# Part 3 - Data Analysis

# Group 1

1. **Data Cleaning and Transformation**
- What does it mean to you to "clean" data, and where do you put this data?
-
- How do you incorporate and document manual steps?

2. **Analyses**
- What are some challenges you've faced, or foresee facing, when it comes to documenting how you've analyzed data?
- To what extent does a tool, such as Git and GitHub, address these challenges?
- What are some pros/cons of sharing your analysis code?

# Group 2

1. **Data Cleaning and Transformation**
- What does it mean to you to "clean" data, and where do you put this data?
- addressing missing data
- correct format - string or numeric data
- outliers
- dealing with image dataset - filtering anything that is not trustable
- metadata

- raw data and clean data in the different folder-
- what data is raw data?
- How do you incorporate and document manual steps?
- comments
- write codes for the future us
- tracing
- version control
-

2. **Analyses**
- What are some challenges you've faced, or foresee facing, when it comes to documenting how you've analyzed data?
- To what extent does a tool, such as Git and GitHub, address these challenges?
- What are some pros/cons of sharing your analysis code?
- Cons
- The code works for me but not for others
- Setting up the environment is easy for me and hard for others
- python venv vs conda
- matlab is better
- Because I've shared my code, I can call it a day, you can just read the code and understand it
- Takes time/effort to comment every line of code

- Pros
- I can see what my teammate has done and understand more about the project
- Papers that share code help me to build my own research on top of it
- Consistency of naming strategies
- Forces you to clean up your processes
- You want to make it as painless as possible for the next person

# Group 3

1. **Data Cleaning and Transformation**
- What does it mean to you to "clean" data, and where do you put this data?
- How do you incorporate and document manual steps?

2. **Analyses**
- What are some challenges you've faced, or foresee facing, when it comes to documenting how you've analyzed data?
- To what extent does a tool, such as Git and GitHub, address these challenges?
- What are some pros/cons of sharing your analysis code?

# Group 4

1. **Data Cleaning and Transformation**
- What does it mean to you to "clean" data, and where do you put this data?
- How do you incorporate and document manual steps?

2. **Analyses**
- What are some challenges you've faced, or foresee facing, when it comes to documenting how you've analyzed data?
- To what extent does a tool, such as Git and GitHub, address these challenges?
- What are some pros/cons of sharing your analysis code?

# Group 5

1. **Data Cleaning and Transformation**
- What does it mean to you to "clean" data, and where do you put this data?
- How do you incorporate and document manual steps?

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2. **Analyses**
- What are some challenges you've faced, or foresee facing, when it comes to documenting how you've analyzed data?
- To what extent does a tool, such as Git and GitHub, address these challenges?
- What are some pros/cons of sharing your analysis code?