Published 07/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 36 lectures (6h 26m) | Size: 2.53 GB
Learn Pandas since scratch to Ninja!
What you’ll learn
Learn about data science
How Pandas library works with it’s building blocks
Know a complete Panda’s set of tools for data analysis, data manipulation and data visualization
Practical examples including time series and the analysis of finantial market
Requirements
Python, Matplotlib, Numpy
Description
Pandas is the most demading python library for Data Science, it comes with a pletora of tools. It provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.
If you are planning to develop or improve your career in Data Science or Machine Learning, is a must to learn about Pandas.
This Course of Pandas offers a complete view of this powerful tool for implementing data analysis, data cleaning, data transformation, different data formats, text manipulation, regular expressions, data I/O, data statistics, data visualization, time series and more.
What you’ll see in the course?
– Data Series and Dataframes,
– Indexing and Multi Indexing,
– Range slicing,
– Group data by condition,
– Concat, Append, Join,
– Pandas and Categorical Data,
– One Hot Encoding,
– Explorative data analysis,
– Utility functions and custom functions,
– Data cleaning,
– Data visualization,
– Statistics,
– Text Manipulation,
– Regular expressions,
– Data transformation,
– Pivot Tables,
– Stack and Melt,
– Wide_to_long,
– Crosstab,
– Data I/O,
– Datetime functions,
– Time series and more.
This course is a practical course with many examples, because the easiest way to learn is practicing!, then we’ll integrate all the knowledge we have learned in a Capstone Project developing a preliminary analysis, cleaning, filtering, transforming and visualize data using the famous IMDB dataset.
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