Skip to content

This repo contains all projects from my second data class at UPenn, designed to demonstrate marked improvements and a broader grasp of key topics through more complex problem sets.

Notifications You must be signed in to change notification settings

ashemsu/intermediate-data-skills-R

Repository files navigation

Summary of Projects

This repo contains projects from my second Data Analytics courses, designed to demonstrate marked improvements and a broader grasp of key topics through more complex problem sets.

Project Summaries

Project 1: Analysis of Voting Rights Restoration for Former Felons

Focus:

  • Investigated the impact of felony convictions and incarceration on voter registration and turnout.
  • Evaluated the effect of informing former felons about restored voting rights on their likelihood to participate in elections.

Skills and Techniques:

  • Data manipulation and cleaning with R (e.g., subsetting data, creating new variables).
  • Statistical analysis (t-tests, linear regression) to assess treatment effects and balance between groups.
  • Understanding experimental design, including treatment and control conditions and randomization.

Key Takeaways:

  • Demonstrated how targeted interventions can influence voter participation among specific populations.
  • Emphasized the importance of experimental design and statistical testing in drawing causal conclusions.

Project 2: Population Density and Revenue Analysis

Focus:

  • Analyzed the relationship between state population density and total revenue across the United States.
  • Investigated correlations and trends using economic and demographic data.

Skills and Techniques:

  • Data wrangling and merging (e.g., combining datasets, calculating new variables).
  • Aggregation of data to summarize state-level revenue.
  • Data visualization using ggplot2 to explore and present relationships between variables.

Key Takeaways:

  • Explored the complex relationship between population density and revenue, highlighting the influence of sector-specific factors.
  • Gained experience in handling and visualizing large datasets to communicate insights effectively.

Project 3: Survey Data Analysis on Political Attitudes

Focus:

  • Analyzed survey data to examine political attitudes, including voter preferences and feelings towards the federal government.
  • Compared attitudes between different political affiliations.

Skills and Techniques:

  • Data cleaning and subsetting (e.g., filtering survey responses, creating new variables).
  • Calculation of descriptive statistics and differences in means.
  • Regression analysis to explore relationships between political attitudes and survey responses.

Key Takeaways:

  • Provided insights into how political affiliation influences attitudes towards government.
  • Developed skills in cleaning, analyzing, and interpreting survey data to understand public opinion.

Project 4: Analysis of State and Federal Election Data

Focus:

  • Investigated the impact of electoral conditions and voter registration data on election outcomes.
  • Analyzed how various factors, such as eligibility information, affect voter turnout and registration rates.

Skills and Techniques:

  • Applied data manipulation and analysis to election data using R.
  • Conducted regression analyses to understand the effects of different variables on election outcomes.
  • Interpreted statistical results to evaluate the effectiveness of electoral interventions.

Key Takeaways:

  • Demonstrated ability to analyze complex election data and assess the impact of interventions on voter behavior.
  • Highlighted the importance of understanding voter eligibility and registration in electoral studies.

Overall Skills Demonstrated

Across these projects, you have demonstrated proficiency in:

  • Data Manipulation: Cleaning, subsetting, and merging datasets to prepare data for analysis.
  • Statistical Analysis: Applying statistical tests (t-tests, regression) to analyze and interpret experimental and survey data.
  • Experimental Design: Understanding and applying principles of experimental design, including treatment and control groups.
  • Data Visualization: Creating effective visualizations using tools like ggplot2 to communicate insights and trends.
  • Analytical Thinking: Interpreting complex data to draw meaningful conclusions and provide actionable insights.

These projects showcase an ability to handle diverse datasets, apply statistical and analytical techniques, and communicate findings effectively.

Releases

No releases published

Packages

No packages published