RUDRA NAYAK

Entry-level Data Analyst

New Delhi, India 0+ yrs exp 86 · Excellent

About

Entry-level Data Analyst with hands-on experience in SQL, Excel, Power BI, Python, and machine learning through internships and portfolio projects. Skilled in data cleaning, exploratory analysis, dashboard development, business reporting, and analytical problem-solving using real-world datasets.

Skills & Expertise (15)

Power BI Intermediate

6.8/10

0.5

Years Exp

SQL Intermediate

6.4/10

0.5

Years Exp

Python Intermediate

6.4/10

0.5

Years Exp

scikit-learn Intermediate

6.4/10

0.5

Years Exp

Microsoft Excel Intermediate

6.2/10

0.5

Years Exp

Power Query Intermediate

6.2/10

0.5

Years Exp

Pandas Intermediate

6.0/10

0.5

Years Exp

Jupyter Notebook Intermediate

6.0/10

0.5

Years Exp

VS Code Intermediate

6.0/10

0.5

Years Exp

NumPy Intermediate

5.6/10

0.5

Years Exp

Matplotlib Intermediate

5.6/10

0.5

Years Exp

Seaborn Intermediate

5.6/10

0.5

Years Exp

NLTK Beginner

4.8/10

0.5

Years Exp

TensorFlow Beginner

4.8/10

0.5

Years Exp

Keras Beginner

4.8/10

0.5

Years Exp

Work Experience

Data Analytics Intern

To-Let Globe

Apr 2026 - Jul 2026

Built an interactive Power BI dashboard using a retail mobile sales dataset containing 3,835 transactions across 14 fields. Designed KPI cards for total sales (n769.2M), quantity sold (19K+), transactions (4K+), and average sales value. Performed data cleaning and transformation in Excel and Power Query and analyzed monthly, brand-wise, city-wise, and payment-method trends. Wrote SQL queries using CTEs, window functions, ranking, and running totals to identify top-performing brands and revenue trends.

Machine Learning Intern

Edxcellence

Jan 2026 - Mar 2026

Developed end-to-end regression and classification workflows using Python and Scikit-learn on Student Performance and Breast Cancer datasets. Trained and evaluated Linear Regression, Gaussian Naive Bayes, and Decision Tree models with train-test split and 5-fold cross-validation. Achieved R² = 0.988 on the regression model and 94.15% classification accuracy on the breast cancer dataset while analyzing precision, recall, F1-score, and confusion matrices.