Pujeeta Singh

Results-driven Software Engineer with a robust background in Java development, API creation, and comprehensive testing. Eager to transition into the dynamic field of Data Analytics, leveraging a solid foundation in programming and a commitment to continuous learning. Proven expertise in problem-solving and debugging, with a focus on data-driven decision-making. Currently enhancing skills through targeted courses in data analytics, with hands-on experience in data cleaning, visualization, and statistical analysis using tools like Python, SQL, and Tableau. A detail-oriented professional with a passion for uncovering actionable insights from complex datasets. Excited to apply a unique blend of software engineering and analytical thinking to contribute meaningfully to data-driven decision-making processes.

• Foundations: Data, Data, Everywhere
• Ask Questions to Make Data-Driven Decisions
• Prepare Data for Exploration
• Process Data from Dirty to Clean
• Analyze Data to Answer Questions
• Share Data Through the Art of Visualization
• Foundations of Data Science
• Get Started with Python
• The Power of Statistics
• Go Beyond the Numbers: Translate Data into Insights

Led comprehensive analysis of customer shopping trends with a dataset of 3900 entries. Uncovered insights through Exploratory Data Analysis (EDA): identified predominant male customers (68%), highlighted top-spending age group (36-55), and showcased popular items and seasonal trends. Analyzed payment methods, emphasizing PayPal and Credit Card. Delivered actionable insights for targeted marketing and inventory optimization. Demonstrates proficiency in data interpretation and strategic decision-making.

Analyzed a housing dataset with 50,000 entries using Python's Pandas and Seaborn libraries. Explored data integrity, handling negative values, and group trends. Discovered key insights, including predominant male customers, popular categories, and high-value age groups. Eliminated negative price entries and visualized price distribution by neighborhood. Calculated and visualized price per square foot. Explored urban data, creating a line chart depicting average price per square foot fluctuation every decade. This project showcases data cleaning, exploratory analysis, and visualization skills for effective housing market insights.