I'm Anushma Sharma, a data analyst passionate about transforming data into actionable insights. With a background in technical design, I bring strong analytical skills, problem-solving, and attention to detail to data-driven projects. I specialize in trend analysis, data wrangling, and creating impactful visualizations. I am proficient in SQL, Python, Tableau, and Excel. Explore my portfolio to see how I uncover insights and drive informed decision-making.
A comprehensive data dashboard analyzing New York Citi Bike trips for 2022.
This project analyzes CitiBike trips in 2022, exploring weather impact, popular stations, trip routes, and user behaviour to provide strategic recommendations. Skills include Python data processing, trend analysis, and interactive Streamlit dashboards.
The analysis revealed that warmer weather significantly increases bike usage, with peak ridership during spring and summer. The top 20 stations are concentrated in business districts and tourist areas, indicating high demand. Subscribers show more consistent usage patterns compared to casual riders, who tend to use bikes for leisure.
It aims to uncover patterns in customer preferences and engagement.
This project analyzes Bengaluru's restaurant performance, focusing on customer ratings, pricing, cuisine trends, and location insights to help Zomato and restaurant owners enhance satisfaction and marketing strategies. Key skills include data analysis with Python, visualization in Tableau, and machine learning for prediction and clustering.
The analysis showed a moderate negative correlation between restaurant ratings and cost, indicating that higher-priced restaurants often receive lower ratings. Restaurants with moderate pricing (₹500–₹1500 for two) attract the most customer engagement. High-rated restaurants are concentrated in areas like Koramangala and Shantala Nagar.
Strategic marketing plan for the grocery shopping app.
This project analyzes Instacart's sales data to uncover customer purchasing patterns and optimize marketing strategies through improved segmentation. Key skills include data wrangling, merging datasets, trend analysis, and visualization using Python and Jupyter Notebook.
The analysis revealed that weekends (Saturday and Sunday) are the busiest days, with peak shopping times between 9 AM and 4 PM. The top-selling departments are produce, dairy, snacks, and beverages. Middle-aged, high-income parents are the primary demographic, suggesting a focus on family-oriented discounts.
Insights to optimize content, revenue, and market strategy for online growth.
Rockbuster Stealth LLC is shifting from physical to online movie rentals to compete with streaming platforms. This project analyzes customer, movie, and payment data using SQL to optimize content offerings, pricing, and market strategy. Key skills include calculating revenue metrics, visualizing insights in Tableau, and developing data-driven recommendations for online expansion.
The analysis showed that Asia is the top revenue-generating region, contributing 43.79% of total revenue. Sports, Sci-Fi, and Animation are the most popular genres. Recommendations include expanding high-demand genres and tailoring content for top markets like Asia.
Analysis of staffing requirements for healthcare facilities.
This project analyzes the impact of influenza on vulnerable populations (under 5 and over 65) compared to non-vulnerable groups (ages 5-65) across U.S. states to support effective resource allocation and staffing during flu season. By examining death rate patterns using CDC and U.S. Census Bureau data (2009-2017), it aims to inform staffing needs, resource distribution, and management strategies through statistical analysis, data cleaning, integration, and visualizations.
The analysis revealed a strong positive correlation (R² = 0.84) between deaths in vulnerable and non-vulnerable populations. States in the Midwest and Southeast are particularly affected, with higher death counts among vulnerable groups (under 5 and over 65 years old). Recommendations include prioritizing high-death states and boosting preventive efforts.
Analyzed global sales trends and popular game genres, providing marketing recommendations for regional optimization.
This project analyzes global sales trends, popular game genres, and leading publishers to drive game development and optimize marketing investments for GameCo, an emerging video game company. By organizing and filtering data in Excel, performing descriptive analysis, cleaning data for consistency, and creating engaging visualizations, the project aims to uncover insights that will inform strategic planning and help GameCo stay competitive in the market.
The analysis showed that North American sales peaked in 2006-2009, while Europe and Japan saw steady declines post 2010. Shooter games are the most popular in North America while role-playing games dominate in Japan. Recommendations include focusing on high-demand genres and tailoring marketing strategies for each region.