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Questions and Answers

What are the skills that make data science happen according to the text?

Coding and computation skills

Why is it beneficial to write more robust, reproducible code?

Other data scientists can use it easily

When are coding and computation skills considered essential for writing maintainable code?

In production code

What was the author's perspective on good engineering earlier in their data science career?

Didn't always see the value of good engineering Signup and view all the answers

What should be ensured when using code samples or technology subject to open source licenses?

Compliance with licenses and/or rights Signup and view all the answers

Who disclaims all responsibility for errors or omissions in the work according to the text?

The publisher and the author Signup and view all the answers

What is the principle in writing good code that emphasizes not repeating information?

Don't Repeat Yourself (DRY) Signup and view all the answers

Why is it important to avoid repetition in code?

Avoiding repetition reduces the chances of introducing bugs, makes code easier to maintain, and improves readability. Signup and view all the answers

How does repetition in code contribute to complexity?

Repetition increases the opportunities for bugs and makes the code harder to read and understand. Signup and view all the answers

What benefits are associated with simplifying code as a project grows larger?

Simplifying code as the project grows larger has huge benefits, making it easier to reason about and reducing complexity. Signup and view all the answers

Name some techniques described in the text to fight against complexity in software development.

Avoiding repetition, making code modular, and improving readability. Signup and view all the answers

What challenges does longer code pose in terms of readability and understanding?

Longer code requires more time to read and understand, increasing complexity. Signup and view all the answers

What is the importance of simplicity in coding according to the text?

Simple is better than complex. Signup and view all the answers

How does complexity impact coding projects?

Complexity makes it hard to modify code when requirements change. Signup and view all the answers

Define complexity in the context of software development.

Complexity is anything related to the structure of a system that makes it hard to understand and modify. Signup and view all the answers

How can you identify when a system becomes more complex?

When a change breaks something unrelated in an unexpected way. Signup and view all the answers

What example is provided in the text to illustrate the impact of complexity?

Forgetting to add a preprocessing step during model deployment. Signup and view all the answers

Why is it challenging to keep all details in mind for a growing project?

As the project grows, it becomes harder to keep track of all the details simultaneously. Signup and view all the answers

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Study Notes

The Importance of Good Code in Data Science

Data science relies heavily on coding and computation skills to build machine learning systems, explore and visualize data, and conduct statistical analysis.
Writing robust, reproducible code is essential for data scientists to make their work efficient and maintainable.

The Problem of Complexity in Code

As projects grow, they tend to become more complex, making it challenging to understand and modify the code.
Complexity can be defined as anything related to the structure of a system that makes it hard to understand and modify a system.
When a system becomes complex, making changes can break unrelated parts of the system in unexpected ways.

Strategies for Managing Complexity

One key principle is the "Don't Repeat Yourself" (DRY) principle, which states that information should not be repeated in multiple places.
Avoiding repetition, making code modular, and making it readable can help reduce complexity.
Simplicity is key: making everything a little bit simpler as you go along can have huge benefits in the long run.
Borrowing strategies from software engineering, such as avoiding repetition and making code modular, can be beneficial for data scientists.

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