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Hello! I'm Nainika Singh, a results-driven Data Analyst with 2 years of experience in delivering actionable insights through data analysis, visualization, and business intelligence. Proven ability to transform raw data into strategic insights that improve decision-making and drive business growth. Proficient in Python, SQL, Power BI, Looker Studio, and advanced Excel techniques. Skilled in statistical analysis, trend forecasting, data mining, and data visualization. Excellent problem-solving abilities and a strong understanding of business operations. Seeking to leverage analytical skills and knowledge in data science to drive impactful business outcomes.
A Python-based music generator using TensorFlow, Keras, Magenta and music21.
Developed a system to send personalized messages on WhatsApp using Python and MySQL.
A Python-based music generator using TensorFlow and Magenta.
Automated the process of competitor analysis using BeautifulSoup, extracting real-time data from websites and generating reports in Excel.
Created a model using Python and SPSS to predict customer churn based on historical data. The model helped reduce churn by 15%.
Developed a real-time Power BI dashboard to track company-wide metrics, improving operational efficiency by 20%.
Led the development of a performance dashboard to monitor key business metrics, improving decision-making efficiency by 20%. Designed and built the entire website for vintagevoguecarpets.com:
Frontend: Tailwind CSS, HTML, JavaScript.
Backend: Node.js, MySQL for database management.
Automated web scraping for competitor analysis and pricing strategies using BeautifulSoup and Selenium.
Built data pipelines and integrated APIs like OpenAI for predictive business insights and automated processes.
Key Achievements:
• Created automated solutions to scrape and analyze market data, contributing to a 15% increase in client acquisition.
• Developed responsive and user-friendly web applications that enhanced customer engagement.
Conducted data visualization, trendline analysis, and statistical forecasting using Power BI and SPSS to support business growth strategies.
Provided market research insights, leading to improved targeting of customers and a 10% rise in subscriptions.
Built models to predict customer churn, helping reduce churn rate by 15% through actionable insights.
Key Achievements:
• Developed predictive analytics models to improve customer retention strategies.
• Successfully visualized complex datasets into actionable insights for senior management.