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Student Life at SSN

SSN’s 250-acre state-of-the-art campus with world class infrastructure is designed to provide 360 degree development through technology-enabled learning for our students. Our students have access to a healthy balance of cultural activities and sports inside our campus, alongside curriculum-oriented projects that form an environment conducive to student growth.

Our Students

Batch 12 in progress.

Gender Ratio

Gender Ratio

Years of Experience

years-of-experience
Fresher
35%
< 2 yrs
25%
2-5 yrs
30%
> 5 yrs
10%

Educational Background

educational Background
80%
B.E/B.Tech
10%
M.E/M.Tech
15%
MCA/M.A Economics/M.Sc Actuarial Science

Industry Background

Industry Background
  • 30%Manufacturing & Production
  • 25%Banking & Finance
  • 35%IT/ ITES
  • 10%Others

Student Projects

At SSN, students don’t just learn Data Science skills, but put them to good use. Here are some of the projects that have been a stepping stone for our students to make a mark in this data-driven world!

Data Visualization Projects

Project 1

Food Nutrition Analysis

Keerthana conducted this project to gain insights about nutrition using the data found with food & drink items. The focus of this analysis was on nutritional elements such as fat, fiber, trans fats, proteins, and carbohydrates. She explored the calorie content of sandwiches and wraps; meat and cheese; cakes, pies, and salads.

Project 2

Analysis of factors contributing to mileage in automobiles

Durai developed a descriptive analytics story to analyze the mileage of automobiles based on various factors such as the number of cylinders, rotations per minute, wheel base, engine type, horsepower, etc. The results of his work depict the impact of the above factors on the mileage of an automobile.

Machine Learning Projects

Project 1

Predicting Titanic Survivors

Juben & Preethika applied appropriate machine learning techniques to predict which of the passengers survived the Titanic shipwreck. They used personal and travel information of the passengers to create predictive models. The accuracy of the models is found to be more effective in the prediction.

Project 2

Recommendation Systems

Lawrence & Naveen developed a recommender system to cluster URLs of various domains, such as education, e-commerce, finance, etc. on the web. The system provides recommendations about the medium of marketing for the domains.