Level: Beginner to Intermediate
Prerequisite: See below
You must use the best data to build the most valuable machine learning models. This idea is summarized in a famous quote, “Data trumps algorithm.” In this hands-on course, you will learn some of the most useful data wrangling techniques for producing the best data for your machine learning models.
This course aims for you to return to work and immediately employ these techniques to wrangle data, enhance data analyses, and craft the most valuable machine learning models. You will get hands-on experience wrangling data using the Python pandas library via a series of labs.
Want to know the best part?
Although the course uses Python because it is free, all the concepts/techniques you will learn apply to any machine learning technology you might use.
You Will Learn
- How to wrangle data in Python
- How to work with character data
- How to wrangle date and time data
- How to pivot/aggregate tables of data
- How to join tables of data
- Strategies for dealing with missing data
- Additional resources to extend your learning
- Business and data analysts
- BI and analytics developers and managers
- Business users
- Aspiring data scientists
- Anyone interested in building machine learning models
No skills in advanced mathematics or statistics are required!
Registrants must be familiar with Python and Python Notebooks or complete a complimentary online course before the conference. Access to the four-hour online course "Python Quick Start" will be provided to registrants three weeks prior to the event.
Students must bring their laptops to class.
- Windows or Max OS X
- 64-bit operating system
- 8 GB available RAM, 16 GB preferred
- 4 GB of HD space for Anaconda Python installation
Anaconda Python is used in this course because it is free, easy to install, and has all the needed libraries.
Laptop setup is required BEFORE the conference. Instructions will be emailed to registrants before the event.
There is no time allotted in class for laptop preparation.
* Enrollment is limited to 40 attendees.