Data Analytics for Finance Portfolio

Course Data Analytics for Finance I&II (MSc) during Exchange semester

USI, Switzerland

Introduction

This portfolio presents a collection of data visualizations and analyses crafted by me as part of the Data Analytics for Finance MSc class during exchange semester. The work demonstrates a deep understanding of data visualization principles, the effective use of Python and Matplotlib, and a keen insight into financial and economic data trends.

Projects Overview

1. Misleading Data Visualizations

• Objective: Identify and correct misleading aspects of a public health data visualization.
• Tools: Python, Matplotlib.
• Findings: Adjusting the arrangement of dates revealed a more accurate trend in COVID-19 cases, demonstrating the importance of chronological integrity in data presentation.

2. The History of Pandemics

• Objective: Create an engaging infographic to communicate complex epidemiological data.
• Tools: Visual Capitalist.
• Findings: Employing 3D spheres and vivid colors, this visualization effectively communicates the magnitude of various pandemics, highlighting their historical context and impact.

3. Climate Change Visualization

• Objective: Showcase global greenhouse gas emissions over time.
• Tools: Python, Matplotlib, IMF Climate Data - GHG Emissions dataset.
• Findings: A heat map provides a clear view of emissions trends, demonstrating countries' shifts towards or away from sustainability.

4. Population Demographics

• Objective: Analyze the US population by gender and age group.
• Data Source: U.S. Census Bureau.
• Findings: A comparison of male and female populations across different age groups reveals societal norms and implications for policy and economic planning.

5. Quality of Life and GDP per Capita

• Objective: Explore the relationship between economic metrics and quality of life.
• Tools: Python, bubble plot visualization.
• Findings: While there's a correlation between GDP per capita and quality of life, exceptions highlight that economic wealth is not the sole determinant of life satisfaction.

6. Government Expenditure Analysis

• Objective: Visualize government primary expenditure as a percentage of GDP.
• Tools: IMF data, Python.
• Findings: The visualization uncovers trends in government spending, reflecting responses to global crises and fiscal policies.

7. Solar Energy Adoption

• Objective: Map the distribution of photovoltaic production facilities.
• Tools: Python, choropleth map visualization.
• Findings: Visual analysis reveals regional variations in solar energy adoption, providing insights for stakeholders in energy planning.

8. Unemployment Rates Interactive Map

• Objective: Illustrate the unemployment rates across New York State post-2008 financial crisis.
• Tools: HoloViews, Bokeh libraries, Bureau of Labor Statistics data.
• Findings: The interactive map highlights economic disparities and the varying impact of the recession across regions.

Technical Skills

• Languages: Python
• Libraries: Matplotlib, HoloViews, Bokeh
• Data Visualization: Heat maps, choropleth maps, bubble plots, radar charts
• Data Sources: IMF, Visual Capitalist, U.S. Census Bureau, Bureau of Labor Statistics, Eurostat, Gapminder

Conclusion

This portfolio reflects my capability to analyze, interpret, and visualize complex datasets in insightful and accessible ways. Through careful consideration of data integrity, visual clarity, and analytical depth, these projects offer valuable perspectives on various global and societal issues.