SC1007: ace the component, not just read the notes
Your complete guide to Nanyang Technological University's data structures and algorithms course. See where the marks are, work real practice questions, and study with an AI tutor that knows SC1007.
Sia generates SC1007 practice questions, walks through introduction and linked lists step by step, and quizzes you on the material the component that weights most heavily.
Find what is wrong
You are asked to delete a node from a singly linked list in C, given only a pointer to the node to delete, with the list holding more nodes after it. A student writes:
void deleteNode(Node *target) {
free(target);
}
The list is corrupted afterwards. What is the correct approach?The bug is not the free itself. It is that the previous node still points at the memory you just released, leaving a dangling pointer and a broken list.
The standard trick copies the successor's data into the target node, so the target now holds the next node's value, then unlinks and frees the successor: target->data = target->next->data; Node *tmp = target->next; target->next = tmp->next; free(tmp);
The observable list is now correct. Note the one case this does not cover: if the target is the last node, there is no successor to copy from, and the deletion genuinely requires access to the previous node.
The trap: Freeing a node without repairing the incoming link. Because C does not stop you, the program often appears to work for several more operations before it crashes somewhere unrelated, which makes this one of the hardest bugs in the course to trace back to its cause. When a lab submission fails scattered test cases rather than all of them, a dangling pointer is the first thing to check. classic slip!
Overview
What SC1007 is, and where it sits
SC1007 is the data structures course in the NTU computing core, and it is taught in C. That choice matters more than it might sound: the course deliberately puts you close to memory, starting with static versus dynamic allocation, heap management and run-time memory protection before any structure is built on top.
From there it builds node-based structures in order: linked lists, then the abstract data types implemented on top of them (stacks, queues, priority queues), then trees, including traversal orders, expression trees and AVL balancing. The second half turns analytical, covering algorithm design strategies, complexity analysis with recurrence relations and asymptotic notation, then searching and hashing, and finally graph representation with breadth-first and depth-first traversal.
The course sits at a structural hinge in the NTU computing curriculum. It takes SC1003 as its prerequisite and is itself the prerequisite for SC2001 Algorithm Design and Analysis, so weaknesses here surface again a semester later rather than being left behind.
Always treat your own course outline and the exam timetable as authoritative.
Difficulty & time commitment
Is SC1007 hard, and how much time does it take?
SC1007 is manageable if you keep a weekly rhythm and treat the back half as the main event. The pattern is consistent: it starts gently and steepens, and the heaviest assessment is the part that separates grades.
The difficulty curve and the assessment weighting point the same way: the back half is harder and worth more. Front-loading effort there is the highest-return decision in the course.
Is this course for you
Who tends to do well, and who tends to struggle
You will likely do well if
- You are comfortable with pointers, or willing to become comfortable quickly. The course assumes C from the first topic.
- You test your own code against edge cases before submitting, since marking is automated and unforgiving.
- You can hold both sides of the course at once: implementing a structure and reasoning about its complexity.
- You treat the analysis topics as first-class rather than as theory to skim before a quiz.
You may struggle if
- You learned programming in a language that manages memory for you and expect the same here.
- You submit ambitious code that does not compile. The rubric scores that at the floor no matter how good the idea was.
- You avoid recurrence relations. Topic 6 needs them, and Topic 7 and Topic 8 assume the analysis habits Topic 6 builds.
- You leave lab work to the last evening. Debugging pointer errors under time pressure is where this course does most of its damage.
- Build a small test harness of your own for each lab, including empty, single-element and boundary cases, and run it before submitting. The automated marker rewards exactly this.
- Draw the pointer diagram before writing the code for any list or tree operation. Almost every list bug is visible in the diagram and invisible in the code.
- For every structure you implement, write down the complexity of each operation as you go, so Topic 6 is consolidation rather than new material.
- Implement BFS and DFS from scratch once without reference. Being able to reproduce them cold is worth more than recognising them.
Syllabus
The 8 topics, topic by topic
The exam-weight marker on each topic shows where the marks concentrate. The amber topics carry the highest exam weight.
T1 · Introduction and dynamic memory allocation
Topic 1Static versus dynamic allocation, heap management and garbage collection, run-time memory protection, and an overview of node-based structures. In C this is where most early bugs are born.
T2 · Linked lists
Topic 2Singly, doubly and circular linked lists, their implementation in C, and the problems they are the right answer to. Pointer discipline established here carries through the whole course.
T3 · Abstract data types and their implementation
Topic 3Stacks, queues and priority queues built on linked lists, with two canonical applications: evaluating arithmetic expressions with a stack, and scheduling jobs with a queue or priority queue.
T4 · Tree structures
Topic 4Moving from linear to hierarchical: binary versus general trees, pre-order, in-order and post-order traversal, expression trees, and AVL balancing.
T5 · Introduction to algorithms
Topic 5What an algorithm is, the important problem types in computing, and the design strategies that SC2001 later develops in depth.
T6 · Analysis of algorithms
Topic 6Time and space complexity, best, worst and average case, recurrence relations for recursive algorithms and how to solve the elementary ones, big-O, big-Omega and big-Theta, and the standard complexity classes.
T7 · Searching
Topic 7Exhaustive search, iterative and recursive sequential search, binary search with its invariant and complexity, then hashing with linear probing and double hashing as collision strategies. Every algorithm arrives with its asymptotic analysis attached.
T8 · Graph representations and searching
Topic 8Adjacency lists and adjacency matrices, systematic traversal with BFS and DFS, and a generic backtracking algorithm with its complexity, applied to the eight-queens and maze-search problems.
How it's assessed
Assessment structure
A component-by-component weighting breakdown is not published for this course. Rather than estimate one, we publish only what the course itself states. Check your current course outline for the exact percentages.
The published course outline for SC1007 available publicly is an AY2020 document. Its weighting is not asserted here as current, because a weighting table from a superseded academic year is worse than no weighting table. What the outline does establish, and what has not changed in the course's design, is that the mark is built from lab assignments, lab tests and quizzes rather than from a single written final. Check your own current course outline for this semester's weights. Lab assignments and lab tests are submitted as code and marked by an automated system against test cases. The published rubric scores code that compiles and passes every test case at the top, allocates partial credit in proportion to the test cases passed, and places code that fails to compile at the bottom. Written quizzes are marked in the same manner as examinations.
How to actually pass it
A weekly rhythm, two checklists, and the traps to avoid
The course rewards consistency over cramming, and practice over re-reading. Here is the loop that works, then what to have nailed before each exam.
The weekly loop
Before the mid-semester checklist
- Dynamic allocation, and what happens to memory you free but still point at
- Linked list insertion and deletion, including the head and tail cases
- Stacks and queues implemented on lists, plus expression evaluation with a stack
- Tree traversal in all three orders, and why the order changes the output
Before the final heaviest topics
- AVL balancing, including which rotation applies to which case
- Complexity analysis from first principles, including deriving and solving a recurrence
- Binary search with its invariant, and hashing with linear probing and double hashing
- Adjacency list versus adjacency matrix, and the complexity consequence of choosing each
- BFS and DFS implemented and analysed, plus backtracking on eight-queens or maze-search
The mistakes that cost marks
Freeing a node without fixing the incoming link. The previous node still points at released memory. C will not stop you, so the program frequently keeps running and fails later somewhere unrelated, which is what makes this bug expensive.
Submitting code that does not compile. The published rubric scores non-compiling submissions at the bottom regardless of the approach. Always keep a compiling fallback version saved before you attempt the more elegant solution.
Confusing a structure's average and worst case. Hashing and binary search trees both look fast on average and can degrade badly. Quizzes test exactly the case where the average-case intuition breaks.
Learning traversals as names rather than as code. Pre-order, in-order, post-order, BFS and DFS are all easy to recognise and harder to write correctly under time pressure. Lab tests ask you to write them.
Formula & concept sheet
The vocabulary and formulas you must own
- Dynamic memory allocation
- Requesting memory from the heap at run time rather than fixing it at compile time, and taking on responsibility for releasing it.
- Dangling pointer
- A pointer that still refers to memory that has been freed. The source of most hard-to-trace bugs in this course.
- Linked list
- A sequence of nodes each holding data and a link to the next; variants include doubly linked and circular lists.
- Abstract data type (ADT)
- A structure defined by its operations rather than its implementation, such as a stack, queue or priority queue.
- Stack
- Last in, first out. Used in this course to evaluate arithmetic expressions.
- Queue and priority queue
- First in, first out, and its ordered variant where the highest priority element is served first. Used for job scheduling.
- Tree traversal
- Systematic visiting of every node: pre-order, in-order and post-order, each producing a different sequence from the same tree.
- AVL tree
- A binary search tree that rebalances by rotation to keep its height logarithmic, preserving worst-case performance.
- Recurrence relation
- An equation expressing a function in terms of its value on smaller inputs; the standard tool for analysing recursive algorithms.
- Big-O, big-Omega, big-Theta
- Asymptotic upper bound, lower bound and tight bound on the growth of a function.
- Binary search
- Halving a sorted range each step, giving logarithmic time; its correctness rests on an invariant that must be maintained.
- Hashing and collision resolution
- Mapping keys to table positions, with linear probing and double hashing as the strategies used when two keys collide.
- Adjacency list and adjacency matrix
- The two standard graph representations; the choice changes the complexity of traversal and of edge lookup.
- BFS and DFS
- Breadth-first and depth-first traversal, the systematic ways to visit a graph and the basis for many later algorithms.
- Backtracking
- Searching a space by extending a partial solution and retreating when it cannot be completed, as in eight-queens and maze-search.
Common acronyms: {'term': 'ADT', 'def': 'Abstract data type'} · {'term': 'BFS', 'def': 'Breadth-first search'} · {'term': 'DFS', 'def': 'Depth-first search'} · {'term': 'AVL', 'def': 'Adelson-Velskii and Landis, the self-balancing binary search tree'} · {'term': 'AU', 'def': 'Academic Units, the NTU credit measure'} · {'term': 'ILO', 'def': 'Intended Learning Outcome'}.
Where it fits
Prerequisites, related courses & why it matters
Prerequisite: SC1003 Introduction to Computational Thinking and Programming. SC1007 is in turn the prerequisite for SC2001 Algorithm Design and Analysis.
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FAQ
Frequently asked questions
What language does SC1007 use?
C. The course starts from dynamic memory allocation and heap management, and every data structure is implemented with explicit pointers, so C fluency is part of what is being assessed.
How is SC1007 assessed?
Through lab assignments, lab tests and quizzes rather than a single written final. Programming submissions are marked by an automated system against test cases: code passing every case scores at the top of the rubric, partial passes earn proportional credit, and code that does not compile scores at the bottom.
What is the prerequisite?
SC1003 Introduction to Computational Thinking and Programming. SC1007 then serves as the prerequisite for SC2001 Algorithm Design and Analysis.
Which topic causes the most trouble?
Linked lists and the ADTs built on them, because pointer errors in C do not announce themselves. A program with a dangling pointer often runs for a while before failing somewhere unrelated to the actual mistake.
Is SC1007 more about coding or analysis?
Both, in roughly equal measure. The first half implements structures; the second half analyses algorithms, including recurrence relations and asymptotic bounds. Students strong at one and weak at the other tend to find the course uneven.
How much does non-compiling code cost me?
A great deal. The published rubric places code that fails to compile at the lowest score regardless of the quality of the approach, so submitting something simpler that compiles and passes some test cases beats submitting an ambitious solution that does not build.
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