University of Mumbai

India · NEP 2020

University of Mumbai

48 lab practicals for the Mumbai University syllabus.

University of Mumbai’s NEP 2020 B.E. Computer Engineering: C programming, data structures, Python, algorithms and DBMS labs. Every lesson is stepped, compiled and checked by a real toolchain on your own machine — the model reviews your code afterwards, it does not decide whether you passed.

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Updated

Semester by semester

Programme: B.E. (Computer Engineering). Scheme: NEP 2020 (2024-25 onwards). Open a course to see its practical units and the lab lesson GroutCode ships for each.

  1. Semester 1

    1 course7 lab lessonsC
    • VSEC102C

      C Programming

      7

      lessons

      • Keywords, operators and formatted I/O in C
      • Decisions and loops in C — from grade ladders to goto
      See all 7 lessons
      1. 1Keywords, operators and formatted I/O in CUnit 1 · Fundamentals of C-Programming
      2. 2Decisions and loops in C — from grade ladders to gotoUnit 2 · Control Structures
      3. 3Functions, recursion and storage classes in CUnit 3 · Functions and Parameter
      4. 4Arrays, strings and Student records in CUnit 4 · Arrays, String, Structure
      5. 5Pointers in C — from swap to a linked listUnit 5 · Pointer
      6. 6File handling in C — text processing and a binary student databaseUnit 6 · Files
      7. 7C Programming lab — from factorials to a linked list and a student fileUnit LAB · Suggested List of Experiments

  2. Semester 2

    3 courses13 lab lessonsCPython
    • PCC2011C

      Data Structure

      6

      lessons

      • A List ADT on a growing array — every operation on a data structure
      • Array stacks, a shared two-stack array and expression evaluators
      See all 6 lessons
      1. 1A List ADT on a growing array — every operation on a data structureUnit I · Introduction
      2. 2Array stacks, a shared two-stack array and expression evaluatorsUnit II · Stack
      3. 3Five queues on arrays — linear, circular, priority, deque and multi-levelUnit III · Queue
      4. 4Linked lists in C — singly, doubly, circular, and the stack and queue built on themUnit IV · Linked List
      5. 5Binary search trees in C — insert, search, four traversals, delete and tree shapesUnit V · Tree
      6. 6Stacks and trees at work — parentheses, infix/postfix/prefix, expression trees and Huffman codesUnit VI · Applications of Data Structures
    • PCL2011C

      Data Structure Lab

      1

      lesson

      • Data Structure Lab — arrays, stacks, queues, lists and BSTs in C
      See the units
      1. 1Data Structure Lab — arrays, stacks, queues, lists and BSTs in CUnit LAB · List of Experiments (all practicals in C)
    • VSEC202Python

      Python Programming

      6

      lessons

      • Python fundamentals for C programmers — types, operators, formatting and the four built-in collections
      • Control flow and functions in Python — number analysers, patterns and a guessing game
      See all 6 lessons
      1. 1Python fundamentals for C programmers — types, operators, formatting and the four built-in collectionsUnit 1 · Introduction to Python
      2. 2Control flow and functions in Python — number analysers, patterns and a guessing gameUnit 2 · Control Flow and Functions
      3. 3Files, exceptions, packages and debugging — a word extractor, a city sorter and a fault-tolerant bank ledgerUnit 3 · File Handling, Packaging, and Debugging
      4. 4Object-oriented Python — a vehicle rental system with single, multilevel and multiple inheritanceUnit 4 · Object-Oriented Programming (OOP) in Python
      5. 5Regular expressions in Python — validators, extractors and the logic behind an admission formUnit 5 · Advanced Python Concepts
      6. 6Python Libraries
      7. 7Python lab experiments — from greetings and set operations to custom exceptions, a rental fleet and regex validatorsUnit LAB · List of Experiments

  3. Semester 3

    2 courses7 lab lessonsC
    • 2113113C

      Analysis of Algorithm

      6

      lessons

      • Selection sort, insertion sort and the Master method — measured, not memorised
      • Divide and conquer — max-min, merge sort, quick sort and binary search
      See all 6 lessons
      1. 1Selection sort, insertion sort and the Master method — measured, not memorisedUnit I · Introduction
      2. 2Divide and conquer — max-min, merge sort, quick sort and binary searchUnit II · Divide and Conquer Approach
      3. 3The greedy method — knapsack, Dijkstra, Prim and KruskalUnit III · Greedy Method Approach
      4. 4Dynamic programming — multistage graphs, Floyd-Warshall, 0/1 knapsack, TSP and LCSUnit IV · Dynamic Programming Approach
      5. 5Backtracking and branch and bound — N-queens to the 15-puzzleUnit V · Backtracking and Branch and bound
      6. 6String matching — naive, Rabin-Karp and Knuth-Morris-PrattUnit VI · String Matching Algorithms
    • 2113115C

      Analysis of Algorithm Lab

      1

      lesson

      • Analysis of Algorithms Lab — thirteen experiments in C
      See the units
      1. 1Analysis of Algorithms Lab — thirteen experiments in CUnit LAB · Suggested list of Experiments

  4. Semester 4

    4 courses14 lab lessonsSQLC
    • 2114112SQL

      Database Management System

      6

      lessons

      • From ER diagram to tables — a college database in SQLite
      • Relational algebra in SQL — selection to division
      See all 6 lessons
      1. 1From ER diagram to tables — a college database in SQLiteUnit I · Introduction to Database and Data Modeling
      2. 2Relational algebra in SQL — selection to divisionUnit II · Relational Model and Relational Algebra
      3. 3A college registrar database in SQL: constraints, views and triggersUnit III · Structured Query Language (SQL)
      4. 4Normalising a registrar spreadsheet from 1NF to 4NF in SQLUnit IV · Database Normalization
      5. 5Transactions in SQL: savepoints, serializability tests, 2PL, log recovery and deadlocksUnit V · Transaction Management and Concurrency Control
      6. 6Modern databases in SQL: documents, key-value, sharding, replication, objects and cloud billingUnit VI · Introduction to Modern databases
    • 2114113C

      Operating System

      6

      lessons

      • Operating system fundamentals in C: system calls, dual mode, boot, multiprogramming and real time
      • A CPU scheduler simulator — FCFS, SJF, priority, SRTF and round robin
      See all 6 lessons
      1. 1Operating system fundamentals in C: system calls, dual mode, boot, multiprogramming and real timeUnit I · Fundamentals of Operating System
      2. 2A CPU scheduler simulator — FCFS, SJF, priority, SRTF and round robinUnit II · Process Management
      3. 3Races, semaphores and the banker's algorithm — synchronization and deadlock in codeUnit III · Process Synchronization and Deadlock Management
      4. 4Allocation, paging and page replacement — a memory manager in CUnit IV · Memory Management
      5. 5Disk scheduling and file allocation — FCFS to C-LOOK, FAT chains and inodesUnit V · File and IO Management
      6. 6Real-time, distributed, mobile and cloud OS — the algorithms behind special-purpose systemsUnit VI · Special-purpose Operating Systems
    • 2114114SQL

      Database Management System Lab

      1

      lesson

      • College database in SQLite: DDL, queries, joins, a stored-procedure view, triggers and a locked seat booking
      See the units
      1. 1College database in SQLite: DDL, queries, joins, a stored-procedure view, triggers and a locked seat bookingUnit LAB · Suggested List of experiments
    • 2114115C

      Operating System Lab

      1

      lesson

      • Operating system mechanisms in C: schedulers, Banker's algorithm, memory placement, paging, disk scheduling and a semaph
      See the units
      1. 1Operating system mechanisms in C: schedulers, Banker's algorithm, memory placement, paging, disk scheduling and a semaphUnit LAB · Suggested List of Experiments

  5. Semester 6

    2 courses7 lab lessonsPython
    • 2116112Python

      System Programming & Compiler Construction

      6

      lessons

      • A hypothetical machine and its language processor: encode, decode, execute, analyse and translate
      • Two-pass assembler with literal pools, and a two-pass macro processor with keyword parameters
      See all 6 lessons
      1. 1A hypothetical machine and its language processor: encode, decode, execute, analyse and translateUnit I · Introduction to System Software
      2. 2Two-pass assembler with literal pools, and a two-pass macro processor with keyword parametersUnit II · Assembler and Macro Processor
      3. 3Loaders and linkers: absolute, relocating and direct linking loaders, and lazy dynamic linkingUnit III · Loader and Linker
      4. 4A compiler front end in Python — tokenizer, DFAs, FIRST/FOLLOW, LL(1) and LR(0)Unit IV · Introduction to Compiler & Lexical Analysis
      5. 5Syntax-directed translation in Python — parse trees, scoped symbol tables, type checking and three-address codeUnit V · Syntax and Semantic Analysis
      6. 6An optimizer and code generator in Python — folding, propagation, DAGs, peephole and Sethi-UllmanUnit VI · Code Optimization and Code Generation
    • 2116120Python

      System Programming & Compiler Construction Lab

      1

      lesson

      • System programming lab in Python — two-pass assembler, nested macro processor and linking loader
      See the units
      1. 1System programming lab in Python — two-pass assembler, nested macro processor and linking loaderUnit LAB · Suggested List of Experiments

The full 43-course syllabus is transcribed in the app, including the theory courses. Lessons are authored against the course codes that have practical work, and the list grows with each release.

GroutCode is not affiliated with or endorsed by University of Mumbai. Course codes and titles are transcribed from the institution’s published scheme documents.

How a practical runs

The same five steps for every lesson, in every language.

  1. 1

    Pick your syllabus

    Choose your university or board and the semester or class you are in. The course list is the transcribed official scheme, not an approximation.

  2. 2

    Open a lab lesson

    Each lesson is a stepped practical for a specific course, with a starter file you edit and a test file you do not.

  3. 3

    Write the code yourself

    The starter has the signatures and the TODOs. You implement them in the editor, in the language your course actually uses.

  4. 4

    Run the checks

    The toolchain compiles and runs the tests — 8 to 15 of them across the lesson, one per step, so you find out which change broke what.

  5. 5

    Get the review

    Once the tests pass, the model reads your implementation and tells you the input that breaks it. It never reports the pass or fail itself.

Why the compiler grades, not the model

The obvious way to build an AI lab tool is to let the model read the student’s code and say whether it is correct. It is also the way that produces a tool nobody can trust. A model will tell you your code works when the compiler says otherwise, and it will fail you for a style it happens to dislike.

So the two jobs are split:

  • The model designs the lesson, writes the starter and the tests, and — after the tests pass — reads what you actually wrote and tells you the input that breaks it.
  • The toolchain decides whether it compiles and whether each step passes. That verdict is not negotiable and the model never issues it.

And why steps instead of one big task

A 40-to-60 minute practical with a single Run at the end gives a beginner one bit of feedback an hour, and it arrives after every decision has already been made. Each lesson here is 8 to 15 steps, each with its own instruction and its own check, so you find out which change broke what while you still remember making it.

Frequently asked questions

Does GroutCode cover the Mumbai University NEP 2020?

Yes. GroutCode ships the transcribed Mumbai University NEP 2020 for B.E. (Computer Engineering) — 43 courses — with 48 authored lab lessons mapped to specific course codes. The syllabus was transcribed from the official documents, and the source and checksum of each one is recorded in the app.

Which languages do the Mumbai University practicals use?

C (28 lessons), Python (13 lessons), SQL (7 lessons). The language is set per course code rather than guessed from the title, because the same subject is taught in different languages at different institutions.

Does the AI just write the practical for me?

No. The starter file has the signatures and the TODOs; you implement them. The toolchain — not the model — decides whether your code compiles and whether each step passes, so the AI cannot tell you your code works when the compiler disagrees. What it does afterwards is read your implementation and point out the input that breaks it.

Do the practicals work offline?

The lessons, the syllabus and the test runner are bundled in the app and run locally, so the compile-and-check loop works with no connection. The review step at the end uses a model, which can be a local one or a cloud one with your own key.

What do I need installed?

GroutCode detects which toolchains are present on your machine and tells you what is missing and what would fix it. You need the compiler or interpreter for the language your course uses — a C compiler, a JDK, Python, Node or SQLite.

Other syllabuses

APJ Abdul Kalam Technological University

KTU

APJ Abdul Kalam Technological University

The 2024 scheme for Kerala’s technological university, covering CSE, IT, AI & DS and ECE from semester three onwards.

85

lab lessons

2024 schemeC · Java · Node.js · Python
Visvesvaraya Technological University

VTU

Visvesvaraya Technological University

Karnataka’s 2022 scheme across CSE, ISE, AI-ML, AI-DS and ECE, including the dedicated lab courses.

123

lab lessons

2022 schemeC · Python · Java · C++
Anna University, Chennai

Anna University

Anna University, Chennai

Anna University’s Regulations 2021 B.E. CSE, from Python and C programming to the data structures and OOP laboratories.

31

lab lessons

R2021C · Python · Java · SQL

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