{"id":1495,"date":"2023-03-02T18:07:42","date_gmt":"2023-03-02T18:07:42","guid":{"rendered":"https:\/\/michaeldebeer.com\/Resume\/?page_id=1495"},"modified":"2026-01-27T20:52:53","modified_gmt":"2026-01-27T20:52:53","slug":"analytics-and-python","status":"publish","type":"page","link":"https:\/\/michaeldebeer.com\/Resume\/analytics-and-python\/","title":{"rendered":"Python"},"content":{"rendered":"\n
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Using Python for web analytics and A\/B testing moves you beyond the limitations of standard dashboards. It allows you to handle massive datasets, automate complex statistical tests, and build custom attribution models that GA4 can’t do out of the box.<\/p>\n<\/div><\/div>\n\n\n\n

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Data Acquisition & Cleaning
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Python is used to merge data from different sources (e.g., matching GA4 session data with internal CRM sales data). You\u2019ll handle missing values, filter out bot traffic, and ensure timestamps are aligned for accurate funnel analysis.<\/p>\n<\/div><\/div>\n\n\n\n

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Bayesian A\/B Testing
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Standard (Frequentist) A\/B testing gives you a p-value, which can be hard to interpret. Python allows you to run Bayesian A\/B tests<\/strong>, which provide the actual probability that “Variant B is better than Variant A.<\/p>\n<\/div><\/div>\n\n\n\n

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Automated Sample Size & Power Calculation<\/h2>\n\n\n\n

Most people guess how long a test should run. You can calculate it exactly.<\/p>\n\n\n\n

Built automated Python scripts for Power Analysis to determine minimum sample sizes and MDE (Minimum Detectable Effect) prior to launch, optimizing traffic allocation and preventing underpowered tests.<\/p>\n\n\n\n

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Using Python for web analytics and A\/B testing moves you beyond the limitations of standard dashboards. It allows you to handle massive datasets, automate complex statistical tests, and build custom attribution models that GA4 can’t do out of the box. Data Acquisition & Cleaning Python is used to merge data from different sources (e.g., matching … <\/p>\n

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