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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.

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ashemsu/intermediate-data-skills-R

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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.