Offered By: IBM
Quick Introduction to A/B Testing for Data Scientists
Experimental design refers to how users are allocated to the different groups in an experiment. A/B testing is an experiment set up to compare two versions of something to decide which performs better.
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At a Glance
Experimental design refers to how users are allocated to the different groups in an experiment. A/B testing is an experiment set up to compare two versions of something to decide which performs better.
About
A/B testing is at the forefront of analytical decision-making for building and managing products in various companies. So what exactly is it? In plain terms, these tests are used to see how users react to changes in certain features or services. This could range from a minor shift in website button position to major changes in pricing strategies. A/B testing is extremely helpful for business analysis and product development alike because the team is able to obtain feedback and insights directly from actual users.
This lab is a quick and gentle introduction to A/B testing, if you are looking to start out in Data Analytics and Product Analysis, this is a great lab to start from. Future courses will include detailed information about each session.
A/B testing is at the forefront of analytical decision-making for building and managing products in various companies. So what exactly is it? In plain terms, these tests are used to see how users react to changes in certain features or services. This could range from a minor shift in website button position to major changes in pricing strategies. A/B testing is extremely helpful for business analysis and product development alike because the team is able to obtain feedback and insights directly from actual users.
This lab is a quick and gentle introduction to A/B testing, if you are looking to start out in Data Analytics and Product Analysis, this is a great lab to start from. Future courses will include detailed information about each session.
A Look at the Project Ahead
After this guided project, you will be able to:
After this guided project, you will be able to:
- Define a metric to track during your experiment
- State Null and Alternative hypothesis
- Select your Significance level and Power
- Design and run your experiment the right way
What You'll Need
This Guided Project mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.
This Guided Project mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.
Frequently Asked Questions
> Do I need to install any software to participate in this project?
Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.
> What web browser should I use?
The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.
Your Instructors
Aije Egwaikhide
Cindy Huang is a data science associate of the IBM Skills Network team. She has a passion for machine learning to improve user experience, especially in the area of computational linguistics.
Your Instructors
Aije Egwaikhide
Cindy Huang is a data science associate of the IBM Skills Network team. She has a passion for machine learning to improve user experience, especially in the area of computational linguistics.
Estimated Effort
1 Hour
Level
Beginner
Skills You Will Learn
Data Analysis, Data Science, Python
Course Code
GPXX04MXEN