Vijayant Pawar C++, C, Java, Python, Blockchain Technology
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I have a PhD from the National Institute of Technology Delhi, and my research topic is the Blockchain domain. I earned a B.Tech and M.Tech in Computer Science. Currently, I am working as an assistant professor at Galgotias University. I have 15 years of total experience (teaching + research). I want to share that students will do well if a teacher teaches them from the very basic level to the advanced level. So, I always try to teach in such a way that a five-year-old kid can understand. I always try to connect every concept with nature or our surroundings. It makes learning easy. I follow a practical-based approach.

Subjects

  • JAVA Beginner-Intermediate

  • Machine Learning Beginner-Expert

  • C and C++ Beginner-Intermediate

  • Data Structures and Algorithms Beginner-Expert

  • Blockchain Technology Beginner-Expert

Experience

  • Assistant Professor (Jun, 2023–Present) at Galgotias University GREATER NOIDA
    Working as a Assistant Professor in Computer Science and Engineering department
  • Assistant professor (Oct, 2014–Aug, 2018) at Noida Institute of Engineering & Technology, Greater Noida

Education

  • PhD (Aug, 2018–Dec, 2023) from National Institute of Technology Delhi

Fee details

    ₹800–1,500/hour (US$9.43–17.69/hour)

Courses offered

  • Design and Analysis of Algorithms

    • ₹1200
    • Duration: 40 Hours
    • Delivery mode: Online
    • Group size: 2
    • Instruction language: English
    • Certificate provided: No
    I taught "Design and Analysis of Algorithms" in five semesters in different colleges. The topics that I will teach in this course are as follows:

    1. Introduction to Algorithms and Complexity
    Definition and Importance of Algorithms
    Analysis of Algorithms: Time and Space Complexity
    Asymptotic Notations: Big-O, Big-Ω, Big-Θ
    Growth of Functions and Rates of Growth

    2. Algorithm Design Techniques
    a. Divide and Conquer
    Merge Sort, Quick Sort
    Binary Search
    Recurrence Relations and Master Theorem

    b. Dynamic Programming
    Optimal Substructure and Overlapping Subproblems
    Examples: Fibonacci Numbers, Longest Common Subsequence, Matrix Chain Multiplication, Knapsack Problem
    c. Greedy Algorithms
    Characteristics of Greedy Algorithms
    Examples: Huffman Encoding, Prim's Algorithm, Kruskal's Algorithm, Dijkstra’s Algorithm
    d. Branch and Bound
    Concept and Applications
    Examples: Travelling Salesman Problem, 0/1 Knapsack Problem

    3. Graph Algorithms
    Graph Representations: Adjacency List/Matrix
    Traversal Techniques: Depth-First Search (DFS), Breadth-First Search (BFS)
    Shortest Path Algorithms: Dijkstra, Bellman-Ford, Floyd-Warshall
    Minimum Spanning Tree: Kruskal’s and Prim’s Algorithms
    Topological Sorting
    Network Flow Problems: Ford-Fulkerson Algorithm

    5. Advanced Data Structures
    Binary Search Trees: AVL Trees, Red-Black Trees
    Heaps: Binary Heaps, Fibonacci Heaps
    Hashing: Hash Tables, Hash Functions
    Tries: Prefix Trees, Suffix Trees

    6. String Algorithms
    Pattern Matching: Knuth-Morris-Pratt (KMP), Rabin-Karp
    Suffix Trees and Arrays
    Longest Prefix Suffix (LPS)

  • Data Structures Using C

    • ₹1000
    • Duration: 40 Hours
    • Delivery mode: Online
    • Group size: 2
    • Instruction language: English
    • Certificate provided: No
    I taught this course many times in various colleges and the topics that I will cover in this course are as follows:

    1. Introduction to Data Structures
    Definition and Importance of Data Structures
    Abstract Data Types (ADTs)
    Classification of Data Structures: Linear vs Non-Linear, Static vs Dynamic
    Complexity Analysis: Time and Space Complexity
    Overview of Recursion and Its Role in Data Structures

    2. Linear Data Structures
    a. Arrays
    Basics and Operations: Traversal, Insertion, Deletion, Searching
    Multi-Dimensional Arrays: Matrices
    Applications: Prefix Sum, Sparse Matrices

    b. Linked Lists
    Singly Linked List
    Doubly Linked List
    Circular Linked List
    Operations: Insertion, Deletion, Traversal, Reversal
    Applications: Polynomial Arithmetic, Memory Management

    c. Stacks
    Implementation: Array-Based and Linked-List-Based
    Operations: Push, Pop, Peek
    Applications: Expression Evaluation (Postfix, Prefix), Balancing Parentheses, Undo Functionality

    d. Queues
    Types: Simple Queue, Circular Queue, Double-Ended Queue (Deque), Priority Queue
    Operations: Enqueue, Dequeue
    Applications: Scheduling, BFS (Breadth-First Search)

    3. Non-Linear Data Structures
    a. Trees
    Basics: Terminology (Root, Leaf, Depth, Height, etc.)
    Binary Trees: Representation and Traversals (Inorder, Preorder, Postorder)
    Binary Search Tree (BST): Operations and Applications
    Balanced Trees: AVL Trees, Red-Black Trees
    Heap: Min-Heap, Max-Heap, Heap Sort
    Advanced Trees: B-Trees, B+ Trees, Trie, Suffix Tree

    b. Graphs
    Representations: Adjacency Matrix, Adjacency List
    Traversals: Depth-First Search (DFS), Breadth-First Search (BFS)
    Directed and Undirected Graphs
    Weighted and Unweighted Graphs
    Applications: Shortest Path (Dijkstra, Floyd-Warshall), MST (Prim’s and Kruskal’s), Topological Sorting

    4. Hashing
    Hash Tables and Hash Functions
    Collision Resolution Techniques: Chaining, Open Addressing
    Applications: Caching, Indexing, Dictionary Implementations

    5. Searching and Sorting Algorithms
    Linear Search, Binary Search
    Sorting Techniques:
    Bubble Sort, Selection Sort, Insertion Sort
    Merge Sort, Quick Sort, Heap Sort, Counting Sort
    Comparison of Sorting Algorithms
    Applications of Sorting and Searching

    6. Strings and Pattern Matching
    Strings as Arrays of Characters
    Pattern Matching Algorithms: Naive, Rabin-Karp, Knuth-Morris-Pratt (KMP)
    Applications: Text Search, DNA Sequencing

    7. Memory Management
    Static and Dynamic Memory Allocation
    Garbage Collection Concepts
    Pointers and Their Role in Dynamic Data Structures

  • Blockchain Technology

    • ₹1200
    • Duration: 30 Hours
    • Delivery mode: Online
    • Group size: 2
    • Instruction language: English
    • Certificate provided: No
    I did my PhD in Blockchain Technology, and the topics that I will cover are as follows:

    1. Introduction to Blockchain Technology
    Overview and History
    Origins of Blockchain: Bitcoin Whitepaper and Cryptography
    Evolution of Blockchain Technology
    What is Blockchain?
    Definition and Basic Concepts
    Key Features: Decentralization, Transparency, Immutability
    Types of Blockchains
    Public, Private, Consortium, and Hybrid Blockchains

    2. Cryptographic Foundations
    Hash Functions
    Properties and Applications
    Common Algorithms (SHA-256, Keccak)
    Digital Signatures and Public-Key Cryptography
    Merkle Trees
    Structure and Role in Blockchain

    3. Blockchain Architecture and Components
    Blocks and Chains
    Structure of a Block: Header, Transactions, Hash
    Chaining of Blocks
    Consensus Mechanisms
    Proof of Work (PoW)
    Proof of Stake (PoS) and Variants (DPoS, NPoS)
    Practical Byzantine Fault Tolerance (PBFT)
    Delegated Proof of Stake (DPoS)
    Hybrid Models
    Nodes and Network
    Full Nodes, Light Nodes, and Mining Nodes
    Peer-to-Peer (P2P) Networks

    4. Smart Contracts
    Definition and Working
    Characteristics and Benefits
    Platforms for Smart Contracts: Ethereum, Hyperledger, etc.
    Development and Tools
    Solidity Language Basics
    Smart Contract Deployment
    Challenges: Security, Scalability

    5. Blockchain Security
    Common Attacks
    51% Attack, Double Spending
    Sybil Attacks
    DDoS Attacks
    Security Best Practices for Blockchain
    Auditing of Smart Contracts
    Cryptographic Vulnerabilities and Mitigation

    6. Scalability and Performance
    Issues with Blockchain Scalability
    Transaction Throughput
    Latency
    Solutions
    Layer-1 Improvements: Sharding, Improved Consensus
    Layer-2 Solutions: Lightning Network, Rollups, Plasma

    7. Real-World Applications
    Finance: Cryptocurrencies, Decentralized Finance (DeFi), Tokenized Assets
    Supply Chain Management: Provenance Tracking
    Healthcare: Secure Data Sharing, Patient Records
    Government and Identity: Digital IDs, Land Registries
    Internet of Things (IoT): Secure IoT Networks

  • Python Programming

    • ₹1000
    • Duration: 30 Hours
    • Delivery mode: Online
    • Group size: 2
    • Instruction language: English
    • Certificate provided: No
    The topics that I will cover in this course are:

    1. Introduction to Python
    History and Features of Python
    Installing Python and Setting Up the Development Environment
    Python IDEs: VS Code, PyCharm, Jupyter Notebook, etc.
    Writing and Running Python Programs
    Basic Syntax, Keywords, and Identifiers

    2. Python Basics
    Variables and Data Types
    Operators
    Arithmetic, Relational, Logical, Assignment, Bitwise
    Input and Output
    Comments and Docstrings
    Type Conversion and Casting

    3. Control Flow
    Conditional Statements
    if, elif, else
    Loops
    for and while Loops
    break, continue, pass
    Exception Handling
    try, except, finally
    Raising and Catching Exceptions

    4. Data Structures in Python
    Strings
    String Methods and Formatting
    String Slicing and Manipulation
    Lists
    List Methods, Slicing, Nested Lists
    Tuples
    Immutability and Applications
    Sets
    Operations: Union, Intersection, Difference
    Dictionaries
    Key-Value Pair Manipulation
    Comprehensions
    List, Dictionary, Set Comprehensions

    5. Functions
    Defining and Calling Functions
    Function Arguments: Positional, Keyword, Default, and Variable-Length
    Lambda Functions
    Scope and Lifetime of Variables
    Recursive Functions

    6. Object-Oriented Programming (OOP)
    Classes and Objects
    Constructors (__init__)
    Methods: Instance, Class, and Static
    Inheritance and Polymorphism
    Encapsulation and Abstraction
    Special Methods (Dunder/Magic Methods): __str__, __repr__, __len__, etc.

    7. Modules and Packages
    Importing and Using Standard Libraries
    Creating and Using Modules
    Working with pip to Manage Packages
    Virtual Environments

    8. File Handling
    Reading and Writing Files
    Working with Text, Binary, and CSV Files
    File and Directory Operations with os and shutil

    9. Advanced Topics
    Decorators
    Generators
    Iterators
    Context Managers (with statement)

    10. Data Science and Numerical Computing (Optional)
    Libraries:
    numpy: Arrays and Matrix Operations
    pandas: Data Manipulation
    matplotlib/seaborn: Data Visualization
    scipy: Scientific Computing
    Working with Jupyter Notebook

  • Machine Learning

    • ₹1200
    • Duration: 40 Hours
    • Delivery mode: Online
    • Group size: 2
    • Instruction language: English
    • Certificate provided: No
    I will cover these topics in this course:

    1. Introduction to Machine Learning
    Definition and Goals of Machine Learning
    Types of Machine Learning:
    Supervised Learning
    Unsupervised Learning
    Semi-Supervised Learning
    Reinforcement Learning
    Applications of Machine Learning
    Overview of ML Workflow

    2. Mathematics and Statistics for Machine Learning
    Linear Algebra
    Vectors, Matrices, and Operations
    Eigenvalues and Eigenvectors
    Probability and Statistics
    Probability Distributions
    Bayes’ Theorem
    Expectation, Variance, and Covariance
    Calculus
    Derivatives and Gradients
    Partial Derivatives and Gradient Descent
    Optimization
    Cost Functions
    Convex Optimization

    3. Data Preprocessing and Feature Engineering
    Data Cleaning and Handling Missing Data
    Feature Scaling: Normalization, Standardization
    Encoding Categorical Variables
    Feature Selection and Dimensionality Reduction
    Principal Component Analysis (PCA)
    Linear Discriminant Analysis (LDA)

    4. Supervised Learning
    a. Regression
    Linear Regression
    Polynomial Regression
    Ridge and Lasso Regression
    b. Classification
    Logistic Regression
    Decision Trees
    Random Forests
    Support Vector Machines (SVMs)
    K-Nearest Neighbors (KNN)
    Naive Bayes

    5. Unsupervised Learning
    Clustering
    K-Means Clustering
    Hierarchical Clustering
    DBSCAN
    Dimensionality Reduction
    PCA, t-SNE, UMAP
    Association Rule Learning
    Apriori Algorithm
    FP-Growth

    6. Ensemble Learning
    Bagging and Boosting
    Random Forests
    AdaBoost
    Gradient Boosting Machines (GBM)
    XGBoost, LightGBM, CatBoost
    Stacking and Blending

    7. Neural Networks and Deep Learning (Optional or Advanced)
    Perceptron and Multilayer Perceptrons (MLPs)
    Activation Functions
    Backpropagation and Optimization
    Introduction to Deep Learning Frameworks (TensorFlow, PyTorch, Keras)
    Convolutional Neural Networks (CNNs) for Image Processing
    Recurrent Neural Networks (RNNs) and LSTMs for Sequential Data

    8. Evaluation and Model Performance
    Train-Test Split and Cross-Validation
    Performance Metrics:
    Regression: MSE, RMSE, R²
    Classification: Accuracy, Precision, Recall, F1-Score, AUC-ROC
    Clustering: Silhouette Score, Inertia
    Overfitting and Underfitting
    Regularization Techniques
    L1, L2 Regularization
    Dropout (for Neural Networks)

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