Course description
Programming with Python for Data Science
ABOUT THIS COURSE
This practical course, developed in partnership with Coding Dojo, targets individuals who have introductory level Python programming experience. The course teaches students how to start looking at data with the lens of a data scientist by applying efficient, well-known mining models in order to unearth useful intelligence, using Python, one of the popular languages for Data Scientists. Topics include data visualization, feature importance and selection, dimensionality reduction, clustering, classification and more! All of the data sets used in this course are gathered live-data or inspired by real-world domains that can benefit from machine learning.
Do you work at this company and want to update this page?
Is there out-of-date information about your company or courses published here? Fill out this form to get in touch with us.
Upcoming start dates
Who should attend?
Audience:
- Data Scientist
Prerequisite:
- There are no prerequisite required for this course
Training content
Course Objective
- What machine learning is and the types of problems it is adept to solving
- How to represent raw data in a manner conducive to deriving valuable information
- How to use various data visualization techniques
- How to use principal component analysis and isomap intelligently to simplify your data
- How to apply supervised learning algorithms to your data, such as random forest and support vector classifier
- Concepts such as model selection, pipelining, and cross validation
Why choose QuickStart?
98% increased workplace productivity
94% instructor and course effectiveness
Partnered with vendors including Microsoft, Cisco, and Citrix
Contact this provider
Meet your career goals with QuickStart!
QuickStart exists to create world-class technologists by personalizing and individualizing training to address the massive skills gap in the IT industry. Through 20 years of research and data analysis, we’ve learned that a modern learner prefers to learn through multiple...