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Signed-off-by: (Bit-Mage) <[email protected]>
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(Bit-Mage) committed Nov 1, 2024
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20 changes: 10 additions & 10 deletions Content/20241031150229-data_science_hierarchy_of_needs.org
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Expand Up @@ -7,25 +7,25 @@ From Upstream (root initiatives) to Downstream (consequent initiatives)

* collect
** instrumentation
** logging
** sensors
** [[id:665e997a-5628-4481-902c-47af4ba30336][logging]]
** [[id:0bb707ba-24a5-44b3-8e23-45ade88f605c][sensors]]
** external data
** user generated content
* move/store
** reliable data flow
** infrastructure
** [[id:54b9dd70-6104-4f01-8007-967b16f8e010][infrastructure]]
** pipelines
** ETL
** structured data storage
** unstructured data storage
** [[id:1656ed9e-9ed0-4ddb-9953-98189f6bb42e][ETL]]
** [[id:a0cb423e-4e39-4b7c-886d-57d9796f35ed][structured data storage]]
** [[id:64b40352-30c9-4b16-80df-2de3dd36d451][unstructured data storage]]
* explore/transform
** cleaning
** anomaly detection
** [[id:a9f08fcf-c62d-40c0-a7fb-53d7f827b5ea][anomaly detection]]
** prepprocessing/preparation
* aggregate/label
** A/B testing
** [[id:85ff1796-5245-4b42-8f97-64b1fc9487e0][A/B testing]]
** Experimentation
** simpler ML algorithms
* learn/optimize
** AI
** Deep Learning
** [[id:db649cb6-047e-426e-8cdc-774586ef30a0][AI]]
** [[id:20230713T110040.814546][Deep Learning]]
5 changes: 5 additions & 0 deletions Content/20241031165027-sensor.org
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:PROPERTIES:
:ID: 0bb707ba-24a5-44b3-8e23-45ade88f605c
:END:
#+title: Sensor
#+filetags: :electronics:
5 changes: 5 additions & 0 deletions Content/20241031165114-structured_data_storage.org
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#+title: Structured data storage
#+filetags: :data:
5 changes: 5 additions & 0 deletions Content/20241031165127-unstructured_data_storage.org
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:PROPERTIES:
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#+title: Unstructured data storage
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40 changes: 40 additions & 0 deletions Content/20241031165152-a_b_testing.org
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:PROPERTIES:
:ID: 85ff1796-5245-4b42-8f97-64b1fc9487e0
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#+title: A/B testing
#+filetags: :cs:

* Overview
** *Definition*:
- A/B testing, also known as split testing, is a method of comparing two versions of a web page, application, or product feature to determine which one performs better in achieving a specific goal.

** *Components*:
- *Control Group (A)*: The original version being tested.
- *Variant Group (B)*: The modified version where some changes have been made.
- *Metrics*: Quantifiable data points used to evaluate the performance, such as conversion rates, click-through rates, or user engagement metrics.

** *Process*:
- *Hypothesis Formation*: Define what change you believe will improve the outcome.
- *Design*: Create alternate versions (A and B) of the element to be tested.
- *Randomization*: Users are randomly assigned to either version A or B to ensure fairness and reliability of the test results.
- *Data Collection*: Gather data on how users interact with both versions.
- *Analysis*: Use statistical methods to determine if the observed differences are significant.

** *Statistical Significance*:
- This refers to the likelihood that the results of the test are not due to random chance. P-values and confidence intervals are often used to assess this.

** *Tools and Software*:
- Common tools include Google Optimize, Optimizely, and Adobe Target, among others, which facilitate the execution and analysis of A/B tests.

** *Applications*:
- Used extensively in web design, marketing campaigns, product development, and UX/UI design.

** *Limitations*:
- *Sample Size Constraints*: Too small sample sizes may not yield significant results.
- *Time and Resources*: Testing can be time-consuming and require substantial resources.
- *External Validity*: Results may not always generalize beyond the specific setting or population tested.

** Connections:
- The success of A/B testing largely depends on correctly identifying impactful changes.
- It requires a balanced approach to data collection and analysis, ensuring that randomization and statistical interpretations are executed properly.
- Tools are integral for managing the complexity and scale of A/B tests, especially as the number of tests or user groups increases.

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