Aspiring Data Analyst | SQL | Python | Power BI

Hyderabad

Bachelor of Technology, Computer science and engineering
amity University
Join Anusha and 10k+ other professionals on
Tal is the app where bosses meet engineers & builders directly, without middlemen
Download Tal BossAspiring Data Analyst | SQL | Python | Power BI
A Computer Science student at Amity University, Gwalior with a specialization in Data Analytics. Hands-on experience in predictive modelling, KPI development, monitoring, and data-driven analysis to support effective decision-making. Worked on in areas such as Predictive Modelling, Data Analysis, and Business Intelligence. Skilled in building and monitoring Key Performance Indicators (KPIs), performing data analysis, and generating actionable recommendations to support data-driven decision-making. Focused on ensuring data accuracy, consistency, and delivering quality insights through structured analysis and visualization. Experience in working with Machine Learning techniques including Linear Regression, Logistic Regression, Decision Trees, Clustering, and Time Series Forecasting. Familiar with data preprocessing, exploratory data analysis (EDA), and model building. Proficient in tools and technologies such as Python, SQL, Advanced Excel, and data visualization tools like Tableau/Power BI. Basic understanding of data handling frameworks and analytical workflows. Strong analytical mindset with the ability to interpret data, identify patterns, and contribute to solving business problems. Eager to apply analytical skills in real-world scenarios and grow in the field of Data Analytics and Data Science.
Greater Gwalior Area
Data Analyst Intern at Defence Research and Development Organisation (DRDO), Aug 2025 - Sep 2025 · 2 mos, Hyderabad, 1. Used Jupyter Notebook and Pandas to analyze telecom call data and find network issues causing call drops, especially in rural areas. 2. Cleaned raw call log data by fixing missing values and organizing it into proper columns for better analysis. 3. Created graphs using Matplotlib and Seaborn to understand calling patterns, peak hours, and unusual trends. 4. Found that people in rural areas talk for longer durations (up to 30 minutes), which overloads towers and causes failures, and suggested improving signal distribution to fix this.
Bachelor of Technology, Computer science and engineering, amity University