Python Project

This project involved a series of data analysis and visualization tasks using Python, designed to demonstrate my ability to work with diverse datasets, perform statistical modeling, and create meaningful visualizations. The project features a variety of scientific data sets, including sunspot data, sine wave interference, Scanning Tunneling Microscope (STM) measurements, and more. Each task required applying specific techniques in data manipulation, smoothing, fitting, and plotting to extract insights and highlight key patterns. The project also showcases my proficiency in Python libraries such as Matplotlib, NumPy, and SciPy, as well as my problem-solving skills in integrating theory with practical applications.

Python Project Image

Sunspot Analysis and Smoothing

  • Generated scatter plots of sunspot data, identifying key patterns and trends.
  • Highlighted the first five peaks in sunspot counts for better insight.
  • Implemented a moving average to smooth out data noise, and overlaid it with raw data, adding legends and labels for clarity.

Wave Interference Modeling

  • Created 1D plots to visualize the interference between two sine waves of different frequencies.
  • Developed a 2D interference pattern, customizing the implementation based on textbook examples.

STM Data Visualization

  • Processed Scanning Tunneling Microscope (STM) data to generate detailed heatmaps using imshow, with proper axis labels, titles, and colorbars.
  • Compared 2D STM images to 3D silicon structures by rotating a 3D model and aligning patterns for a more accurate visual match.

Histogram Analysis

  • Analyzed various datasets by generating histograms to identify underlying distributions.
  • Estimated distribution parameters and created two-dimensional histograms for more complex data shapes.

Photoelectric Effect Analysis

  • Created and saved a 2D dataset for frequency and voltage.
  • Plotted voltage vs. frequency, calculated the slope (m) and intercept (c) using textbook equations, and validated results using scipy.optimize.curve_fit.
  • Overlayed fitted functions on the data to visually compare the accuracy of the model.

COVID-19 Data Modeling

  • Visualized the progression of COVID-19 cases and deaths over time.
  • Developed exponential fits for deaths within specific time ranges, overlaying these fits on the data for better insight.
  • Combined all results into a comprehensive visualization, highlighting trends and providing a clearer picture of the pandemic's evolution.

This project required advanced data manipulation, statistical modeling, and visualization techniques, demonstrating proficiency in Python libraries like Matplotlib, NumPy, and SciPy. It showcases my ability to extract meaningful insights from complex data and integrate theoretical knowledge to create clear, visually appealing outputs.