Data Analysis with Python and Pandas

Learn Python for data analysis and visualization by analyzing large datasets and covering Python 3, Pandas, and Seaborn.

This Python course by Ardit Sulce will get you up and running with using Python for data analysis and visualization. You will learn how to handle, analyze and visualize data in Python by actually completing two big data analysis projects, one demonstrated through videos and another laid out through six exercises.

The course assumes you have no prior knowledge of Python, so you also get to learn the basics of Python in the first two sections of the course. However, if you already know Python, the first two sections can serve as a refresher before you jump into the data analysis and visualization part.

In the course you will learn to use Python third-party data analysis libraries such as Pandas, Matplotlib, Seaborn, just to mention a few and tools to boost your productivity such as Spyder and Jupyter.

As you progress through the course, you will be guided step by step on building a program that uses real world data containing hundreds of files and millions of records. You will learn to write Python code that downloads, extracts, cleans, manipulates, aggregates and visualizes these datasets using Python. Apart from following the video screencasts, you will also be required to write your own Python scripts from scratch for completing a data analysis project on income data. (Full View)

What Will you learn from this course:

• Build 10 advanced Python scripts which together make up a data analysis and visualization program.
• Solve six exercises related to processing, analyzing and visualizing US income data with Python.
• Learn the fundamental blocks of the Python programming language such as variables, datatypes, loops, conditionals, functions and more.
• Use Python to batch download files from FTP sites, extract, rename and store remote files locally.
• Import data into Python for analysis and visualization from various sources such as CSV and delimited TXT files.
• Keep the data organized inside Python in easily manageable pandas dataframes.
• Merge large datasets taken from various data file formats.
• Create pivot tables in Python out of large datasets.
• Perform various operations among data columns and rows.
• Query data from Python pandas dataframes.
• Export data from Python into various formats such as TXT, CSV, Excel, HTML and more.
• Use Python to perform various visualizations such as time series, plots, heatmaps, and more.
• Create KML Google Earth files out of CSV files.