A/B Testing in Python

Overzicht

Learn How To Define, Start, And Analyze The Results Of An A/B Test. Improve Business Performance Through A/B Testing What you'll learn:

How to use A/B tests to improve business performanceDefine A/B testsStart A/B testsAnalyze the results of A/B testsMeasure the success of A/B testsHow to define a hypothesisDesign tracking for the metricsHow to prepare for a data science interview (when you get asked about A/B tests)How to design A/B tests for digital productsAdvanced considerations when you run multiple A/B tests at the same time A/B testing is a tool that helps companies make reliable decisions based on data.This is one of the fundamental skills you need to land a job as a data scientist or data analyst.Do you want to become a data scientist or a data analyst?If you do, this is the perfect course for you!Your instructor Anastasia is a senior data scientist working at a Stockholm-based music streaming startup.

She has earned two Master's degrees in Business Intelligence and Computer Science, and grown from a recent graduate to a Senior role in just 3 years. Anastasia has performed a significant number of A/B tests for large tech companies with hundreds of millions monthly users.By taking this course, you will learn how to:

· Define an A/B test· Start an A/B test· Analyse the results of an A/B test on your ownAlong your learning journey Anastasia will walk you through an A/B testing process for a fictional company with a digital product.

This case study unfolds throughout the course and touches on everything from the very beginning of the A/B testing process to the very end including some advanced considerations. Moreover, Anastasia takes some time to share with you her advice on how to prepare for the questions on the A/B test interview for a data scientist or data analyst position.One strong point of differentiation from statistical textbooks and theoretical trainings is that the A/B Testing in Python course will teach you how to design A/B tests for digital products that have millions or hundreds of millions of users.

It is a rare overview of the A/B testing process from a business, technical, and data analysis perspective.This is the perfect course for you if you are:

- a data science student who wants to learn one of the fundamental skills needed on the job- junior data scientists with no experience with A/B testing- software developers and product managers who want to learn how to run A/B tests in their company to improve the product they are buildingYou will learn an invaluable skill that can transform a company’s business (and your career along the way).So, what are you waiting for?Click the ‘Buy now’ button and let’s begin this journey today!

Lesprogramma

• Introduction to A/B Testing
Definition and Purpose
Historical Context and Applications
Importance in Data-Driven Decision Making
• Fundamental Concepts of A/B Testing
Control and Treatment Groups
Randomization and Bias Reduction
Key Metrics and KPIs
• Designing an A/B Test
Hypothesis Formulation
Sample Size Determination
Test Duration and Run-Time Considerations
• Conducting A/B Tests with Python
Introduction to Python Libraries: NumPy, Pandas, SciPy
Loading and Preparing Data
Exploratory Data Analysis for A/B Testing
• Statistical Analysis in A/B Testing
Descriptive vs. Inferential Statistics
Significance Testing: p-value and Confidence Intervals
Types of Statistical Tests: t-Tests, Chi-Square Tests
• Implementing A/B Tests in Python
Writing Code for Test Execution
Handling Data Inconsistencies and Anomalies
Interpreting Test Results
• Advanced Techniques in A/B Testing
Multi-armed Bandit Approach
Sequential Testing Methods
Bayesian A/B Testing
• Case Studies and Real-World Applications
E-commerce and Conversion Rate Optimization
Product Feature Testing
Marketing Campaign Evaluation
• Best Practices and Ethical Considerations
Avoiding Common Pitfalls and Misinterpretations
Ensuring Statistical Power and Validity
Ethical Implications in Experimentation
• Tools and Resources for A/B Testing
Overview of Industry Tools and Platforms
Open-source Libraries and Community Resources
Further Reading and Research Papers
• Final Project
Design, Conduct, and Present an A/B Test
Peer Reviews and Feedback
Discussion and Insights on Learning Outcomes

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Gegeven door

365 Careers and Anastasia K

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Vakgebieden

Data Science