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Case Study in A/B Testing

1h 56m 17s
English
Paid
Updated September 2026

Case Study in A/B Testing is a 6-lesson 1 hour 56 minutes self-paced course by LunarTech. A focused course on running A/B tests that actually produce trustworthy answers — useful whether you work in marketing, product, or data analysis.

Course facts

Lessons
6
Duration
1 hour 56 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
LunarTech
Price
Premium

A focused course on running A/B tests that actually produce trustworthy answers — useful whether you work in marketing, product, or data analysis.

Designing tests you can trust

You'll learn how to set up test and control groups correctly and avoid the setup mistakes that quietly invalidate results before you even collect data.

Reading the results

Coverage includes statistical significance, choosing the right metrics, and using analysis tools to interpret outcomes with confidence rather than guesswork.

Acting on what you find

The course closes the loop by covering how to turn test results into real changes and track whether those changes actually deliver the improvement the data predicted.

Who teaches Case Study in A/B Testing? LunarTech

LunarTech thumbnail

LunarTech is an online tech academy focused on data science, machine learning, and quantitative analysis — covering both the theoretical foundations (linear algebra, calculus, statistics) and the practical Python / SQL toolchain that working data scientists use. The school operates globally with cohort-based and self-paced tracks.

The CourseFlix listing carries twelve LunarTech courses spanning machine-learning theory, deep learning, applied data-science workflows, and the math fundamentals underlying the field. Material is paid and aimed at engineers and analysts transitioning into formal data-science roles or upskilling within them.

What lessons are included in Case Study in A/B Testing?

This is a demo lesson (10:00 remaining)

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#1: Introduction
All Course Lessons (6)
#Lesson TitleDurationAccess
1
Introduction Demo
02:47
2
Part 1 - Business Hypothesis and Primary Metric
10:11
3
Part 2 - Data Exploration and Visualization
23:18
4
Part 3 - Calculation of Pooled Estimates and Beyond
28:47
5
Part 4 - Statistical and Practical Significance
25:37
6
Download Python Code
25:37
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Frequently asked questions

What prerequisites are needed for this A/B Testing course?
The course does not list specific prerequisites, but familiarity with basic statistical concepts and data analysis would be beneficial. The course covers topics such as statistical significance and data exploration, which may require some foundational knowledge in these areas.
What kind of projects will I be working on in this course?
The course includes practical exercises related to designing experiments, analyzing data, and evaluating metrics. These activities will help you apply A/B testing methodologies to real-world scenarios, enhancing your ability to make data-driven decisions.
Who is the target audience for this A/B Testing course?
This course is designed for marketers, product managers, and data analysts who want to improve their decision-making skills using data-driven insights. The techniques taught are applicable across various industries to optimize strategies and enhance customer engagement.
How does the depth of this course compare to other A/B testing courses?
The course provides a comprehensive overview of A/B testing, covering core principles, experiment design, and result analysis. While it offers foundational knowledge, individuals seeking advanced statistical methodologies or in-depth software tool training may need additional resources.
What specific tools or platforms are covered in this course?
The course includes lessons on using software tools for data analysis, with a specific focus on Python. It provides downloadable Python code to aid in understanding and implementing A/B testing techniques.
What topics are not covered in this A/B Testing course?
The course focuses on the fundamental aspects of A/B testing, such as experiment design and result analysis. It does not cover advanced statistical modeling, machine learning applications, or platform-specific implementations beyond Python.
How can the skills learned in this course be applied to other careers?
Skills acquired from this course, such as experiment design, data analysis, and statistical evaluation, are valuable in various fields. Professionals in marketing, product management, and business analysis can leverage these skills to optimize strategies and improve decision-making processes.