Title: Exploring Global Terrorism Data: A Data Visualization Capstone Project
Introduction: Terrorism is a global issue that affects countries worldwide, causing loss of life and property damage. In this data visualization capstone project, we will explore global terrorism data from 1970 to 2017 to understand the trends, patterns, and characteristics of terrorism worldwide. We will use Pandas, NumPy, and data wrangling techniques to clean, manipulate, and analyze the data, and we will use data visualization tools such as Matplotlib and Seaborn to create interactive and informative visualizations.
Data: The data for this project is the Global Terrorism Database (GTD), a comprehensive database of terrorist attacks worldwide. The GTD contains information on more than 180,000 terrorist attacks from 1970 to 2017, including the date, location, type of attack, number of casualties, and other details.
Objectives: The main objectives of this project are:
To clean and preprocess the GTD data using Pandas and NumPy.
To explore the trends and patterns of terrorism worldwide, such as the number of attacks and casualties over time, the most common types of attacks, and the most affected countries and regions.
To analyze the characteristics of terrorism, such as the target types, the weapon types, and the group responsible for the attacks.
To create interactive and informative visualizations using Matplotlib and Seaborn to communicate the insights gained from the analysis.
Methodology: The methodology for this project includes the following steps:
Data wrangling: We will use Pandas and NumPy to clean, preprocess, and transform the GTD data into a usable format. This includes handling missing values, encoding categorical variables, and aggregating the data by year, region, country, and other relevant variables.
Exploratory data analysis: We will use Pandas, NumPy, and data visualization tools such as Matplotlib and Seaborn to explore the data and gain insights into the trends and patterns of terrorism worldwide. This includes creating line plots, bar plots, heatmaps, and other visualizations to visualize the data and identify the most relevant features.
Data visualization: We will create interactive and informative visualizations using Matplotlib and Seaborn to communicate the insights gained from the analysis. This includes creating interactive dashboards, maps, and other visualizations to engage the audience and convey the key messages of the project.
Conclusion: In this data visualization capstone project, we explored global terrorism data from 1970 to 2017 using Pandas, NumPy, and data wrangling techniques. We identified the trends and patterns of terrorism worldwide and analyzed the characteristics of terrorism such as the target types, weapon types, and group responsible for the attacks. We also created interactive and informative visualizations to communicate the insights gained from the analysis. This project provides a valuable contribution to the understanding of global terrorism and can be used to inform policy and decision-making in the future.