I'm glad you're here! Explore my work and skills below.
I’m Nuriddin Abdussalaam, an aspiring data analyst with a strong foundation in Python, SQL, and statistics. With hands-on experience from real-world projects and certifications from Google and TripleTen, I’m passionate about turning raw data into actionable insights that drive better decision-making. I’m currently seeking opportunities where I can grow my skills, contribute to impactful analytics work, and solve meaningful business problems.
Detail-oriented and business-savvy data analyst with a background in operations and quality assurance. Passionate about solving real-world problems through data storytelling, pattern recognition, and process improvement. Skilled in problem-solving, data cleaning, and interpreting complex data to support process improvement.
Contact Me:Tools: Python, pandas, matplotlib, data visualization, EDA
Data: A modified version of Instacart’s grocery order data, including orders, products, departments, and customer behavior.
Goal: Understand shopping habits by time, product, and customer reordering patterns.
Challenges: Missing and duplicate values were introduced intentionally; datasets required merging and cleaning across multiple tables.
Solutions & Methods: Cleaned and joined datasets using pandas; handled missing values and duplicates; verified distributions in key time-based fields; created histograms and bar charts to show customer behavior.
Insights: Identified top 20 most ordered and reordered products; discovered that weekends and late mornings saw peak activity; analyzed typical time between orders and items per order.
Tools: Hypothesis testing, Python, statistical analysis, pandas, scipy
Data: Monthly usage and revenue data for customers subscribed to two different phone plans (Surf and Ultimate).
Goal: Identify which plan is more profitable and whether location impacts revenue.
Challenges: Revenue had to be calculated from raw usage using plan pricing rules; some entries contained $0 revenue and required validation.
Solutions & Methods: Cleaned data, calculated monthly revenue per user, segmented by region and plan, performed independent t-tests to compare average revenue.
Insights: Found statistically significant differences in revenue between plans; users in NY/NJ showed distinct usage patterns that influenced profitability.
Tools: pandas, data wrangling, visualization
Data: Dataset of streaming movies and TV shows including genre, rating, platform, and release year.
Goal: Explore trends in content availability, genre distribution, and release patterns.
Challenges: Columns contained inconsistencies in genres and platform labels; some values missing or misformatted.
Solutions & Methods: Cleaned and standardized genre/platform columns, grouped and visualized data using bar charts and value counts.
Insights: Identified most common genres across platforms, content concentration trends by year, and potential market gaps in content types.