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NextGen Career Program
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4 / 6 / 8-week structured internship
A well-defined internship structure aligned with academic schedules, allowing colleges to choose 4, 6, or 8-week formats based on curriculum needs and depth of learning
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Integrated technical training
Students undergo focused technical training using industry-relevant tools and technologies, building strong foundations before moving into applied work
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Hands-on, real-world learning
Learners work on real-world tasks, guided practice, and practical assignments, ensuring concepts are applied in realistic industry scenarios rather than remaining theoretical.
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Industry & workplace readiness focus
The program bridges the gap between academics and industry by developing practical technical skills, professional confidence, and workplace readiness, helping students become more employable and industry-ready
Choose your Tech Track
Pick A Course To Get Started
My Modal
Machine Learning with Python
- by Dr. Divya Kumari
- English
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(720 Rating)
Week 1: Introduction to Python, Data Preprocessing & Regression
- Introduction to Machine Learning
- Downloading and Installing Python
- Data Preprocessing in Python
- Multiple Linear Regression
- Polynomial Linear Regression
- Decision Tree Regression
- Group Project: Perform data preprocessing (handling missing data, encoding categorical data, dataset splitting and feature scaling)
- Case Study: Implement and visualize Polynomial Regression, Decision Tree Regression, Support Vector Regression and Random Forest Regression
Week 2: Clustering & Reinforcement Learning
- Introduction to Clustering
- K-Means Clustering (Part I & II)
- Hierarchical Clustering (Part I & II)
- Introduction to Reinforcement Learning
- Upper Confidence Bound (Part I & II)
- Thompson’s Sampling
- Group Project: Build and visualize K-Means and Hierarchical clustering models
- Implement and visualize UCB and Thompson Sampling on datasets
Week 3: Classification Algorithms
- Introduction to Classification Problems
- Logistic Regression (Part I & II)
- Naïve Bayes
- Support Vector Machine (SVM)
- K-Nearest Neighbour (KNN)
- Random Forest and Decision Tree
- Mini Project: Implement and visualize classification algorithms and evaluate accuracy, sensitivity, specificity, F-score and precision
Week 4: Deep Learning – ANN, CNN & LSTM
- Introduction to Deep Learning
- Artificial Neural Network (Theory & Practical)
- Convolutional Neural Network (Theory & Practical)
- LSTM (Theory & Practical)
- Final Project: Build ANN, CNN and LSTM models and evaluate best performing model
Course Includes:
- Price: Rs 3000.00
- Instrutor: Dr. Divya Kumari
- Duration: 20 Hour
- Enrolled: 65 students
- Language: English
Mentor : Dr. Divya Kumari
Mission: “To build strong technical foundations and transform academic learning into industry-ready skills through practical, concept-driven teaching.”With over a decade of experience spanning academia and industry, Dr. Divya Kumari is a PhD-qualified Computer Science professional and passionate educator. Her expertise includes Python, Data Structures, Algorithms, Databases, Operating Systems, and Artificial Intelligence, backed by extensive teaching at reputed universities and hands-on industry exposure.
Her training sessions focus on clarity of concepts, real-world problem solving, and practical application, enabling learners to confidently apply their knowledge in interviews, projects, and professional environments.
My Modal
FullStack Java developer
- by Madhu Sharma
- English
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(720 Rating)
Week 1: Java Programming Fundamentals & OOP
- Day 1: Introduction to Internship Program, Overview of Java, JDK, JVM, JRE, Data Types, Variables, Operators, Writing First Java Program
- Day 2: Control Statements (if-else, loops, switch), Arrays in Java, Problem Solving using Loops and Arrays
- Day 3: Object-Oriented Programming Concepts, Classes & Objects, Constructors,
thiskeyword, Encapsulation - Day 4: Inheritance & Polymorphism, Method Overriding, Abstraction & Interfaces, Real-world OOP Examples
- Day 5: Exception Handling, Introduction to Java Collections, ArrayList & LinkedList Usage, Coding Practice Session
- Group Project: Java-based Mini Application using OOP Concepts, Problem Statement Discussion and Design
Week 2: Data Structures – Basics
- Day 1: Introduction to Data Structures, Need of Data Structures, Time & Space Complexity, Big-O Notation
- Day 2: Arrays as a Data Structure, Operations on Arrays, Problem Solving, Complexity Analysis
- Day 3: Stack Data Structure, Stack Operations (push, pop, peek), Implementation using Array, Applications of Stack
- Day 4: Queue Data Structure, Queue Operations, Types of Queue, Implementation using Array
- Day 5: Introduction to Linked List, Singly Linked List, Insertion & Deletion Operations, Coding Practice Session
- Group Project: Implementation of Stack, Queue, and Linked List in Java with Problem Discussion and Solution Explanation
Week 3: Advanced Data Structures & Algorithms
- Day 1: Doubly Linked List, Operations and Comparison with Singly Linked List
- Day 2: Searching Algorithms – Linear Search, Binary Search, Use Cases and Complexity Analysis
- Day 3: Sorting Algorithms – Bubble Sort, Selection Sort, Insertion Sort
- Day 4: Merge Sort, Introduction to Quick Sort, Comparison of Sorting Techniques
- Day 5: Recursion Concepts, Recursive vs Iterative Approach, Classic Recursion Problems, Coding Practice
- Mini Project: DSA-based Mini Project using Java, Problem Solving and Code Optimization
Week 4: Trees, Hashing & Interview Preparation
- Day 1: Introduction to Trees, Binary Tree Concepts, Tree Traversals (Inorder, Preorder, Postorder)
- Day 2: Binary Search Tree (BST), Insertion, Deletion, Search, Applications of BST
- Day 3: Hashing Concepts, HashMap & HashSet, Collision Handling, Real-world Use Cases
- Day 4: Problem-Solving Techniques, Interview-Oriented DSA Questions, Optimizing Brute-force Solutions
- Day 5: Revision of Java and Data Structures, Mock Interview Questions, Career Guidance and Best Practices
- Final Project: Java + Data Structures Project, Code Implementation & Explanation, Project Presentation and Evaluation
Course Includes:
- Price: Rs 3000.00
- Instrutor: Madhu Sharma
- Duration: 30 Hour
- Enrolled: 65 students
- Language: English
Mentor : Madhu Sharma
Mission: “To simplify complex programming concepts and empower learners to become confident, job-ready developers.”Madhu is a passionate technical trainer and developer with strong expertise in Java Full Stack development and Data Structures. She has trained engineering students and professionals through reputed programs like ByteXL, Wipro TalentNext, and Chandigarh University, helping learners crack fundamentals and build real-world projects with confidence.
My Modal
Data Analytics
- by Jyoti C Nayak
- English
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(720 Rating)
Data Analytics Internship Program – 10 Days Curriculum
- Day 1: Analytics Mindset & Business Problem Framing – Data analytics in business, Role of analytics in Marketing, Finance, HR & Operations, Descriptive vs Diagnostic vs Predictive Analytics, KPIs vs Metrics
- Day 2: Excel for Analytics – Data types, Logical functions (IF, AND, OR), Lookup functions (VLOOKUP, XLOOKUP), Date & Text functions
- Day 3: Excel Analytics & Visualization – Financial formulas, NPV, IRR, Dashboards, Business decision analysis
- Day 4: SQL Basics for Financial Data – SELECT, WHERE, ORDER BY, Filtering transaction data, Banking datasets
- Day 5: SQL for Analytics – Joins, Subqueries, GROUP BY, KPI calculation, Financial reporting
- Day 6: Power BI Fundamentals – BI vs Analytics, Data modeling basics, Relationships & Star schema, DAX overview
- Day 7: Power BI Analytics & DAX – Measures vs Calculated Columns, Core DAX functions (SUM, CALCULATE, IF), Time intelligence basics
- Day 8: Python for Data Analytics – Python syntax, Data types, Loops, Functions
- Day 9: Python for Data Analytics – Why Python for analytics, Pandas & NumPy basics, Reading & Cleaning data
- Day 10: Data Visualization & Storytelling – Choosing the right chart, Data storytelling for management, Executive presentation best practices
- Final Project & Industry Use Cases: Retail Sales Analytics, Banking KPIs, Marketing Campaign Analysis, HR Attrition Analysis
Course Includes:
- Price: Rs 1000.00
- Instrutor: Jyoti C Nayak
- Duration: 10 Hour
- Enrolled: 65 students
- Language: English
Mentor : Jyoti C Nayak
Mission: “To empower learners with industry-ready skills by combining practical technology training with real-world business applications.”With over 20 years of experience across industry and academia, Jyoti C. Nayak is a seasoned technical trainer and Microsoft Certified Trainer (MCT) specializing in Artificial Intelligence, Data Analytics, and Power BI. She has delivered both academic and corporate training programs, helping learners build strong analytical and technical capabilities.
Her expertise spans Power BI, Python, AI fundamentals, machine learning concepts, databases, and enterprise applications, supported by hands-on industry experience as a software developer. Known for her learner-centric approach, clear explanations, and practical demonstrations
My Modal
Data Structure in Python
- by Dr. Divya Kumari
- English
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(720 Rating)
Week 1: Basics of Python
- Day 1: Introduction to Python, Variables, Keywords, Data Types, Input and Output in Python
- Day 2: Conditional Statements – If, If-Else, Elif, Ternary Operator, Match-Case
- Day 3: Loops – For Loop, While Loop, Nested Loops
- Day 4: Python Functions
- Day 5: Recursion and Lambda Functions
- Group Project: Implement Python programs using conditional statements, loops and functions
Week 2: Strings and Lists
- Day 1: Python Strings – Operations on Strings, Common String Functions
- Day 2: Lists – Operations and Manipulating Lists
- Day 3: Array vs List, Binary Search
- Day 4: Selection Sort and Insertion Sort
- Day 5: Recursion in Detail, List Comprehension
- Group Project: Implement Searching Algorithms (Linear Search, Binary Search)
- Implement Sorting Algorithms (Selection Sort, Insertion Sort)
Week 3: Sorting, Tuples and Dictionary
- Day 1: Merge Sort and its Analysis
- Day 2: Quick Sort and its Analysis
- Day 3: Tuples and Dictionaries
- Day 4: Function Definitions and Exception Handling
- Day 5: Standard Input and Output Functions
- Mini Project: Implement Merge Sort and Quick Sort
- Perform operations on Tuples and Dictionaries
Week 4: Sets and Data Structures
- Day 1: Sets and their Operations
- Day 2: Stacks and Queues
- Day 3: Priority Queues and Heaps
- Day 4: Backtracking, N-Queens Problem
- Day 5: Global Scope and Nested Functions
- Final Project: Implement Set and its Operations
- Implement various Data Structures (Stack, Queue, Priority Queue, Heaps) and their Operations
Course Includes:
- Price: Rs 3000.00
- Instrutor: Dr. Divya Kumari
- Duration: 20 Hour
- Enrolled: 65 students
- Language: English
Mentor : Dr. Divya Kumari
Mission: “To build strong technical foundations and transform academic learning into industry-ready skills through practical, concept-driven teaching.”
With over a decade of experience spanning academia and industry, Dr. Divya Kumari is a PhD-qualified Computer Science professional and passionate educator. Her expertise includes Python, Data Structures, Algorithms, Databases, Operating Systems, and Artificial Intelligence, backed by extensive teaching at reputed universities and hands-on industry exposure.
Her training sessions focus on clarity of concepts, real-world problem solving, and practical application, enabling learners to confidently apply their knowledge in interviews, projects, and professional environments.



