title: Data analysis

category: page

slug: data-analysis

sortorder: 0917

toc: False

sidebartitle: Data analysis

meta: Data analysis is a broad set of activities that involves cleaning, processing, transforming and understanding a data collection.

Data analysis involves a broad set of activities to clean, process and

transform a data collection to learn from it. Python is commonly used

as a programming language to perform data analysis because many tools,

such as [Jupyter Notebook](/jupyter-notebook.html),

[pandas](/pandas.html) and [Bokeh](/bokeh.html), are written in Python

and can be quickly applied rather than coding your own data analysis

libraries from scratch.

* The following series on data exploration uses Python as the

implementation language while walking through various stages of

how to analyze a data set.

* [Part 1](http://blog.districtdatalabs.com/data-exploration-with-python-1)

gives insight into how you should think about data and clarify

what you are looking to learn.

* [Part 2](http://blog.districtdatalabs.com/data-exploration-with-python-2)

explains categorization and transforming a data set into one that

is easier to analyze.

* [Part 3](http://blog.districtdatalabs.com/data-exploration-with-python-3)

shows how to visualize the results of your data exploration.

* [The Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)

is available to read for free online, although I also recommend

buying the book as it is a great resource for learning the topic.

* [PyData TV](https://www.youtube.com/user/PyDataTV) contains all the

videos from the PyData conference series. The conference talks are

often given by professional data scientists and the developers who

write these analysis libraries, so there is a wealth of information

not necessarily captured anywhere else.