With IR 4.0 technology being rapidly adopted across all industries and trades, the job roles are also transitioning from the traditional ones. To quickly adapt to this transition in the job market and be ready to take up the available jobs, getting the right set of skills needed for these jobs is of utmost importance. The most ubiquitous new job roles are related to the technologies of Artificial Intelligence, Cloud Computing, hence the modules being delivered in this program are also related to these skills.
IR 4.0 Foundation Course
- Introduction of Operating System
- Getting started with Open-Source OS
- Linux Kernel and its distributions
- Installation of Ubuntu
- Linux Commands
- Shell Scripting, SSH and SCP commands in Linux
- Working on different text editors: nano, vi
- Managing Linux Files and User Permissions
- Why use a database DBMS, its characteristics and Applications
- Advantages and Disadvantages of DBMS
- Introduction to Relational Database – MySQL
- Structure of RDBMS and Terminology
- Manipulating and Querying Data: Introduction to MySQL, SQL Commands, DDL, DML, DQL, CRUD operations
- Understanding the WEB: Internet, Web page, Website, Web applications
- HTML for web Layout: HTML Basic Components, List, Tables, Graphics, Multi-Media, Forms, Text formatting, Block components
- CSS for Page Design: CSS design principles, property:values, dynamic CSS3, box model, design layout controls
- JS for Client-side scripting: Handling HTML Events, Animations, Reading element state & data, form handling and validations. Handling Cookies and Session Data
- Building Dynamic web pages using HTML 5, CSS3 and JS
Essential of JAVA Programming
- Fundamentals of open-source Tools & Technology: Installation of JDK and Set up the Java Environment
- Writing and interpreting the first program in Java
- Understanding data Structure in Java
- Control Statements and Functions, Methods in Java
- Understanding and implementing OOPS Concepts
- Integration of database technologies with Java
Essentials of Python Programming
- Fundamentals of open-source Tools & Technology: Installation of Anaconda and Set up the python Environment.
- Writing and interpreting the first program in Python.
- Understanding data Structure in Python.
- Control Statements and Functions, Methods in Python.
- Understanding and implementing OOPS Concepts.
- Integration of database technologies with python.
- Introduction to Python Popular Frameworks.
Understanding the Azure Cloud:
- Introduction to Azure Portal Cloud Services and Deployment Models
- Getting Started with an Azure Cloud: Exploring the Azure Service offerings.
- Fundamentals of Networking and Networking Protocols
- Creating Custom VPC in Azure and add subnets to created VPC and deploy VM in subnets and set up communication between the VMs.
- Market and JOB trends in Cloud
Advance Courses or Deep Dive Courses
These courses are for the final year students. This program has 2 verticals.
Vertical 1: Applied Cloud Computing for Software Development: Tools, technology, and Frameworks.
This is a comprehensive course track designed to equip students from final year with specialisation in technologies to become proficient software developers. The program covers key concepts, tools, and technologies in programming, web development, and software engineering methodologies. Through a combination of theoretical instruction, practical exercises, and real-world projects, learners will gain hands-on experience in developing software applications from inception to deployment.
The 140-160 hours of the program, covers the following aspects:
- Core Technical training with Hands on practice (80 hours).
- Capstone projects (25 hours).
- Sessions on employability skills (15 hours).
- Career conversations and knowledge sharing sessions by Industry Experts (10 hours).
- Self-paced courses from Microsoft and SAP (10 hours).
The core technical content breakdown has been delineated below.
- Understanding the need and requirement of Software development, Types of Software development.
- Overview of SDLC, Various SDLC Models (Waterfall, iterative, prototype, spiral, agile).
- Architecture of Software Application.
- Overview of frontend (HTML, CSS, JS), Dos & Don’t in designing.
- Getting started with HTML.
- Styling with CSS
- Front end development with Java Script
- Java Fundamentals
- Java Control Statements
- Java Array & String
- Java OOPs Concepts
- Java Error Handling
- Advanced Java
- Learning Server-Side Scripting
- Handling Data using Data Base Management System
- Getting Started with ReactJS: ReactJS, Features, Advantages & Disadvantages, Development Environment Setup.
- React Fundamentals: Components, Class & Function, Prop & States.
- Advanced Guide for React: Page Routing, Hooks, Apps, React
- Connection & Deployment.
- Getting Familiar with Restful API: Restful API, needs, Characteristics, Advantages and Disadvantages, Difference between REST API and RESTful API
- Moving Ahead with Spring Boot: Features, Architecture, Spring IO, Methods of RESTful API (GET, POST, PUT, DELETE), Spring Boot JDBC connection, Richardson Maturity Model
- RESTful Web Services Best Practices: Maven Project
- Getting Familiar with UI/UX.
- Five-key Principles of UX Design.
- Five key UI design Principles.
- How Companies Are Engaging Their Users with Real-Time on UX/UI.
- Azure Fundamentals
- Azure Database Services
- Azure App Service
- Connection to Apps & Database
- Scalability and Load Balancing
- Security and Compliance
- DevOps Practices
Vertical 2- The Industrial Artificial Intelligence with Cloud Computing
The Hands-on AI, Cloud Computing, and Database Integration program is designed to provide participants with a comprehensive understanding of Artificial Intelligence (AI), practical experience in cloud computing environments, and the integration of databases for efficient data storage and retrieval. Through a combination of theoretical knowledge and hands-on exercises, learners will develop the skills needed to build and deploy AI models, leverage cloud platforms for scalable data processing, and effectively manage data using databases.
The 140-160 hours of the program, covers the following aspects:
- Core Technical training with Hands on practice (80 hours).
- Capstone projects (25 hours).
- Sessions on employability skills (15 hours).
- Career conversations and knowledge sharing sessions by Industry Experts (10 hours).
- Self-paced courses from Microsoft and SAP (10 hours).
The core technical content breakdown has been delineated below.
- Understanding the terms like Artificial Intelligence, Machine Learning, Deep Learning.
- Applications of Machine Learning / Deep Learning techniques.
- Types of Data: Tabular Data, Text Data and Visual Data.
- Fundamentals of open-source Tools & Technology: Installation of Anaconda and Set up the python Environment.
- Writing and interpreting the first program in Python.
- Understanding data Structure in Python.
- Control Statements and Functions in Python.
- Understanding and implementing OOPS Concepts.
- Integration of database technologies with python.
- Understanding Exploratory Data Analysis and types of EDA.
- Python packages for EDA applications: Practicing with NumPy and Pandas.
- Practicing with Data Visualization python packages (matplotlib & seaborn).
- Understating web scraping for data gathering.
- Understanding Machine Learning Pipeline.
- Necessity of data pre-processing and cleaning.
- Techniques of data pre-processing and cleaning.
- Implementing an end-to-end deployment of Machine Learning models like Linear Regression, Logistic Regression, Decision Tree/ Random Forest, Naïve Bayes, SVM, K-Means, Ensemble Algorithms.
- Introduction to Multi-Layer Perceptron.
- Comparing machine learning and deep learning. Shallow vs Deep Neural Network.
- Exploring different DL algorithms and frameworks/libraries.
- Implementing an end-to-end deployment of Deep Learning models Multi-layer Perceptron, CNN, RNN, LSTM, BiLSTM.
- Hyperparameter tuning and model optimization.
- Underfitting, overfitting, best fitting.
- Gradient Descent Algorithm and other optimization techniques.
- Azure Fundamentals.
- Azure Database Services and Data Lake.
- Azure Machine Learning to Implement AI models and algorithms.
- Azure Cognitive Services like text analysis, vision, language, speech services of creation of responsive AI models.
- Azure OpenAI Services.
- Examining real-world use cases and success stories of regenerative AI and responsive AI.
- Discussing the ethical considerations and challenges associated with these AI approaches.
- Highlighting best practices and lessons learned from industry implementations.
