Post Graduate Program inPre-Graduate Program in Full Stack Web Development

• Setting up the environment for Java
• Understanding the Java Ecosystem
• Understanding the Eclipse IDE
• Running your first Java program
• Running and debugging Java Application

• Data types and operators
• Conditional statements
• Iterative statements
• Other control statements
• Functions and methods
• Working with Arrays in Java
• Working with Strings in Java

• Classes and objects
• Constructors
• The ‘this’ pointer
• The static and final keyword
• Access modifiers

• Inheritance (is-a relationship)
• Composition (has-a relationship)
• Abstract classes and interfaces
• Method overloading and overriding
• Polymorphism
• Encapsulation and abstraction

• Packages
• Exception Handling
• File handling
• I/O

• Optionals
• Memory Management
• Multithreading

A data structure is a named location that can be used to store and organise data. An algorithm is a collection of steps to solve a particular problem. Data structures provide a means to manage large amounts of data efficiently.

• In the first week of this course, you will learn:
  
  • Stack memory vs heap memory in Java
  • Physical vs logical data structure
  • Abstract data types and data structures
  • Complexity analysis
  • Asymptotic analysis, comparison of functions, recurrence relations
    
    • Time complexity
    • Space complexity
    • Master theorem
    • Amortized analysis
    • Iteration Vs recursion
  • Recursion - Factorial, Fibonacci, finding nCr, tower of Hanoi problem.

Arrays are used to store multiple values in a single variable, instead of declaring separate variables for each value. A string is a sequence of characters like - "hello".

• In this session, we will learn about:
  
  • Arrays
    
    • Static arrays
    • Dynamic arrays
    • 2D arrays, row-major and column-major mapping, matrices
    • Strings
  • LinkedList
    
    • Singly LinkedList and its operations
    • Doubly LinkedList and its operations
    • Circular LinkedList and its operation

The stack is a linear data structure that is used to store a collection of objects. It is based on Last-In-First-Out (LIFO).

• In this session, we will be discussing about:
  
  • Stacks
    
    • Implementations- using arrays, using linked lists
    • Operation
    • Applications
  • Queues
    
    • Implementations- using arrays, using a LinkedList, using two stack
    • Circular queues
    • Priority queues

A tree is a non-linear data structure where data objects are organised in terms of hierarchical relationships. In this week's session, we’ll be discussing them one by one.

• In this week, we will discuss about
  • Trees.
    
    • Binary trees, their representations
    • Pre-order, In-order, post-order traversal
    • Binary search trees
    • AVL trees

Heap and Trie are special cases of trees. A heap is a special tree-based data structure in which the tree is a complete binary trees.

• In the session, we will discuss about
  • How to solve the longest word in the dictionary problem using tries.
  • Heaps
    
    • Min Heap
    • Max Heap
    • Find K largest (or smallest) elements in the array
  • Tries
    
    • Longest Word in Dictionary Problem

A graph is a data structure that consists of a finite set of vertices, also called nodes and a finite set of ordered pairs of the form (u, v) called edges.

• In this week's session, We will be discussing different types of algorithms, like:
• Graphs
  
  • Types of graphs
  • Breadth-first search, depth-first search
  • Topological sort
  • Shortest path problem
• Algorithms
  
  • Dijkstra's algorithm
  • Bellman–Ford algorithm
    
    • Minimum spanning tree problem
    • Prim's algorithm
  • Kruskal's algorithm

• In this week, we will discuss about
  • Sorting-
    
    • Types of sorting techniques
      
      • Bubble sort
      • Insertion sort
      • Selection sort
      • Quick sort
      • Merge sort
      • Heap sort
      • Count sort
      • Bucket sort
      • Radix sort
      • Shell sort

Searching is probably one of the most common actions performed in a regular business application. This involves fetching data stored in data structures like arrays, lists, and maps. Hashing is the process of converting a given key into another value. A hash function is used to generate the new value according to a mathematical algorithm.

• During this week we will be discussing the following:
  
  • Searching
    
    • Linear search
      
    • Binary search
      
  • Hashing
    
    • Hash function
      
    • Collision handling
      
  • Chaining
    
  • Open addressing
    
    • Linear probing, primary clustering
      
    • Quadratic probing, secondary clustering
      
  • Double hashing

A greedy algorithm is a simple, intuitive algorithm that is used in optimization problems.

• The topics discussed in this session will be-
  
  • The strategy of greedy algorithms
  • Elements of greedy algorithms
  • Advantages of greedy algorithms
  • Disadvantages of greedy algorithms
  • Applications of greedy algorithms
  • Knapsack problem
  • Job sequencing with deadlines
  • Huffman coding

The divide and conquer approach is pretty straightforward, the problem at hand is divided into smaller subproblems and then each problem is solved independently. As we keep dividing the subproblems into even smaller problems, we eventually reach a stage where no more division is possible.

• In the session this week, we will discuss:
  
  • The strategy of divide and conquer techniques
  • Advantages of divide and conquer techniques
  • Disadvantages of divide and conquer techniques
  • The master theorem of divide and conquer techniques
  • Applications of divide and conquer techniques
  • Find the median of two sorted arrays
    

Backtracking is an algorithmic-technique for solving problems recursively by trying to build a solution incrementally.

• We’ll discuss two methods used to backtrack.
  Backtracking-
  
  • Brute force approach
  • N queens problem
• After backtracking, we will be studying how Java 8 collections framework is implemented. We will be covering the following,
  
  • Set
  • List
  • Queue
  • PriorityQueue
  • Map
  • HashSet
  • TreeSet
  • ArrayList
  • LinkedList
  • Vector
  • Dictionary
  • Stack

Dynamic Programming is also an algorithmic technique for solving optimization of problems. It is done by breaking the problem down into simpler subproblems, and utilizing the fact that the optimal solution to the overall problem depends upon the optimal solution to its subproblems.

• During this session, we will be covering the following:
  
  • Dynamic programming-
    
    • The strategy of dynamic programming
    • Properties of dynamic programming
    • Approaches of dynamic programming
    • 0/1 knapsack
    • Travelling salesman problem
    • Floyd Warshall
  • In the end, we will conclude and summarize the course by having a brief discussion on
    • Greedy vs Divide & Conquer vs Dynamic
    • Programming examples

• Types of application architectures
• Comparisons between various architectures
• Introduction to Microservices
• Java Ecosystem for Microservices
• Create a simple microservices

• Understanding cloud-native app
• Traditional vs cloud-native app
• Understand 12 Factors for cloud-native app
• Sizing and boundary identification for microservices

• Overview of the app we are creating
• Create accounts microservices
• Create cards microservices
• Create loans microservices

• Introduction to containerization
• Overview of docker
• Docker image
• Deploy using images/containers
• Build packs
• Docker-compose

• Configuration management basics
• Sprint cloud configuration
• RefreshScope
• Securing config with encryption

• Service discovery and registration basics
• Spring cloud config
• Eureka server

• What is resiliency?
• Bulkhead pattern
• Circuit breaker patterns
• Retry pattern
• Rate Limiter

• Overview of routing and cross-cutting concerns
• Spring cloud gateway
• Implementing routing
• Implementing cross-cutting concerns - Logging

• Distributed logging/tracing aggregation overview
• Spring cloud sleuth for tracing
• Zipkin overview for logging
• Challenges with monitoring microservices
• Approaches to monitoring microservices
• Using Prometheus and Grafana to monitor

• Kubernetes overview
• K8s cluster
• Deployment
• Self-healing
• Logging/Monitoring of Kubernetes cluster
• Auto-scaling with HPA
• Validating deployed clusters
• Challenges with manually created Kubernetes manifest

• Understand Helm
• Using Helm with Kubernetes cluster
• Creating Helm charts for our microservices
• Some common Helm commands

• Overview of Spring MVC
• Spring MVC Architecture
• Using Spring Boot for Spring MVC Configuration
• Performing CRUD operation through a Spring Boot MVC application

• Introduction to databases
• Downloading XAMPP and creating the first table
• Components of DBMS
• Characteristics of DBMS, ACID properties
• Databases vs File system
• Types of DB
• SQL vs NoSQL
• CRUD operations example in MySQL, OLTP, and OLAP

• Database design
• ER model
• Entity and attributes
• Entity relationships
• Cardinality ratio
• Generalization, specialization, and aggregation
• Case study of Ecommerce website

• RDBMS
• Converting ER diagram to table
• Properties of relational model
• Primary key and foreign key
• Relational model
• E-commerce use case
• Codd's rules
• Data integrity in RDBMS

• Database design
• Redundancy
• Insertion anomaly
• Functional dependencies
• Closure set of attributes
• Lossy decomposition
• Dependency preserving
• Normalization
• Transitive dependency
• 3NF
• BCNF
• Multivalued dependency
• 5NF
• Comparison between Normal forms, OLTP, and OLAP

• Discrete maths
• Relational Algebra and null values
• Relational calculus
• Characteristics of SQL
• Create a DB (example)
• SQL Constraints
• Foreign key
• SQL SELECT statement
• INSERT, DELETE statements
• Scalar and Aggregate functions
• HAVING clause
• SQL Aliases
• IN, BETWEEN operators
• EXISTS, ANY, ALL operator
• Auto increment
• MySQL best practices

• JOIN-types
• JOIN examples
• String operators
• Compound queries
• SQL index
• SQL views
• Query optimization
• Security Risks

• Introduction to NoSQL
• ACID
• Key value pair-based databases
• Search DB and Graph DB
• Advantages of NoSQL
• Introduction to MongoDB

• Working with MongoDB
• CRUD Operations with examples
• Data Modelling in NoSQL
• One to One and One to Many relationships
• Advanced MongoDB queries
  • Join, sort, group, and index

• In memory DB
• Redis
• Key value store
• Cashing layer
• Datatypes
• Transaction
• Backup of data from Redis

• PL/SQL Basics
• Operators
• Record
• Recursive Functions
• Exception Handling
• Triggers

• Transactions
• Concurrency control
• Isolation
• Consistency
• ACID
• Database lock
• Database lock types
• Lock Contention
• Deadlock
• Scaling the database
• Storage systems
• Database indexing
• Index types
• Distributed DB
• DB Replication
• Database sharding
• DB on Cloud