Contents
Advanced Pandas Data Analytics
This course takes a look at some of the lesser-known but highly useful methods that can be used in Pandas for advanced data analytics. We'll explore the methods available to you in Pandas to make your code more efficient through evaluating expressions and conditional iterative statements.
We'll also look at methods for time series and windows operations and how these can be used for analyzing datetime objects.
This is a hands-on course that is full of real-world demonstrations in Pandas. If you want to follow along with the course, you can find everything you need in the GitHub repo below.
If you have any feedback on this course, please write to us at support@cloudacademy.com.
Learning Objectives
- Perform iterative operations in Pandas to make your code more efficient
- Learn about evaluation expressions and how to use them
- Perform time series data analysis using a variety of methods
Intended Audience
- Data scientists
- Anyone looking to enhance their knowledge of Pandas for data analytics
Prerequisites
To the most out of this course, you should already have a good understanding of handling data using Pandas. We recommend taking our Data Wrangling with Pandas course before embarking on this one.
Resources
The GitHub repository for this course can be found here: https://github.com/cloudacademy/advanced-pandas-for-data-analytics
Welcome. My name is Andrea Giussani and I am going to be your instructor for this course on Advanced Pandas for Data Analytics. Throughout this course, we’re going to look at advanced Pandas methods that are not well recognized among data scientists and analysts. This course has two main objectives.
First, we will investigate methods that are used to make your code more efficient. To do so, we will investigate the family of expression evaluation and as well as conditional iterative statements in Pandas.
Then, we will dive into Pandas methods to deal with time series and windows operations. These are very useful for performing analysis with datetime objects.
To better illustrate the methods, we are going to use a Bike Sharing Dataset, which contains the daily counts of bike-sharing observed from Capital Bikeshare system in Washington D.C. in the years 2011 and 2012. Please note that this set of data is freely available. You can download it and find more information on it through the GitHub repository for this course, the link to which is included in the course description.
To better understand the material covered in this course, we strongly recommend taking the Data Wrangling with Pandas course first, which is available in our content library.
Now, when you’re ready, I’ll see you in the next lecture!
Andrea is a Data Scientist at Cloud Academy. He is passionate about statistical modeling and machine learning algorithms, especially for solving business tasks.
He holds a PhD in Statistics, and he has published in several peer-reviewed academic journals. He is also the author of the book Applied Machine Learning with Python.