IB Computer Science Topic B.2.9 Data Collection Changes Revision Notes (SL/HL)

IB Computer Science Topic B.2.9 Data Collection Changes Revision Notes (SL/HL) | RevisionDojo

Data collection is a critical component of building accurate and reliable models and simulations.
The quality, quantity, and organization of data directly impact the model's performance and predictive capabilities.
By making strategic changes to data collection processes, you can significantly enhance the effectiveness of your model or simulation.

Note

Key Terminologies:
- Model: A simplified representation of a system or process, often used for prediction or analysis.
- Simulation: The imitation of a real-world process or system over time, often using models.
- Training Data: The dataset used to teach a model or simulation how to perform a task.
- Test Data: The dataset used to evaluate the performance of a trained model or simulation.

Why Data Collection Matters

Accuracy: High-quality data ensures the model reflects real-world scenarios accurately.
Generalization: Diverse data helps the model perform well on new, unseen situations.
Efficiency: Well-organized data reduces computational resources and training time.

Example

Imagine training a weather prediction model with temperature data from only one city.
The model would struggle to predict weather patterns in other regions due to the lack of diverse data.

Strategies for Improving Data Collection

1. Collecting Additional Data

Expand the Dataset: Increasing the volume of data can help the model learn more patterns and reduce overfitting.

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Note

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Excepteur sint occaecat cupidatat non proident

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Tip

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum.

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Data collection is a critical component of building accurate and reliable models and simulations.
The quality, quantity, and organization of data directly impact the model's performance and predictive capabilities.
By making strategic changes to data collection processes, you can significantly enhance the effectiveness of your model or simulation.

Note

Key Terminologies:
- Model: A simplified representation of a system or process, often used for prediction or analysis.
- Simulation: The imitation of a real-world process or system over time, often using models.
- Training Data: The dataset used to teach a model or simulation how to perform a task.
- Test Data: The dataset used to evaluate the performance of a trained model or simulation.

Why Data Collection Matters

Accuracy: High-quality data ensures the model reflects real-world scenarios accurately.
Generalization: Diverse data helps the model perform well on new, unseen situations.
Efficiency: Well-organized data reduces computational resources and training time.

Example

Imagine training a weather prediction model with temperature data from only one city.
The model would struggle to predict weather patterns in other regions due to the lack of diverse data.

Strategies for Improving Data Collection

1. Collecting Additional Data

Expand the Dataset: Increasing the volume of data can help the model learn more patterns and reduce overfitting.

Definition

Paywall

(on a website) an arrangement whereby access is restricted to users who have paid to subscribe to the site.

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Note

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam quis nostrud exercitation.

Excepteur sint occaecat cupidatat non proident

Tip

Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum.

1. System fundamentals2 subtopics

2. Computer organization1 subtopic

3. Networks1 subtopic

4. Computational thinking, problem-solving and programming3 subtopics

5. Abstract data structures (HL)1 subtopic

6. Resource management (HL)1 subtopic

7. Control (HL)1 subtopic

A. Databases4 subtopics

B. Modelling and simulation4 subtopics

C. Web science6 subtopics

D. Object-oriented programming (OOP)4 subtopics

IB Computer Science B.2.9 Data Collection Changes Notes

1. System fundamentals2 subtopics

2. Computer organization1 subtopic

3. Networks1 subtopic

4. Computational thinking, problem-solving and programming3 subtopics

5. Abstract data structures (HL)1 subtopic

6. Resource management (HL)1 subtopic

7. Control (HL)1 subtopic

A. Databases4 subtopics

B. Modelling and simulation4 subtopics

C. Web science6 subtopics

D. Object-oriented programming (OOP)4 subtopics

Why Data Collection Matters

Strategies for Improving Data Collection

1. Collecting Additional Data

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Excepteur sint occaecat cupidatat non proident

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Why Data Collection Matters

Strategies for Improving Data Collection

1. Collecting Additional Data

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Excepteur sint occaecat cupidatat non proident

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