Tharun Sai Vengala

Results-driven Data Analyst with a strong foundation in Python, SQL, and data visualization. Passionate about transforming raw data into actionable insights to drive business decisions. Proficient in statistical analysis, data manipulation, and dashboard creation. Seeking a dynamic role to apply analytical skills in solving real-world problems.

📜 SQL – Skill Mastery (Scaler)
📜 EDA Fundamentals – Skill Mastery (Scaler)
📜 Python Libraries – Skill Mastery (Scaler)

Conducted exploratory data analysis (EDA) on Netflix’s dataset to identify content trends across different countries.

• Used Python (Pandas, NumPy, Matplotlib, Seaborn) to clean, manipulate, and visualize data.
• Extracted insights on popular genres, directors, and actors to help optimize Netflix’s content production strategy.
• Analyzed TV ratings and release trends to determine audience preferences by region.
• Provided data-driven recommendations on content expansion strategies to improve global subscriber engagement.

• Conducted descriptive analytics on Aerofit’s customer data to identify key characteristics of treadmill buyers.
• Used Python (Pandas, NumPy, Matplotlib, Seaborn) and SQL to clean, process, and analyze the dataset.
• Created customer profiles for each treadmill product (KP281, KP481, KP781) based on demographics, income, and usage patterns.
• Built contingency tables and computed conditional & marginal probabilities to derive insights on customer preferences.
• Developed data visualizations (bar charts, heatmaps, scatter plots) to present purchase trends and fitness habits.
• Provided data-driven recommendations for product positioning and targeted marketing strategies to optimize sales.

• Conducted exploratory data analysis (EDA) on Walmart's Black Friday transactional data to analyze customer spending patterns.
• Used Python (Pandas, NumPy, Matplotlib, Seaborn) to clean data, detect null values and outliers, and visualize key insights.
• Performed gender-based spending analysis, calculating average purchase amounts for male and female customers.
• Applied Central Limit Theorem and constructed confidence intervals (90%, 95%, 99%) to estimate population spending behavior.
• Segmented customers by marital status and age groups (0-17, 18-25, 26-35, etc.) to uncover targeted marketing insights.
• Created boxplots, histograms, and distribution curves to compare spending behavior across demographics.
• Delivered data-driven recommendations for personalized promotions and strategic marketing efforts based on spending trends.

• Conducted exploratory data analysis (EDA) on Yulu’s shared electric cycle rental dataset to identify demand patterns.
• Applied bi-variate analysis, t-tests, ANOVA, and chi-square tests to assess the impact of season, weather, and working days on demand.
• Established relationships between the dependent variable (rental count) and independent variables such as temperature, humidity, and windspeed.
• Formulated hypothesis tests (Null and Alternative) and validated them using statistical methods like Levene’s test and Shapiro-Wilk test for normality and variance assumptions.
• Used Python (Pandas, NumPy, SciPy, Statsmodels, Seaborn, Matplotlib) to perform data preprocessing and hypothesis testing.
• Visualized demand trends across different seasons and weather conditions using boxplots, histograms, and Q-Q plots.
• Provided data-driven recommendations to improve Yulu’s revenue by optimizing bike placement based on high-demand factors.