Curriculum

The Fourth Industrial Revolution (Industry 4.0) is driven by advanced technologies such as database management, object-oriented programming (OOP), data structures & algorithms, which form the backbone of efficient software systems. Data science & analytics enable data-driven decision-making, while AI, machine learning (ML), and deep learning (DL) power intelligent automation and predictive modelling. Edge computing enhances real-time processing by decentralizing computation, reducing latency. Additionally, competitive coding skills sharpen problem-solving abilities, ensuring optimized and scalable solutions. Together, these technologies accelerate innovation, transforming industries through smarter automation, enhanced analytics, and seamless connectivity.

The Centres of Excellence established by Edunet Foundation in collaboration with SAP across the selected engineering colleges in Gujarat, Maharashtra Telangana and Karnataka will offer skilling opportunities to the students on the top emerging tech skills of Data Science & Analytics, Object Oriented Programming, Database management, Competitive Coding, Generative AI, and its predecessor Deep learning & Machine learning, Edge Computing, alongwith Industry Specific SAP tools like SAP ABAP Programming and Data Analytics Tools SAP Analytics Cloud & SAP HANA respectively. The program helps the student acquire the skills relevant to the current industry needs, and thereby gain a competitive edge in the job market.

The program curriculum caters to the 2nd, 3rd, and 4th year students from the partner engineering colleges as summarized below:

The program curriculum caters to the 2nd, 3rd, and 4th year students from the partner engineering colleges as summarised below:

Content Type Duration (Hours) Delivery Type Delivery Mode
2nd Year 3rd Year 4th Year
1Core Deep Tech Offering~55~75~35Instructor ledHybrid
2Employability Skills–~20~15Instructor ledHybrid
3Capstone Project~20~30~40Instructor ledHybrid
Year-wise Total Hours7512590

The core technical content breakdown has been delineated below:

II Year — Foundation Course

The Foundation Course builds the core programming, problem-solving and data fundamentals every engineering student needs before progressing to advanced, domain-specific technologies in later years.

Learning Outcomes

  • Write efficient programs and apply structured problem-solving.
  • Design software using object-oriented principles.
  • Implement and analyse common data structures and algorithms.
  • Model and query relational and non-relational databases.
  • Apply data science techniques to real datasets.
  • Build and present an end-to-end capstone solution.
01Python Programming Language

Python Programming Language

  • Introduction to Python
  • Data Types & Variables
  • Operators & Expressions
  • Control Flow (loops, conditionals)
  • Functions & Modules
  • Exception Handling
  • File Handling
  • Python Standard Library
  • Mini Project
02Object-Oriented Programming and Software Design

Object-Oriented Programming & Software Design

  • Classes & Objects
  • Inheritance
  • Polymorphism
  • Encapsulation & Abstraction
  • SOLID Principles
  • Design Patterns
  • UML Diagrams
  • Software Design Basics
  • Mini Project
03Data Structures and Algorithms

Data Structures & Algorithms (DSA)

  • Arrays & Linked Lists
  • Stacks & Queues
  • Trees & Graphs
  • Sorting & Searching Algorithms
  • Recursion
  • Dynamic Programming
  • Hashing
  • Time & Space Complexity Analysis
  • Mini Project
04Database Management Systems

Database Management Systems (SQL & NoSQL)

  • Introduction to DBMS
  • SQL Basics
  • Joins & Subqueries
  • Normalization
  • Transactions & Indexing
  • NoSQL Concepts
  • MongoDB Basics
  • Database Design
  • Mini Project
05Introduction to Competitive Coding

Introduction to Competitive Coding

  • Problem-Solving Techniques
  • Greedy Algorithms
  • Backtracking
  • Divide & Conquer
  • Bit Manipulation
  • String Algorithms
  • Practice Platforms
  • Coding Contests
  • Mini Project
06Data Science and Analytics

Data Science & Analytics

  • Data Preprocessing & Cleaning
  • Exploratory Data Analysis
  • Statistical Analysis
  • Data Visualization
  • NumPy & Pandas
  • Introduction to Machine Learning
  • Business Intelligence Basics
  • Reporting & Dashboards
  • Mini Project
07Capstone Project

Capstone Project

  • Design Thinking
  • Problem Identification
  • Solution Prototyping
  • Tech Stack Selection
  • Implementation
  • Testing & Validation
  • Documentation
  • Presentation & Demo

III Year — Advance Course

Code Unnati for the Advance course will be offered to the pre-final/final year students pursuing engineering and other technical degree courses. This course will cover the Advance Concept of Artificial Intelligence, Machine Learning, Deep Learnings, Generative AI, Edge Computing and Industry ready SAP ABAP & SAC tools with Practical Hands-on based Experimental Learnings.

Learning Outcomes

  • Apply the basic principles, models, and algorithms of AI to recognize, model, and solve problems in the analysis and design of information systems.
  • Analyze the structures and algorithms of a decision support system related to the field of machine learning and Artificial Intelligence.
  • Able to design and implement various machine learning algorithms in a range of real-world applications.
  • Appreciate the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and un-supervised learning.
  • Design deep learning based solution for image, time series and text related problems using CNN, RNN and LSTM categories of neural network respectively.
  • Deploy a solution for new problem in similar data domain using pretrained neural models.
  • Create an interfacing design including embedded boards, and deploy modern AI application on low power devices.
  • Demonstrate Industry Specific Modules, SAP Technical Modules – SAP ABAP.

Advance Course Outline (105 Hours)

01Artificial Intelligence & Machine Learning

Artificial Intelligence & Machine Learning

  • Supervised & Unsupervised Learning
  • Regression & Classification
  • Feature Engineering
  • Model Evaluation & Tuning
  • Decision Trees
  • Random Forest
  • Support Vector Machine
  • Clustering Algorithms
  • Reinforcement Learning Basics
  • Introduction to TensorFlow & PyTorch
  • Mini Project
02Deep Learning

Deep Learning

  • Neural Network Basics
  • Backpropagation & Activation Functions
  • Convolutional Neural Networks
  • RNNs & LSTMs
  • Transformer Architecture
  • Large Language Models
  • Fine-Tuning Pretrained Models
  • Mini Project
03Advanced AI/ML - AI on Edge Devices

Advanced AI/ML – AI on Edge Devices

  • Edge AI Concepts
  • Optimization for Low-Power Devices
  • TensorFlow Lite
  • ONNX Runtime
  • Raspberry Pi & Microcontrollers
  • Model Quantization & Pruning
  • Edge Deployment Strategies
  • Real-Time Inference on Edge
  • Mini Project
04SAP Developer Skills

SAP Developer Skills

  • Basic components of SAP Business Technology Platform
  • ABAP RESTful Programming Model
  • SAP Extension Suite and development efficiency
  • ABAP RESTful Application Programming Model
  • Transactional Behavior of an App
  • Service Consumption and Web APIs
  • Mini Project
05SAP Analytics Cloud

SAP Analytics Cloud

  • Getting Started with Stories
  • Building Stories
  • Configuring Story Elements
  • Basics of Data Structures in SAP Analytics Cloud
  • Designing and Creating Dimensions
  • Working With Live Models
  • Defining Data Security
  • Mini Project
06Capstone Project

Capstone Project

  • Capstone Project is based on learning and students create prototype level solutions for real life problems.
  • Design Thinking – Way to solve problems with creative thinking.

Employability Skills (20 Hours)

  • Mastering Self-Introduction & Personal Branding
  • Professional Communication
  • Smart Thinking
  • Career Readiness
  • Practice Session

IV Year — Value-Added Course

Code Unnati for the Value-Added course will be offered to those students who completed Advance Course of Code Unnati. This course will cover the needful skills required for recent trend of Competitive Coding, Placement practices and Industry ready tool SAP HANA with practical hands-on based experimental learnings.

Learning Outcomes

  • Uniquely skilled in enterprise database systems, algorithmic optimization, and high-performance computing.
  • Understand nodes, edges, adjacency lists/matrices, traversal methods and shortest path algorithms.
  • Implement efficient strategies for routing/navigation and solving network flow & connectivity issues.

Value-Added Course Outline (75 Hours)

01Advanced Competitive Coding

Advanced Competitive Coding

  • Advanced Graph Algorithms
  • Segment Trees & Fenwick Trees
  • Advanced Dynamic Programming
  • Bitmasking
  • Computational Geometry
  • Advanced Number Theory
  • Mini Project
02Placement Practice

Placement Practice

  • Aptitude Tests
  • Technical Skills
  • Coding Practice
  • Domain-Specific Knowledge
03Provisioning Data to SAP HANA

Provisioning Data to SAP HANA

  • Data Provisioning
  • Data Virtualization
  • Data Replication
  • Data Transformation
  • Modeling Environment
  • Calculation Views
  • Joining Data Sources
  • Data Slices
  • Embedding Functions
  • Deploying Custom Logic using SQL
  • Modeling Productivity Tools
  • Implementing Security in SAP HANA Modeling
04Capstone Project

Capstone Project

  • Capstone Project is based on learning and students create prototype level solutions for real life problems.
  • Design Thinking – Way to solve problems with creative thinking.

Employability Skills (15 Hours)

  • Job Readiness
  • Workplace Readiness
  • Design Thinking
  • Practice Session

Machine Learning, Computer Vision, IoT, SAP Tech. Skills — 5 Days Workshop

Workshop Objective / Outcome

  • To bridge the industry-academic gap by preaching the latest technology trends.
  • Exposure to different concepts, tools and algorithms in building intelligent systems through experiential learning.
  • Learn how to use Python libraries to develop Machine learning applications.
  • Use data analytics tools and technologies with ease.
  • Translate data-driven insights into decisions and actions.
  • Equip themselves with knowledge in areas of Data Analytics, AI, ML and computer vision, Deep Learning.
  • Develop the SAP tech level skills to deploy applications based on SAP tools ABAP & Analytic Cloud.

Workshop Prerequisite

  • Prior knowledge of basic python and its packages like NumPy, Pandas, and scikit-learn would be an advantage.
  • Working experience of Python Programming tools and IDE like Anaconda and Jupyter notebook.
  • Prior basic knowledge of Linux commands and familiar with Linux environment.

Tools / Software Requirements

  • Laptop/Computer with minimum i3 processor, 4GB RAM running with 64-bit Windows 10 or above along with access of internet connection with adequate speed.
  • Anaconda for / with python 3.

NOTE: *Uninterrupted high-speed LAN and Wi-Fi connectivity without any proxy is necessary for the training program.

Workshop Agenda

Sl. No. Topic Description Duration (30 Hrs.)
FDP Pre-assessment
Day-1
1. Introduction – Workshop Agenda, Objective · Data Analytics with Python · Numerical Python - NumPy · Pandas (Data Manipulation and data analysis) · Data Visualization Using Matplotlib and Seaborn · Machine Learning Algorithms · Introduction – Machine Learning – Supervised, unsupervised ML · Linear Machine learning model (linear regression / logistic regression and it’s Evaluation matrices). 6 Hrs
Day-2
2. Machine Learning Algorithms · Ensemble Machine learning models · Dimensionality Reduction Techniques (PCA) · Non-Linear Model (SVM & KNN) 6 Hrs
3. Deep Learning · Neural Networks – Neurons, Loss Functions, Weights · Gradient Descent and Back propagation · Convolutional Neural Network · Computer Vision – With Open cv and Keras
Day-3
4. Hands-on session on Computer vision · Canny edge detection · Viola-Jones Algorithm for face detection · Face detection · Full body detection · Number plate detection 6 Hrs
5. IoT Fundamentals · Internet Usage and Population Statistics. · IoT in Technical Perspective. · Introduction to Raspberry Pi 4B. · Installation of operating system on Raspberry PI
Day-4
6. Hands on with Raspberry pi and DFRobot · Hardware connection for DFRobot · Sensors and Actuators · LED interfacing with Raspberry PI · Light Sensor interfacing with Raspberry PI · Automatic streetlight · Button interfacing with Raspberry PI · Ultrasonic Sensor interfacing with Raspberry PI · SAP Tech Skills — SAP Business Technology Platform ABAP Environment · Understanding the SAP BTP platform · Creating a BTP ABAP Environment · Creating an ABAP Package 6 Hrs
Day-5
7. ABAP RESTful Programming Model · Condition statement and looping with ABAP · Creating a Database Table · Create an ABAP Class · Conditional statement and looping with sap ABAP · Introduction of ABAP RESTful Application Programming Model · Developing a Read-Only List Report App · Enabling the Transactional Behavior of an App · Dealing with Existing Code · SAP Analytics Cloud · Getting Started with Stories · Building Stories · Configuring Story Elements · Manipulating Data in Stories · Presenting Stories 6 Hrs
FDP Post-assessment and Feedback