ArefMilani/python_nobel_winners_analysis

This project aims to analyze Nobel Prize winners across various categories, exploring trends such as gender distribution, age, countries, and other factors that may have influenced Nobel Prize awards over time.

Python Data Analysis Project: Nobel Prize Winners

Project Objective

This project aims to analyze Nobel Prize winners across various categories, exploring trends such as gender distribution, age, countries, and other factors that may have influenced Nobel Prize awards over time.

Dataset

The dataset used in this project is a collection of Nobel Prize winners, which includes details like:

• Award categories (e.g., Chemistry, Physics, Medicine, etc.)
• Year of the award
• Country of the winners
• Gender and age of the recipients
• Additional relevant data

Project Questions

1. Identify the top 10 countries with the most Nobel Prize winners.
2. List the first women to win Nobel Prizes in each category.
3. List the first men to win Nobel Prizes in each category.
4. Visualize the dominance of the country with the most Nobel Prize winners over time and provide insights into this trend.
5. Visualize Nobel Prize winners by gender, age, category, and year, and provide insights based on these visualizations.
6. Visualize the categories and countries of Nobel Prize winners between 1938 and 1945 and provide insights.
7. Visualize the categories and countries of Nobel Prize winners between 1947 and 1991 and provide insights.
8. Visualize the countries and ages of Nobel Prize winners after 2000 in the Chemistry, Literature, Peace, Physics, and Medicine categories, and provide insights.

Methods Used

1. Pandas and NumPy:

   • Used for data manipulation and preprocessing.
   • Data cleaning, handling missing values, and filtering based on project requirements.

2. Data Visualization (Seaborn and Matplotlib):

   • Generated plots to visualize gender, age, category distribution, and country dominance in Nobel Prize awards.
   • Used line plots, bar charts, and histograms to highlight trends and provide insights into the data.

3. Exploratory Data Analysis (EDA):

   • Explored and summarized the Nobel Prize dataset to find patterns and anomalies.
   • Identified historical trends in the Nobel Prize distribution.

4. Time Series Analysis:

   • Analyzed the dominance of certain countries over time, especially the country with the most Nobel Prize winners.
   • Focused on trends during significant historical periods, such as World War II and the Cold War era.

5. Categorical Analysis:

   • Visualized and analyzed Nobel Prize categories over specific time periods (e.g., 1938-1945, 1947-1991, post-2000).

Key Insights

1. The United States has dominated Nobel Prize awards, especially in the 20th century, with significant contributions in science-related categories.
2. Women are underrepresented in the Nobel Prize list, but the trend is slowly improving, particularly in the Peace and Literature categories.
3. During and after World War II (1938-1945), certain countries and categories had more significant representations.
4. There is a notable age range for Nobel Prize winners, with most recipients being older, especially in science-related categories.
5. After 2000, the countries with the most Nobel Prize winners have remained relatively consistent, with a slight increase in diversity.

Conclusion

The project provides valuable insights into the distribution of Nobel Prize winners by category, country, and gender over time. It highlights historical trends and uncovers factors such as global events and academic influence that have shaped Nobel Prize distribution. This analysis can help understand the factors contributing to academic and scientific achievements on a global scale and inform future trends in awarding prestigious prizes like the Nobel Prize.