Data Science

Skills Learned: Data cleaning, preprocessing, exploratory data analysis (EDA), and visualization using matplotlib and seaborn.

Data Science Projects and Experiments

This page showcases a variety of data science projects and learning experiments I’ve undertaken to enhance my skills in data analysis, visualization, and Python programming. Each project represents a step towards mastering different tools and techniques in the field of data science.

Project Highlights

1. Google Play Store Apps & Reviews Analysis

  • Overview: Analyzed Google Play Store app data to gain insights into ratings, sizes, reviews, and revenue estimates.
  • Skills Learned: Data cleaning, preprocessing, exploratory data analysis (EDA), and visualization using matplotlib and seaborn.

2. Data Exploration of Salaries by College Major

  • Overview: Explored a dataset on salaries by college major to understand trends and insights.
  • Skills Learned: Data manipulation, handling missing data, basic statistical analysis, and Pandas operations for summarization.
  • Overview: Visualized programming language popularity trends using historical data.
  • Skills Learned: Plotting with matplotlib, creating informative charts, and data interpretation.
  • Overview: Analyzed trends related to Bitcoin, TESLA, and unemployment benefits using Google Trends data.
  • Skills Learned: Time series data analysis, correlation analysis between different trends, and insightful visualization techniques.

5. LEGO Data Analysis

  • Overview: Analyzed LEGO datasets to understand themes and sets.
  • Skills Learned: Data aggregation, merging datasets, visualizing hierarchical data structures, and insights into product trends.

6. NumPy & N-dimensional Array Operations

  • Overview: Demonstrated practical usage of NumPy for efficient computation with n-dimensional arrays.
  • Skills Learned: Efficient computation with NumPy arrays, basic image manipulation with NumPy, broadcasting, and vectorization techniques.