About me
Hello, I’m Lingabarani M G, a Data Analyst who enjoys turning data into clear insights and practical solutions. I like working hands-on with data—cleaning it, analyzing patterns, and presenting results in a way that helps people make better decisions.
I have completed multiple certifications and internships that strengthened my skills in Python, SQL, Power BI, Excel, Machine Learning, and Data Analytics. I successfully completed a Diploma in Data Analytics (Professional) and industry simulations from Tata (Forage), Deloitte, JPMorgan Chase, and One Roadmap, where I worked on real-world tasks like data analysis, risk profiling, AI-driven insights, and business reporting.
I also completed an AI/ML internship, where I worked on data-driven models, explored machine learning techniques, and applied analytical thinking to practical problems. My academic and certification projects include a Multiple Disease Prediction System, a Liver Disease Prediction model presented at a national conference, and interactive dashboards built using Power BI and SQL.
I’m a continuous learner who enjoys applying theory to real projects. I’m looking to grow as a data professional while contributing meaningful, data-backed solutions to a dynamic team.
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.
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