Stand out in the competitive job market in the field of data science. Master A/B testing—a skill highly valued by employers. Learn to design experiments, analyze results using Python, and confidently showcase your knowledge in interviews.
A/B Testing for Data Science
1h 47m 56s
English
Paid
A/B Testing for Data Science is a 8-lesson 1 hour 47 minutes self-paced course by LunarTech. Stand out in the competitive job market in the field of data science.
Course facts
- Lessons
- 8
- Duration
- 1 hour 47 minutes
- Level
- All levels
- Language
- English
- Updated
- Instructor
- LunarTech
- Price
- Premium
Who teaches A/B Testing for Data Science? LunarTech
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 A/B Testing for Data Science?
0:00
/ #1: 1. A/B Testing Basics
All Course Lessons (8)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | 1. A/B Testing Basics Demo | 03:54 | |
| 2 | 2. Setting Hypothesis & Primary Metric | 10:32 | |
| 3 | 3. A/B Design | 18:38 | |
| 4 | 4. Running A/B Testing | 02:13 | |
| 5 | 5. A/B Test Results Analysis (Part 1) | 20:38 | |
| 6 | 6. A/B Testing Results Analysis (Part 2) | 22:49 | |
| 7 | 7. A/B Test Results Analysis Coding (Part 3) | 17:26 | |
| 8 | 8. Common Pitfalls A/B Test | 11:46 |
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Frequently asked questions
What prerequisites are needed for this course?
This course assumes a basic understanding of Python programming and fundamental statistical concepts. Familiarity with data analysis and handling datasets in Python will be beneficial as the course involves coding and analysis exercises related to A/B testing.
What specific tools will I learn to use in this course?
The course focuses on using Python for designing and analyzing A/B tests. Specific tools and libraries commonly used in Python for statistical analysis and data manipulation will be part of the curriculum, especially during the lessons on coding and results analysis.
Who is the target audience for this course?
This course is designed for data science professionals or students who aim to enhance their skills in experimental design and analysis. It is particularly beneficial for those looking to add A/B testing to their skill set for practical applications in data-driven decision-making and job interviews.
What is not covered in this course?
The course does not cover advanced statistical theories beyond what is necessary for A/B testing. It also does not delve into other types of experimental designs or cover machine learning topics unrelated to A/B test scenarios.
How does the depth of this course compare to other A/B testing courses?
This course provides a focused exploration of A/B testing, emphasizing practical skills in experiment design and result analysis using Python. It covers the full cycle of A/B testing, from hypothesis setting to understanding common pitfalls, making it suitable for those seeking hands-on experience rather than theoretical exploration.
What will I be able to build by the end of the course?
By the end of the course, you will have the skills to design and execute A/B tests, analyze their results, and identify potential issues. Practical exercises include coding for results analysis, enabling you to confidently apply these skills in real-world scenarios.
How much time should I expect to commit to this course?
The course comprises 8 lessons, each designed to build incrementally on your understanding of A/B testing. While the total runtime is not specified, students should anticipate additional time for coding exercises and practical application, depending on their familiarity with the subject matter.