Course Outline
Introduction
- Overview of Kaggle
- Kaggle categories and performance tiers
Kaggle Competitions
- Overview of Kaggle competitions
- Competition formats
- Joining a Kaggle competition
- Forming a team
Kaggle Datasets
- Kaggle types of datasets
- Searching and creating datasets
- Organizing and collaborating
Kaggle Kernels
- Kaggle kernel types
- Searching for kernels
- Kernel editor and data sources
- Collaborating on kernels
Kaggle Public API
- Installing and authenticating
- Using Kaggle API with competitions
- Using Kaggle with datasets
- Creating and maintaining datasets
- Using Kaggle API with kernels
- Pushing and pulling a kernel
- Checking the status and output of a kernel
- Creating and running a new kernel
- Kaggle configurations
Summary and Next Steps
Requirements
- Python programming skills
- Knowledge of machine learning
- Understanding of statistics
Audience
- Data scientists
- Developers
- Anyone who wants to learn Data Science using Kaggle
Testimonials (5)
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
Machine Translated
All the examples used and the lecturing style was on point even for a begginer i was able to understand and the training was so patient and always willing to go extra mile when in need of assistance.
Mathipa Chepape - Vodacom
Course - Big Data Business Intelligence for Telecom and Communication Service Providers
Trainer was accommodative. And actually quite encouraging for me to take up the course.
Grace Goh - DBS Bank Ltd
Course - Python in Data Science
It is great to have the course custom made to the key areas that I have highlighted in the pre-course questionnaire. This really helps to address the questions that I have with the subject matter and to align with my learning goals.
Winnie Chan - Statistics Canada
Course - Jupyter for Data Science Teams
Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra