Data Science Certification Course in Chandigarh — Python, ML & Power BI

Verifiable Certificate ID

Each certificate carries a unique ID and QR code so employers can confirm it directly from our records.

Competencies Listed

Your certificate names the exact skills validated — Python, Pandas, SQL, Statistics, ML, Power BI, Visualisation.

Capstone-Backed

Issued only after submission of a real-world data science capstone reviewed by our mentors, not on attendance alone.

Three Recognised Tiers

Fundamental (3 mo), Advanced (6 mo) or Pro + Data Science (12 mo) — pick the certification level that fits your goal.

Understand what data science is, its applications across industries, the data science lifecycle, and the role of a data scientist in modern organizations.

Master Python from scratch — variables, data types, control flow, functions, OOP, file handling, and libraries. Build a solid programming foundation for data analysis.

Learn NumPy for efficient numerical operations, array manipulation, broadcasting, linear algebra, and statistical computations that power data science workflows.

Master Pandas for data cleaning, transformation, aggregation, merging, and exploratory data analysis. Work with real-world datasets including CSV, Excel, and JSON.

Create compelling visualizations using Matplotlib and Seaborn. Learn charts, plots, heatmaps, and storytelling with data to communicate insights effectively.

Master SQL for querying relational databases. Learn SELECT, JOIN, GROUP BY, subqueries, and database design. Connect Python to MySQL and PostgreSQL for data extraction.

Learn descriptive statistics, probability distributions, hypothesis testing, confidence intervals, correlation, and regression analysis — the mathematical foundation of data science.

Master the art of EDA — identifying patterns, outliers, distributions, and relationships in data. Learn systematic approaches to understanding datasets before modeling.

Introduction to supervised and unsupervised learning. Learn linear regression, logistic regression, decision trees, random forests, and model evaluation metrics.

Deep dive into SVM, KNN, gradient boosting, ensemble methods, clustering algorithms, dimensionality reduction, and hyperparameter tuning with Scikit-Learn.

Learn feature selection, encoding, scaling, handling missing data, imbalanced datasets, and creating features that improve model performance significantly.