Fall Internship with Airbus Americas

Over the course of my internship, I had the privilege of working on a captivating newcomer survey project utilizing Python, which proved to be a transformative journey encompassing diverse aspects of data analysis.

In the project's initial phase, my focus was on meticulous data preprocessing. Employing Python alongside the Pandas library, I imported survey data and navigated the complexities of missing values, duplicates, and inconsistent data types. This process was not only foundational but also affirmed my coding proficiency in Python for effective data manipulation. The outcome was a well-structured survey dataset that laid the groundwork for subsequent analyses.

Moving forward, the second phase involved exploratory data analysis, where I crafted fundamental visualizations such as bar charts, histograms, and box plots for outlier detection. Regression analysis was employed to unveil patterns within the data, and the insights derived were documented through summary statistics and an updated table featuring a Confusion Matrix. This phase highlighted my capability to glean meaningful information from raw data and showcased my skills in delivering clear and concise visualizations.

As the project advanced, I delved into more intricate analyses, specifically focusing on sentiment analysis and Natural Language Processing (NLP). Sentiment aggregation, model evaluations, and the application of NLP tools like Blob, Vader, and LSTM models were central to this phase. The resulting visualizations, including density plots, pie charts illustrating sentiment percentages, and model comparisons, underscored my ability to tackle complex tasks and deliver impactful insights. Fine-tuning the entire codebase was a challenging yet rewarding aspect, requiring a dedicated effort spanning 3-4 days.

In the final phase, the project reached an advanced stage with in-depth data analysis. This encompassed feature extraction, cumulative frequency plots, word clouds, and visualization of collocations. A strategic comparison between my training models and existing ones was executed, culminating in a comprehensive model usage strategy. The deliverables included visually appealing representations of advanced data analyses, feature matrices, and a well-defined model strategy.

This internship was a holistic learning experience, enhancing my Python coding skills, honing my ability to derive insights from data, and providing hands-on experience with machine learning models. The journey not only contributed significantly to my professional growth but also deepened my passion for data analysis.

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