Delve into Python data analysis and visualization using NumPy, SciPy, Pandas, and Seaborn. Gain insights into reading, cleaning, and visualizing data to generate actionable insights.

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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

# Introduction to scaling

The scale of your features matter for many machine learning algorithms. Having income values that range from 100 to 100,000 and ages that range from 0 to 100 can cause issues because of the large difference in scale of these two data columns. To deal with this, it is standard to rescale the data. There are many ways to do this, but the two most common ones are:

* Standard scaling
* Min/Max scaling.

# Types of scaling

## Standard scaling

Standard scaling subtracts the mean and divides by the standard deviation. This centers the feature on zero with unit variance.

This lesson explains how scaling helps cleaning the data.

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Scaling

Introduction to scaling #