Data Analysis with Python, SQL & Data Visualization
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Detail-oriented Data Analyst fresher with a strong foundation in Python, SQL, and data visualization. Proficient in cleaning, transforming, and analyzing datasets using Pandas, NumPy, Matplotlib, and Seaborn to surface actionable business insights. Hands-on project experience spanning machine learning, exploratory data analysis, and customer segmentation. Eager to contribute to data-driven decision-making in a collaborative analytics environment.
Sree Vidyanikethan Engineering College, Tirupati, AP
B.Tech · Computer Science and Engineering
August 1, 2020 – June 30, 2024
Exploratory Data Analysis on Retail Sales Dataset
January 1, 2024 – December 31, 2024
Performed end-to-end EDA on a retail dataset — handled missing values, removed duplicates, and standardized formats using Pandas to ensure data quality. Identified top-performing product categories and seasonal revenue trends through statistical aggregation and correlation analysis. Designed 10+ visualizations (bar charts, heatmaps, line plots) using Matplotlib and Seaborn to communicate patterns and support business decision-making.
SMS Spam Detection using Machine Learning
January 1, 2024 – December 31, 2024
Built a binary text classifier in Python using Scikit-learn (Multinomial Naive Bayes), achieving accurate spam vs. ham classification on a real-world SMS dataset. Applied NLP preprocessing techniques including tokenization, stop-word removal, and TF-IDF vectorization to transform raw text into machine-readable features. Evaluated model performance using confusion matrix, precision, recall, and F1-score, demonstrating strong understanding of classification metrics.
Customer Segmentation using K-Means Clustering
January 1, 2024 – December 31, 2024
Implemented K-Means clustering in Python to segment customers into distinct behavioral groups based on purchase frequency, recency, and monetary value (RFM analysis). Determined optimal number of clusters using the Elbow Method, improving segmentation accuracy for targeted marketing strategy recommendations. Visualized cluster distributions using 2D scatter plots and interpreted segment characteristics to generate actionable marketing insights.
AICTE-AWS Data Engineering Virtual Internship
Amazon Web Services / AICTE
January 1, 2023 – Present
AICTE Cloud Virtual Internship
AICTE
January 1, 2023 – Present
Salesforce Developer Virtual Internship
Salesforce / AICTE
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's academic projects show a good breadth of data analysis and machine learning applications, aligning well with the problem-solving nature of a Data Analyst role. The virtual internships, while not providing direct work experience, indicate an eagerness to learn and adapt to industry tools (AWS, Salesforce). The focus on Python, SQL, and data visualization aligns with common industry requirements for data analysts. However, the lack of diverse project types (all academic) and professional experience limits the assessment of cultural fit in a corporate setting.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical thinking, written communication, and data storytelling skills, which are crucial for a Data Analyst role. Their experience with Git/GitHub indicates a collaborative mindset. The self-study track record suggests proactivity and a quick learning ability, which are positive for operational fit. However, without actual work experience, the application of these skills in a professional, fast-paced environment is yet to be validated.