These projects encompass my first year of Data Analytics at UPenn.
Overall, the projects were designed to test several key skills and concepts related to data analysis and visualization. Overall, they covered these key concepts in R:
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Data Preparation and Cleaning:
- Skill Tested: Ability to clean and preprocess data for analysis.
- Assessment: Evaluation of the capacity to remove irrelevant information, handle missing values, and prepare the dataset for meaningful analysis.
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Exploratory Data Analysis (EDA):
- Skill Tested: Use of various visual and statistical techniques to explore data and identify patterns or trends.
- Assessment: Proficiency in using ggplot2 for different types of visualizations (e.g., scatter plots, box plots, bar charts) and interpreting the results.
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Correlation and Relationship Analysis:
- Skill Tested: Identification and explanation of relationships between variables.
- Assessment: Ability to discern and describe how different variables interact, such as the relationship between delays and cancellations or the impact of engine size on highway mileage.
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Data Interpretation and Conclusion Drawing:
- Skill Tested: Drawing meaningful conclusions from data and visualizations.
- Assessment: Competence in interpreting the results of analyses and providing reasoned conclusions about the data.
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Practical Application of Statistical Concepts:
- Skill Tested: Application of statistical principles and data analysis techniques to real-world datasets.
- Assessment: Understanding of how to apply concepts like averages, proportions, and correlations to practical scenarios.
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Visual Communication:
- Skill Tested: Effective communication of findings through visualizations.
- Assessment: Ability to create clear, informative charts and graphs that accurately represent the data and support conclusions.