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A/B testing: The art and science of data-driven choices

BeginnerGuided Project

A/B testing is an essential task in the tool kits of tech giants such as OpenAI, Amazon, Google, and Netflix. It plays a crucial role in refining marketing approaches and enhancing user experiences. Dive into this captivating realm of data-driven decision-making with this A/B testing Guided Project. Is your company seeking to boost user satisfaction by launching a dark mode feature on your website? Then, seize this opportunity to master the art of data-driven choices, and discover which options work best for you.

4.3 (25 Reviews)

Language

  • English

Topic

  • Statistics

Enrollment Count

  • 176

Skills You Will Learn

  • Python, Data Analysis, A/B Testing

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • May 15, 2024
About This Guided Project
Welcome to our Introduction to A/B Testing Guided Project, where we delve into the intriguing world of data-driven decision-making. As a foundational skill for any data scientist, A/B testing frequently appears in data-centric job descriptions. In this guided project, get hands-on experience in analyzing data and interpreting results from A/B tests to add to your data science skill set.

Imagine that your company is considering the introduction of a dark mode feature on its website, with the goal of enhancing user experiences and potentially increasing conversions. Learn how to analyze data from A/B tests, analyze results using the p-value, and gain statistical intuition by using Python in this beginner-level project.
Generated using DALL·E 3
To begin, you explore A/B testing fundamentals, learning how this powerful technique enables businesses to compare and optimize different versions of a variable. You'll examine key metrics and formulate hypotheses to guide your experimentation.

The project continues with hands-on data manipulation using the pandas library, allowing you to clean, explore, and prepare the data set for analysis. Navigating through NumPy, you'll engage in statistical analysis, unraveling patterns and trends in user behaviour.

The heart of the project lies in hypothesis testing with statsmodels, where you'll assess whether the introduction of dark mode has a significant impact on website conversions. 

A look at the project ahead

After completing this project, you understand:
  • A/B testing fundamentals: Understand the core principles of A/B testing and its application in optimizing digital experiences.
  • Data manipulation: Learn to wrangle and analyze data efficiently using the pandas and NumPy libraries.
  • Hypothesis testing: Explore hypothesis testing techniques with the statsmodels library, enabling you to make informed decisions based on data.

What you'll need

No prior technical or industry-specific knowledge is required, and all tools will be provided.

Instructors

Lucy Xu

Data Scientist

I am a Data Scientist Intern at IBM. I am also currently in my fourth year at the University of Waterloo studying Statistics with a minor in Computing.

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Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. From modeling to storytelling, I bring a holistic approach to data science. Leveraging machine learning algorithms, I construct predictive models tailored to both real-world challenges as well as old, well-understood problems. My knack for data-driven storytelling ensures that the insights uncovered resonate with both technical and non-technical audiences. Open to collaboration, I'm eager to take on new challenges and contribute to transformative data-driven endeavors. Whether you seek to extract insights, enhance predictive models, or explore untapped potential within your datasets, I'm here to help. Feel free to connect to me via my LinkedIn profile. Let's learn from each other!

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Kang Wang

Data Scientist

I am a Data Scientist in the IBM. I am also a PhD Candidate in the University of Waterloo.

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Belinda Fu

Growth Marketer

Digital marketing specialist within organic social media, content creation, E-commerce, and email strategies. Belinda studied marketing with a focus on digital transformation and has experience founding her own startups, as well as working in Retail, B2B, agency, CPG, and EdTech.

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