About me
Hello! My name is Lingabarani M G, and I am a Data Analyst. I have a passion for transforming data into actionable insights and creating meaningful visualizations. With a strong foundation in statistics and data analysis, I enjoy uncovering patterns and trends that drive informed decision-making.
Over the past few months, I completed a 3-month Data Analytics certification course, where I gained hands-on experience with Python (pandas, NumPy, matplotlib), SQL, Power BI, MS Excel, and Google Colab. I worked on key projects like a Multiple Disease Prediction system (diabetes, heart disease, Parkinson’s) using Streamlit, and a Liver Disease Prediction model, which I presented at a national conference at KPR Engineering College. I also created dashboards using Power BI and SQL, and built a web-based Online Examination Portal using HTML, CSS, and MySQL. I enjoy turning data into useful insights to support better decisions. I’m always eager to learn more and contribute to a team where I can grow and make an impact.
Areas of Expertise
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Data Analysis & Insights
Proficient in analyzing structured data using Python, pandas, NumPy, and Excel to deliver actionable business insights.
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Dashboard & Reporting
Experienced in building interactive dashboards with Power BI and Excel to visualize KPIs and trends clearly.
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ML Projects & Research
Developed disease prediction models using machine learning and published research presented at national conferences.
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SQL & Database Handling
Strong command in writing SQL queries for data retrieval, transformation, and integration with reporting tools.
Technologies
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Advanced Excel
Advanced Excel plays a crucial role in data analytics projects by enabling efficient data cleaning, transformation, and analysis. It offers powerful tools like PivotTables, VLOOKUP/XLOOKUP, Power Query, and advanced charting for insightful data visualization. With Excel’s built-in functions and formulas, analysts can perform complex calculations and uncover trends. It’s especially useful for handling medium-sized datasets and presenting data-driven decisions clearly.
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Python
Python was used for data cleaning, analysis, and visualization using libraries like pandas, NumPy, and matplotlib. It enabled automation of repetitive tasks and efficient handling of large datasets. With Python, I performed statistical analysis, built data models, and generated insights to support data-driven decisions. Its flexibility and integration with other tools made it essential for end-to-end analytics workflows.
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SQL
SQL was used to extract, filter, and manipulate data from relational databases in the data analytics project. It enabled efficient querying using `SELECT`, `JOIN`, `GROUP BY`, and `WHERE` clauses to gather meaningful insights. SQL helped in transforming raw data into structured formats for analysis and reporting. It also played a key role in data validation and preparing datasets for visualization in Power BI.
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Power BI
Power BI was used to create interactive dashboards and visual reports that made complex data easy to understand. It helped connect multiple data sources, perform data transformations using Power Query, and generate real-time insights. With features like slicers, drill-through, and DAX formulas, Power BI enabled dynamic filtering and deeper data exploration. It played a key role in presenting findings clearly to stakeholders.
