course
A/B Testing in Python
मध्यवर्तीकौशल स्तर
अद्यतन 11/2025कोर्स मुफ्त में शुरू करें
इसमें शामिल हैअधिमूल्य or टीमें
PythonProbability & Statistics4 घंटा16 videos51 exercises4,000 एक्सपी11,621उपलब्धि का कथन
अपना निःशुल्क खाता बनाएँ
या
जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।हजारों कंपनियों में कार्यरत शिक्षार्थियों द्वारा पसंद किया जाता है
दो या दो से अधिक लोगों को प्रशिक्षण देना?
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Discover How A/B Tests Work
Did you know that you are almost guaranteed to participate in an A/B test every time you browse the internet? From search engines and e-commerce sites to social networks and marketing campaigns — all businesses hire the best data analysts, scientists, and engineers to leverage the power of AB testing. Testing different variants can help optimize the customer experience, maximize profits, inform the next best design, and much more.
Learn About A/B Testing in Python
You’ll start by learning how to define the right metrics before learning how to estimate the appropriate sample size and duration to yield conclusive results. Throughout this course, you’ll use a range of Python packages to help with A/B testing, including statsmodels, scipy, and pingouin.
By the end of the course, you will be able to run the necessary checks that guarantee accurate results, master the art of p-values, and analyze the results of A/B tests with ease and confidence to guide the most critical business decisions.
आवश्यक शर्तें
Hypothesis Testing in Python1
Overview of A/B Testing
In this chapter, you’ll learn the foundations of A/B testing. You’ll explore clear steps and use cases, learn the reasons and value of designing and running A/B tests, and discover the most commonly used metrics design and estimation frameworks.
2
Experiment Design and Planning
In Chapter 2, you’ll cover the experiment design process. Starting with learning how to formulate strong A/B testing hypotheses, you’ll also cover statistical concepts such as power, error rates, and minimum detectable effects. You’ll finish the chapter by learning to estimate the appropriate sample size needed to yield conclusive results and tackle scenarios with multiple comparisons.
3
Data Processing, Sanity Checks, and Results Analysis
Here, you’ll discover a concrete workflow for cleaning, preprocessing, and exploring AB testing data, as well as learn the necessary sanity checks we need to follow to ensure valid results. You’ll explore a detailed explanation and example of analyzing difference in proportions A/B tests.
4
Practical Considerations and Making Decisions
In the final chapter, you’ll develop frameworks for analyzing differences in means and leveraging non-parametric tests when several assumptions aren't met. You’ll also learn how to apply the Delta method when analyzing ratio metrics and discover the best practices and some advanced topics to continue the A/B testing mastery journey.
A/B Testing in Python
कोर्स पूरा
उपलब्धि प्रमाण पत्र अर्जित करें
इस क्रेडेंशियल को अपने लिंक्डइन प्रोफाइल, रिज्यूमे या सीवी में जोड़ें।इसे सोशल मीडिया पर और अपनी परफॉर्मेंस रिव्यू में साझा करें।
इसमें शामिल हैअधिमूल्य or टीमें
अभी दाखिला लेंजुड़ें 19 मिलियन शिक्षार्थी और आज ही A/B Testing in Python शुरू करें!
अपना निःशुल्क खाता बनाएँ
या
जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।