April 10, 2025 2 min to read

(ENG) Corporate Analysis, Data Team Week 4

Written by CA-Data Team

📌 Topic Selection and Data Collection Planning

The CA Data Team continued its focus on the Environmental aspect of ESG, specifically Scope 1 carbon emissions. Each member emailed 10 companies to request data, but most were denied due to sensitivity. As a result, the team pivoted to practice data analysis using a Netflix dataset from Kaggle.

📊 Kaggle Data Practice: Netflix Dataset Analysis

✅ Goal: Each member developed a hypothesis and practiced Excel + Power Query analysis

Jihee Lee

• Hypothesis: Most Japanese movies fall under horror/thriller/crime genres
• Used pivot table with Japan filtered; analyzed genre frequency

• Result: Only 21 out of 245 Japanese contents were horror/thriller related
• Conclusion: Hypothesis disproven; Anime Feature was the most common genre
• Visualization confirmed the small proportion of horror genres

Choi Onhyuk

• Hypothesis: Korea’s Netflix content includes more season-based TV shows than single movies

• Used Power Query to filter South Korea → Grouped by content type

• Result: 170 TV Shows vs. 61 Movies → Hypothesis confirmed
• SQL query included for future expansion

SELECT type, COUNT(*) FROM netflix_titles WHERE country LIKE '%South Korea%' GROUP BY type;

Chaewon Jung

• Hypothesis: Crime is the most common genre among US TV Shows
• Filtered for TV Show + United States; split genres using delimiter

• Result: Kids’ TV was most common → Hypothesis disproven

Eunjae Kim

• Hypothesis: In 2020 (COVID era), there were more TV Shows than Movies
• Extracted year from date_added → Created pivot table

• Result: In 2020, Movies outnumbered TV Shows 2:1 → Hypothesis disproven
• Trend from 2018–2021 showed relative increase in TV Show share during COVID years

Woojin Shin

• Hypothesis: Specific countries tend to produce content concentrated in specific genres
• Used Power Query + Pivot table to analyze genre distribution by country
• Excel file attached for reference

Technical Skill Development

• Completed up to Day 2 of Power Query tutorials
• Enhanced analytical thinking through structured Kaggle exercises

Next Steps

• Finish Power Query Day 3
• Try more advanced analysis cases on Kaggle
• Discuss the possibility of shifting the project to a general data analysis theme (if ESG data remains limited)
• Each member will research and propose potential new topics of interest for future analysis