• Mode: Online + Offline
• Duration: 4–6 Months
• Level: Beginner to Advanced
• Training Type: 100% Practical
• Internship + Placement: Included

• Become job-ready for Data Science & ML roles
• Gain strong practical skills with tools like Python, Pandas, Power BI
• Build multiple ML projects for your resume
• Learn how to analyze and visualize data professionally
• Internship opportunity & placement assistance
• Lifetime study material access

• Module 1: Introduction to Data Science

  • What is Data Science?

  • Tools & Industry Use Cases

  Module 2: Python for Data Science

  • Python Basics & Libraries

  • NumPy, Pandas, Matplotlib, Seaborn

  Module 3: Statistics & Mathematics

  • Probability, Distribution

  • Hypothesis Testing, Correlation

  Module 4: Data Cleaning & Preprocessing

  • Handling Missing Data

  • Feature Engineering

  Module 5: Data Visualization

  • Charts & Graphs

  • Dashboards (Power BI / Tableau)

  Module 6: SQL for Data Analysis

  • Joins, Group By, Subqueries

  • Analytical Queries

  Module 7: Machine Learning

  • Supervised & Unsupervised Learning

  • Regression, Classification, Clustering

  Module 8: Advanced ML

  • Feature Selection

  • Model Evaluation & Tuning

  Module 9: Capstone Projects

  • End-to-End ML Project

  • Analytics Case Studies

Our fee structure is most reasonable as per current market demands. We understand the importance of hard earned money and hence we have very cost effective fee structure for different role based trainings which is affordable compared to other institute in market.

We also have discount plans in case of more students from single college or group of students willing to enroll for specific course. In group of students we provide special discount making the total fee even more affordable.

• PDF Notes
• Practice Code Files
• Real Datasets
• Recorded Classes
• Project Templates

1. What is Data Science?
   Data Science is the process of analyzing data to extract insights and build predictive models.

2. What libraries are used in Data Science?
   NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn.

3. What is Machine Learning?
   A method where computers learn patterns from data to make predictions.

4. Difference between supervised and unsupervised learning?
   Supervised uses labeled data; unsupervised finds hidden patterns.

5. What is overfitting?
   When a model performs well on training data but poorly on new data.

6. What is a confusion matrix?
   It shows prediction accuracy for classification problems.

7. Explain linear regression.
   A model to predict a continuous value using a straight-line relationship.

8. What is feature engineering?
   Transforming raw data into meaningful input for machine learning.

9. What is EDA?
   Exploratory Data Analysis helps understand data patterns and distribution.

10. What is cross-validation?
    A technique to test model performance on different subsets of data.

11.