Finishing School Programme under Joint certification with IIT Ropar

NIELIT and IIT Ropar announce

“Five month’s Finishing School Programme”

 Under Joint Certification

For Pass out & Final Year Students in the field of Computer Science/IT/ Electronics


Sr No.

Course Name

Tentative Start date

Tentative Start Time

Batch Size

1. Finishing School   10.00 am onwards 25 Rs 22,000/-*

*The fee is exclusive of GST or any other applicable taxes.


Eligibility  : B.Tech., MCA,  M.Sc. (IT/CS) or equivalent                         Admission Process: Screening interview


Course Highlights
  • Joint assessment and certification

  • Lectures by both NIELIT and IIT faculty

  • Major thrust on Hands-on training    
  • Course curriculum jointly designed by NIELIT and IIT

  •  Exposure & access to high standards of IIT & NIELIT’s industry oriented approach

   Finishing School - IT Programme

1. Core concepts

     a. Fundamentals of computers, Data Structures, DBMS, SAD, Web Designing, Networking concepts, IPR, IT Act, Soft Skills and

         Business Communication.

2. Specialization areas

    a. Compulsory

         i.Big Data Analytics and Data Science using Hadoop, Python, R Programming, Machine Learning.

     b. Elective (One) (Depending Upon the industry requirement):

          i. Java and Mobile Application Development using Android

          ii. Web application using .Net Technologies

          iii. Web application using PHP


   Finishing School - Electronics Programme

 1. Core concepts

      a. Basic Electronics Concepts, Embedded System Design, Single Board Computer (SBC): Raspberry Pi with Python, Basics of

          MATLAB  Programming, PCB design and layout using tools like OrCAD

  2. Specialization areas

      a. Compulsory 

           i. Internet of Things (IoT)

      b. Elective (One) (Depending Upon the industry requirement):

           i. Augmented Reality using IoT

           ii. Image processing using MATLAB

           iii. VLSI using Verilog

           iv. AI with Microcontroller with Tensor Flow