mlxtend.tar.gz

visualization

This course assumes no prior experience and can teach anyone how to use Python to analyze and visualize data. Stop using Excel and start harnessing the power of Python.

Data analysis is one of the hottest careers of the 21st century. As an analyst, your goal is to peel back layers of data in order to answer questions of interest; that is the power of analytics. It allows you to take raw data and create meaningful, actionable insights.

In this course, you’ll learn how to use Python, NumPy, SciPy, Pandas, and Seaborn to perform data analysis and visualization. You’ll explore the four crucial steps for any data analysis project: reading, describing, cleaning, and visualizing data. In each step, you will work with the most common and popular tools that data analysts use every day. By the end of the course, you will be able to confidently extract knowledge and answers from data.

Python Data Analysis and Visualization

In the last chapter, while studying [how to read data from CSV files](https://www.educative.io/courses/python-data-analysis-and-visualization/comma-separated-files), we used Pandas. Now, we will look deeper at Pandas to process data.

To do so, we will be using the same [data set](http://archive.ics.uci.edu/ml/datasets/Adult) we used when learning about how to read in CSV data. Here are the first 5 rows to refresh your memory.

This lesson discusses principal data processing and data analytics techniques using Pandas. 

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Statistics and Counts