Advanced Diploma in Software Engineering (ADSE) 

Term 1 (CPISM) + Term 2 (DISM) + Term 3A (Java) + Term 4D (AIML)
Total 2 Years 
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Course Overview

Module Duration Instructional Hours Theory Lab Self-Study
Web Component Development using Jakarta EE 40 20 20 16
Building Java Web Applications with Spring Framework 24 12 12 6
Introduction to Dart Programming 16 8 8 8
Application Development Using Flutter and Dart 40 20 20 16
Agile and DevOps 24 24 8
eProject-Cross Platform App Development 2 2
Application Based Programming in Python 36 18 18 10
Inferential Statistical Analysis 16 16
AI Primer 16 16 16
AI Applications of NLP 40 20 20 8
AI and ML with Python 40 20 20 12
Applied Machine Learning using Python 40 20 20 12
Deep Learning using Neural Networks 60 30 30 12
Capstone Project-Recommendation Engine and Customer Churn Prediction 40 2 38
Total Hours 434 228 206 124

Tool / Software

TERM 3A
  • Jakarta EE  Platform 10
  • Spring 6.x, Spring Boot 3.0.x
  • Flutter SDK 1.22 with Dart 2.10.x
TERM 4D
  • Python
  • NLP Tools and Libraries
  • Jupyter Notebook, Google Collab
  • TensorFlow

Career Opportunity

  • AI Developer

LEARNING OUTCOMES

TERM 3A

Term 3A focuses on building Web application and mobile App development skills in students. After the completion of Term 3A, students will be able to:
  • Develop Web applications suited to any Jakarta EE application server using JSP and Servlet APIs
  • Understand and work with the Spring Framework and Spring Boot
  • Build cross platform apps using Flutter Framework and Dart language
  • Develop Web applications suited to any Jakarta EE application server using JSP and Servlet APIs
  • Implement software development process using Agile methodology
  • Develop a Cross Platform App using Dart and Flutter.
The Project in this Term will involve developing a real-world App using Cross Platform technologies.

TERM 4D

Term 4E enables students to master concepts of AI & ML and then, apply them to develop applications. After the completion of Term 4E, students will be able to:
 
  • Understand the basics of statistical analysis, descriptive statistics, predictive analytics, probability, and Bayes theorem.
  • Gain an understanding of AI.
  • Gain knowledge in NLP and learn the use of AI in NLP.
  •  Use important building blocks of AI & ML with Python, make data modelling decisions, interpret output of the algorithms, and validate results.
  • Master ML concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop AI algorithms.
  • Master deep learning concepts and TensorFlow open-source framework, implement deep learning algorithms, and build ANN.
  • Develop a real-world Capstone project on recommendation engine and perform customer churn prediction.
The Project in this term will involve developing a recommendation engine and perform customer churn prediction.