DreamArch Learning
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
Program
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6 Weeks
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6 Weeks
My Modal
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
My Modal
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
My Modal
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
My Modal
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