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Diploma In Digital Marketing and Web Development
Advance Digital Marketing
Social Media Marketing
Search Engine Optimization
Affilate Marketing
Google Ads
Full-Stack Development
UI/UX Design
Front-End Development
Back-End Development
Web Design & Development
Wordpress Development
Master Certification in ChatGPT
Certification Course in ChatGPT
Google Apps
AI Graphic Design
The Comprehensive Diploma in Data Science and AI - offers complete educational
content for those who want to achieve excellence in data science and artificial intelligence and
full stack development. Students learn a programming fusion and advanced analytical methods
together with AI proficiency from this advanced course to excel in modern tech industries.
A basic introduction to programming languages C and C++ and Python establish fundamental
understanding for students to solve challenging problems. The web technology disciplines
HTML, CSS, Advanced CSS, JavaScript, Advanced Javascript and Bootstrap establish
knowledge foundations which guide students toward developing robust Frontend Development
abilities and executing practical projects.
Python & MySQL serve as foundation backend topics while the class teaches server-side
development techniques before students complete Full Stack Development projects.
Understanding data analysis to predict results requires students to learn Data Analysis
alongside Statistics and Machine Learning Basics during their moving to Data Science.
The course program evolves through Advanced Data Science and Machine Learning until it
reaches Ai Basics to AI Applications where learners acquire modern artificial intelligence
expertise and its business applications.
A project-based learning pathway of this program makes graduates suitable to enter AI and data
science professions in their expanding market.
Course Highlights
1. |
Front-End Develpment |
2. |
Back-End Development |
3. |
10 Course Modules |
4. |
365 hours Training |
5. |
10+ Assignments |
6. |
10+ Live Projects |
7. |
Industrial Level Projects |
8. |
1 Year Free Backup Classes |
Learning Outcome
| • | Proficiency in Frontend Technologies: Mastery of HTML, CSS, and JavaScript to create interactive and visually appealing websites. |
| • | Backend Development Skills: In-depth knowledge of backend development using Node.js and Express.js and Experience in building RESTful APIs and integrating them with frontend systems. |
| • | Proficiency in Frontend Technologies: Mastery of HTML, CSS, and JavaScript to create interactive and visually appealing websites. |
| • | Database Management and Integration: Ability to design and manage efficient database schemas and handle data storage, retrieval, and manipulation. | Read More |
| • | Version Control and Collaboration: Competence in using Git for version control, collaboration, and managing code repositories on platforms like GitHub or GitLab. |
| • | Deployment and Cloud Integration: Understanding of DevOps principles and CI/CD practices for continuous integration and delivery. |
| • | Problem-Solving and Communication Skills: Strong debugging and problem-solving abilities for complex development challenges. | Read Less |
Software that you will learn in this course
Course Content
| • | Introduction to C and Environment Setup |
| • | Variables, Data Types, and Operators |
| • | Control Structures: Decision Making and Loops |
| • | Functions and Arrays |
| • | Pointers and Memory Management |
| • | Strings and String Handling |
| • | File Handling in C |
| • | Building Mini-Projects in C |
| • | Basics of C++ and Object-Oriented Programming |
| • | Classes, Objects, and Constructors |
| • | Inheritance and Polymorphism |
| • | Operator Overloading and Templates |
| • | File Handling and Streams |
| • | Exception Handling in C++ |
| • | Standard Template Library (STL) |
| • | Advanced C++ Concepts and Mini-Projects |
| • | Introduction to Python and IDE Setup |
| • | Variables, Data Types, and Basic Input/Output |
| • | Control Flow: Conditional Statements and Loops |
| • | Functions, Modules, and Packages |
| • | File Handling and Exception Handling |
| • | Object-Oriented Programming in Python |
| • | Working with Libraries (e.g., NumPy, Pandas) |
| • | Python Mini-Project (e.g., Calculator or Basic Automation Script) |
| • | Introduction to HTML and Setting Up the Environment |
| • | HTML Tags and Attributes |
| • | Working with Text, Links, and Images |
| • | Tables and Lists in HTML |
| • | Forms and Input Controls |
| • | Multimedia in HTML: Audio and Video |
| • | Semantic HTML and Accessibility |
| • | Mini Project: Basic Web Page Creation |
| • | Introduction to CSS: Syntax and Selectors |
| • | Applying Styles to Text and Fonts |
| • | Box Model: Margins, Borders, and Padding |
| • | CSS Positioning and Layouts |
| • | Styling Links, Buttons, and Forms |
| • | Colors and Backgrounds in CSS |
| • | Media Queries for Responsive Design |
| • | Mini Project: Styling a Static Web Page |
| • | CSS Flexbox: Concepts and Applications |
| • | CSS Grid Layout System |
| • | Animations and Transitions in CSS |
| • | Advanced Selectors and Pseudo-classes |
| • | Custom Fonts and Typography Techniques |
| • | CSS Variables and Preprocessors (e.g., SASS) |
| • | Building Complex Layouts with Advanced CSS |
| • | Mini Project: Advanced Styling for a Web Application |
| • | Introduction to JavaScript and Basics of Syntax |
| • | Variables, Data Types, and Operators |
| • | Control Flow: Conditionals and Loops |
| • | Functions and Events in JavaScript |
| • | DOM Manipulation Basics |
| • | Arrays and Objects in JavaScript |
| • | Error Handling and Debugging |
| • | Mini Project: Interactive Web Page (e.g., Image Gallery) |
| • | Introduction to ES6+ Features (e.g., Let, Const, Arrow Functions) |
| • | JavaScript Promises and Async/Await |
| • | Classes and Modules in JavaScript |
| • | Advanced DOM Manipulation |
| • | Event Propagation: Bubbling and Capturing |
| • | Fetch API and AJAX Requests |
| • | Web Storage (LocalStorage, SessionStorage) |
| • | Mini Project: Dynamic Web Application (e.g., Weather App) |
| • | Introduction to Bootstrap and Setup |
| • | Bootstrap Grid System |
| • | Components: Navbar, Buttons, and Modals |
| • | Forms and Input Styling in Bootstrap |
| • | Customizing Bootstrap with CSS |
| • | Responsive Design Using Bootstrap |
| • | Adding Icons and Utilities |
| • | Mini Project: Bootstrap-Based Website |
| • | Project Overview and Requirement Gathering |
| • | Wireframe and Design Preparation |
| • | Setting Up the Project Environment |
| • | HTML Structure for the Web Application |
| • | Applying CSS Styles and Responsive Design |
| • | Adding Interactivity with JavaScript |
| • | Debugging and Testing the Application |
| • | Final Deployment of the Frontend Project |
| • | Setting Up the Environment (Python and MySQL) |
| • | Basics of Databases and SQL Queries |
| • | Connecting Python with MySQL |
| • | CRUD Operations with Python and MySQL |
| • | Using Flask for Backend Development |
| • | User Authentication System Development |
| • | Handling Errors and Logging |
| • | Mini Project: Backend for a Blog Application |
| • | Introduction to Full Stack Development |
| • | Frontend Development Recap (HTML, CSS, JavaScript) |
| • | Backend Development Recap (Flask and MySQL) |
| • | RESTful API Creation with Flask |
| • | Integrating Frontend with Backend |
| • | Deploying Applications on Cloud Platforms |
| • | Security Best Practices for Web Applications |
| • | Mini Project: Full Stack Application Development |
| • | Requirement Gathering and Wireframe Design |
| • | Setting Up the Full Stack Environment |
| • | Frontend Development for the Project |
| • | Backend API Development |
| • | Integration and Testing |
| • | Adding Authentication and Authorization |
| • | Debugging and Performance Optimization |
| • | Final Deployment and Project Submission |
| • | Introduction to Data Analysis and Tools |
| • | Data Cleaning and Preprocessing |
| • | Exploratory Data Analysis (EDA) |
| • | Data Visualization with Matplotlib and Seaborn |
| • | Statistical Concepts in Data Analysis |
| • | Handling Time-Series Data |
| • | Automating Data Analysis Workflows |
| • | Mini Project: Dataset Analysis and Visualization |
| • | Introduction to Machine Learning and Statistics |
| • | Linear Regression and Logistic Regression |
| • | Classification Algorithms (e.g., Decision Trees) |
| • | Clustering Algorithms (e.g., K-Means) |
| • | Evaluation Metrics for ML Models |
| • | Data Preparation for Machine Learning |
| • | Basics of Feature Engineering |
| • | Mini Project: Building a Simple ML Model |
| • | Working with Large Datasets |
| • | Advanced Visualization Techniques |
| • | Deep Dive into Feature Engineering |
| • | Dimensionality Reduction Techniques (e.g., PCA) |
| • | Model Optimization Techniques (e.g., Grid Search) |
| • | Deploying Machine Learning Models |
| • | Introduction to Big Data Tools (e.g., Spark) |
| • | Mini Project: Advanced Machine Learning Pipeline |
| • | Introduction to Artificial Intelligence |
| • | AI vs. Machine Learning vs. Deep Learning |
| • | AI Applications in Real Life |
| • | Basics of Neural Networks |
| • | Introduction to Natural Language Processing (NLP) |
| • | AI Tools and Libraries Overview |
| • | Ethical Aspects of AI Development |
| • | Mini Project: Simple AI Model Development |
| • | Deep Learning Basics and Frameworks (e.g., TensorFlow) |
| • | Building Neural Networks with Keras |
| • | Convolutional Neural Networks (CNNs) for Image Processing |
| • | Recurrent Neural Networks (RNNs) for Time-Series Data |
| • | Transfer Learning and Pretrained Models |
| • | Hyperparameter Tuning for Deep Learning Models |
| • | Generative Adversarial Networks (GANs) |
| • | Mini Project: Image Recognition or Sentiment Analysis |
| • | Advanced Natural Language Processing |
| • | AI for Computer Vision Applications |
| • | AI in Healthcare and Finance |
| • | Building Chatbots with AI |
| • | Using AI for Predictive Analysis |
| • | AI for Automation and Robotics |
| • | AI in Gaming and Entertainment |
| • | Final AI Project: Real-World AI Solution |
Jobs and Career Opportunity After Completing this Course
A Diploma in Data Science and AI opens doors to various job profiles that are in high demand due to the increasing reliance on data-driven decision-making and AI technologies across industries. Here are some common job profiles associated with this diploma along with their estimated salary ranges:
| Job profile After completing this course |
Average salary ( 1+ year experience) |
|---|---|
| Data Scientist | 6L-15L |
| Machine Learning Engineer | 7L- 8L |
| Data Analyst | 3.5L- 8L |
| Business Intelligence Analyst | 5L- 10L |
| Data Science Researcher | 8L- 18L |
| AI Product Manager | 12L- 25L |
Backup Class
Flexible Timing
Fees Installment
Expert Trainer
100% job assistance
Free Library
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Live Project
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Practical learning
This pro-level data science and AI diploma follows an incredible structured format to teach programming basics while it progresses beyond advanced AI applications. Hands-on projects rendered particular value due to the Full Stack Development Project. The curriculum of this program combined with all necessary knowledge points makes me feel ready to solve practical data science and AI problems.
I entered this course for data science career path training but it turned out better than projected. The Advanced Machine Learning section along with the AI Applications section gave me comprehensive learning about deep practical understanding. By combining statistics with programming knowledge I obtained the competency necessary for industry work.
The course direction between foundational topics and advanced content positions itself entirely unique from other programs. Through the combination of machine learning and backend development I gained comprehensive insight about how data powers software applications. The introductory AI Basics module provided essential groundwork leading students toward their exploration of AI applications.
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Frequently Asked Questions
This course focuses on C and C++ alongside Python editing and HTML design with CSS modeling and JavaScript programming and advanced language versions.
Studying this project gives students an opportunity to merge frontend and backend capabilities as they work on making usable web applications from scratch.
Through AI Applications and Machine Learning Advanced students acquire practical abilities to build AI solutions for real-world implementations.
The program foundation covers AI basics and machine learning advancement followed by practical applications so students qualify for AI Engineer and Data Scientist and Machine Learning Specialist positions.
The field allows entrance to IT or healthcare or financial or e-commerce or educational or data science and AI-powered establishments.
The learning institution offers placement support which matches graduates with data science and artificial intelligence job opportunities.
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