Abhishek Nath

Data Analyst

Bhubaneswar, Odisha 80 · Excellent

About

Bachelor of Technology (Computer Science & Engineering) graduate with hands-on experience in data analysis, statistical modelling, data visualization, and machine learning using Python, SQL, Pandas, NumPy, Power BI, and Tableau. Skilled in exploratory data analysis (EDA), data cleaning, data validation, feature engineering, and MIS reporting to derive actionable business insights from structured datasets. Seeking an entry-level Data Analyst role to leverage analytical skills in transforming raw data into data-driven decisions that support business strategy and growth.

Skills & Expertise (45)

Python Advanced

8.0/10

Years Exp

Pandas Advanced

8.0/10

Years Exp

SQL Advanced

7.5/10

Years Exp

Data Cleaning Advanced

7.5/10

Years Exp

Exploratory data analysis Advanced

7.5/10

Years Exp

Data Analytics Advanced

7.5/10

Years Exp

NumPy Advanced

7.5/10

Years Exp

Feature Engineering Advanced

7.0/10

Years Exp

Data Visualization Advanced

7.0/10

Years Exp

ETL Intermediate

6.5/10

Years Exp

scikit-learn Intermediate

6.0/10

Years Exp

Data-driven decision making Classification Predictive Modelling Hypothesis Testing Git GitHub Jupyter Notebook MS Excel Cursor Attention to Detail Business Intelligence record maintenance Cross-Functional Collaboration Reporting Analytical Thinking Problem Solving Documentation Seaborn PySpark MySql Postgresql MongoDb Data Validation Data Wrangling Statistical Analysis Matplotlib Clustering Power BI Tableau Google Looker Studio Dashboard Development MIS Reporting Data Storytelling Linear Regression

Work Experience

Data Science & Machine Learning Intern

Prodigy InfoTech

Jan 2025 - Jan 2025

Analysed and preprocessed structured datasets using Python (Pandas, NumPy) — performed data cleaning, handling missing values, outlier detection, and exploratory data analysis (EDA) across 4 projects. Built statistical models and visualised insights using Matplotlib and Seaborn; translated model outputs into actionable business recommendations for retail customer segmentation and pricing analysis. Delivered end-to-end data pipelines covering data collection, data wrangling, feature engineering, model development, and performance reporting with key metrics (R², accuracy, precision, recall, F1-score).