulating and importing data isn’t just easier—in some cases, it’s only possible—with Pandas.

What will you learn

Extracting meaning from numbers
Manage large data sources and extract relevant data from them
Import, cleanup and calculation of statistics
Visualize data and make smarter decisions

Who is this course for?

This Python and Pandas training for data manipulation is considered associate-level open source training, which means it was designed for data analysts.

Python and pandas specifications for data manipulation

Publisher: CBTNuggets
Teacher: Jonathan Barrios
English language
Level: Intermediate
Number of courses: 113
Duration: 17 hours

Python and pandas content for data manipulation

Python and pandas for data manipulation

Requirements

This Python skills course is useful for new IT professionals with at least one year of data science experience, as well as seasoned practitioners looking to prove their data science skills.