I’m a data analyst with a strong background in operations and leadership, transitioning into data science through TripleTen.
I specialize in analyzing complex datasets, building predictive models, and uncovering insights that drive real business decisions. My experience managing high volume operations has shaped my ability to think critically, solve problems under pressure, and communicate insights clearly to stakeholders.
• Data analysis & forecasting (Python, Pandas, SQL) • Machine learning & predictive modeling (Scikit-learn) • Business-focused insights from real-world datasets • Strong leadership & communication from 10+ years in management
I’m passionate about using data to improve decision making in industries like sports, retail, and telecom.
• 🎓 TripleTen Data Science Graduate
• 📊 Experienced in EDA, forecasting, A/B testing, and visualization
• 💬 Strong communication + leadership background (10+ years)
• 🎮 Passionate about analytics in gaming, telecom, sports, and retail
• Improving machine learning model performance and evaluation techniques • Building end-to-end data projects with real-world business scenarios • Expanding SQL and data visualization skills
• Data Analysis: Pandas, NumPy, SQL • Visualization: Matplotlib, Seaborn, Tableau • Machine Learning: Scikit-learn • Tools: Git, Jupyter Notebook
• Analyzed game release trends over time, identifying a peak in industry activity around 2008–2009 followed by a market decline. • Evaluated platform performance across years, uncovering platform life cycles and periods of dominance. • Aggregated total sales across regions to create a global sales metric for more accurate analysis. • Identified data quality issues including missing values, inconsistent formatting, and placeholder entries (e.g., "TBD"). • Cleaned and standardized dataset to improve reliability of analysis and modeling. • Explored relationships between release timing, platform success, and total sales performance. 🔗 Repo: Video-Game-Sales-Forecasting
• The video game market experienced rapid growth from the late 1990s to mid-2000s, peaking around 2008–2009, followed by a noticeable decline. This suggests a market saturation point and highlights the importance of timing when entering or investing in the gaming industry. • Certain platforms dominate sales during specific time periods, indicating strong product life cycles. For example, platforms like Wii and DS generated significantly higher sales during peak years, reinforcing the importance of platform selection in maximizing game success. • Regional sales differences and genre popularity suggest that game success is not universal across markets. Tailoring game releases based on region-specific preferences can significantly improve overall performance and revenue.
Focus on launching games during rising market trends and prioritize high-performing platforms during their peak lifecycle. Additionally, adapting game genres and marketing strategies based on regional preferences can maximize global sales performance.
• Merged and transformed multiple datasets (calls, messages, internet usage) into a unified user level dataset for analysis. • Engineered features to calculate monthly revenue per user based on complex pricing rules and overage conditions. • Conducted exploratory data analysis to identify behavioral differences between customer segments across service usage. • Performed statistical hypothesis testing (two-sample t-test) to validate revenue differences between plans (p < 0.01). • Identified key revenue drivers, including internet usage and overage frequency, impacting profitability. • Segmented users based on usage behavior to uncover high value customer groups. • Translated analytical findings into actionable business recommendations to increase revenue and improve customer retention. 🔗 Repo: Megaline-Telecom-Analysis
• Cleaned and merged multiple datasets to create a unified view of customer orders. • Analyzed purchasing patterns to identify peak ordering times and customer habits. • Investigated product popularity and reorder frequency to uncover buying behavior. • Identified top categories and products driving repeat purchases. • Visualized trends using Matplotlib/Seaborn to communicate insights effectively. • Explored customer segmentation based on ordering frequency and basket size. • Generated insights to support marketing strategies and inventory optimization. 🔗 Repo: Instacart-EDA-Analysis
• Cleaned and processed dataset containing ratings, votes, and genre information. • Analyzed relationship between vote count and average ratings to measure engagement. • Explored genre based performance to identify audience preferences. • Identified trends in highly rated vs highly popular shows. • Visualized correlations and distributions to support findings. • Provided insights on factors influencing viewer engagement and content success. 🔗 Repo: IMDb-TV-Show-Analysis
🚀 Let’s connect!
I’m actively seeking Data Analyst / Data Scientist opportunities where I can apply my skills to real world business problems.
📩 Open to networking, collaborations, and opportunities.
