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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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

# Lists in Python

As mentioned in the previous section, **lists** are a useful data structure in Python. Lists are *ordered*, *changeable*, and allow for *duplicates*. You create a list with the [].

We can slice a list. _Slicing_ means to extract a part of the list. When slicing, the first number is included in the return set while the last number is not. For example:
```python
p1 = [1,2,3,4,5,6,7,8,9,10]
p2 = p1[0:5]
```
The list `p2` will be `[1,2,3,4,5]`. Because we'll start from __0th__ index of `p1` and will stop at __5th__ index slicing all the elements till __4th__ index. 

> __Note:__ You can also write `p2 = p1[0:5]` as `p2 = p1[:5]`.

Here are many of the ways you can use lists.

Please take the time to review the comments and the `print` statements to understand what each line is doing. You can tie the print statements to the code by the lines printed above the results in the output.

This lesson discusses the common built-in data structures of Python used for data processing.


What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Data Structures

Lists in Python #