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Data Science with less than a year in Python, SQL, and Machine Learning
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Assessing your cultural and operational fit
Motivated and detail-oriented B.Tech graduate in Electronics and Communication Engineering with strong skills in Python, SQL, Power BI, Tableau, and R. Hands-on experience in data analytics, machine learning, data visualization, and predictive modeling through internship and academic projects. Strong analytical thinking and problem-solving abilities, eager to contribute to data-driven decision-making in a dynamic organization.
Jawaharlal Nehru Technological University, Kakinada
Bachelor of Technology · Electronics and Communication Engineering
February 1, 2021 – May 1, 2024
Sir C R Reddy Polytechnic
Diploma · Electronics and Communication Engineering
April 1, 2016 – February 1, 2021
Z.P. High School (SSC)
SSC
N/A – May 31, 2016
Sales Dashboard & Forecasting using Power BI
June 1, 2024 – June 1, 2026
Designed an interactive Sales Dashboard using Microsoft Power BI. Cleaned and transformed raw sales data using Power Query. Created KPIs including Total Sales, Profit, Quantity, and Growth %. Built visualizations such as bar charts, line charts, pie charts, and slicers. Implemented sales forecasting using time-series analysis. Generated insights to support business decision-making.
Customer Segmentation using K-Means Clustering
June 1, 2024 – June 1, 2026
Developed a customer segmentation model using Python. Implemented the K-Means clustering algorithm to group customers based on spending behavior. Performed data preprocessing and feature scaling for improved model accuracy. Used pandas, NumPy, scikit-learn, matplotlib, and seaborn for data analysis and visualization. Visualized cluster insights to support targeted marketing strategies.
Sales & Product Data Analysis using SQL
June 1, 2024 – June 1, 2026
Designed and analyzed relational database tables: Sales (transactional data) and Products (reference data). Queried fields such as quantity sold, sale date, total price, product name, category, and unit price. Performed JOIN operations to link sales transactions with product details. Used GROUP BY, HAVING, and aggregate functions (SUM, COUNT, AVG) to calculate total revenue and product-wise performance. Analyzed monthly sales trends and category-wise revenue insights. Generated business insights to support pricing and sales strategy decisions.
TATA GenAI Powered Data Analytics
Forage
June 1, 2026 – Present
Python for Data Analysis
Great Learning
June 1, 2026 – Present
MySQL
Great Learning
June 1, 2026 – Present
Data Analytics
AWS Academy
June 1, 2026 – Present
Introduction to Strategy Consulting BCG
Forage
June 1, 2026 – Present
Foundations of Data Visualisation using Tableau
Great Learning
June 1, 2026 – Present
Data Visualisation With Power BI
Great Learning
June 1, 2026 – Present
TATA Data Visualisation: Empowering Business with Effective Insights | Virtual Experience
Forage
June 1, 2026 – Present
Data Analytics Essentials
Cisco Networking Academy
June 1, 2024 – Present
Cultural Fit Analysis
The candidate's academic projects and certifications show a strong interest and foundational knowledge in the Data Science domain, aligning well with the target role. The diversity of tools and techniques (SQL, Power BI, Python, ML algorithms) indicates a broad learning approach. The academic nature of all projects suggests a learning-oriented individual, which can be a good cultural fit for organizations that value continuous development. However, the lack of professional experience means their adaptability and performance under real-world business pressures are unknown.
Soft Skills & Operational Fit
The candidate's resume highlights analytical thinking, problem-solving, and teamwork as soft skills. The academic projects demonstrate an ability to work through structured data analysis tasks. The internship experience mentions collaboration and being recognized as sincere and hardworking, suggesting a positive operational fit within a team environment. However, without direct work experience, the application of these soft skills in a professional, fast-paced setting is yet to be fully validated.