CIT237

Programming and Algorithms

Sciences
200 level
3 credit units
138 pages
21 units

This course introduces the methods and concepts of programming and the role of algorithms in programming. It covers programming concepts such as basic data types, algorithms, performance analysis, fundamental data structures, P, NP and NP-Complete Problems and some sorting algorithms. The course aims to equip students with the basic knowledge of writing efficient programs through the use of concise and efficient algorithms.

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About this course

Difficulty: Intermediate
Study hours: 150 hours
Maths: Intermediate
Content: Theoretical, practical, problem solving
Practical work: Yes

Before you start

Basic computer literacy
Fundamental mathematics

How it is assessed

Assignments
Tutor marked assignments
Final examination

What you'll read

The real module and unit structure of CIT237, taken from the course material NOUN publishes.

Module 1 · INTRODUCTION TO PROGRAMMING AND ALGORITHM

Module 2 · PERFORMANCE ANALYSIS OF ALGORITHMS

Module 3 · SORTING AND SOME SPECIAL PROBLEMS

What you should be able to do

Define an algorithm and its characteristics
Explain the program development life cycle
Discuss the concept of order of growth
Develop algorithms to perform basic sorting
Evaluate the performance of sorting algorithms

What it prepares you for

Careers

Software Developer
Data Analyst
Systems Analyst
Database Administrator
Computer Programmer

Where it is applied

Software Development
Data Science
Information Technology
Financial Services
Healthcare

Tools

C
C++
Java

Where it gets hard

The units students slow down on, and what makes each one heavy.

Module 2: Performance Analysis of Algorithms
Unit 4: P, NP and NP-Complete Problems
The unit requires a solid understanding of mathematical concepts and notations, including limits, asymptotic notations, and algorithm analysis techniques.
Module 3: Sorting and Some Special Problems
Unit 3: Quick Sort
Understanding the underlying logic and implementation details of the quicksort algorithm can be challenging due to its recursive nature and partitioning process.

A suggested way through it

Suggested

13 weeks, about 91 hours in total. Yours will differ.

Week 1Module 1: Introduction to Programming and Algorithms
- Unit 1: Introduction to Programming · 4 hours
  Read the introduction to programming.. Understand the different levels of programming languages.. Outline the conventional features of programming languages..
- Unit 2: Programming Concepts · 3 hours
  Explain program development cycle.. Outline the principles of a good programming style.. Understand the program execution stages..
Week 2Module 1: Introduction to Programming and Algorithms
- Unit 3: Algorithms · 4 hours
  Explain what an algorithm is.. Differentiate between computational problems and algorithms.. Outline the characteristics of an algorithm..
- Unit 4: Basic Data Types · 3 hours
  Outline the data types, which include numeric and non-numeric data types.. Outline the constituents of an integer, real-numbers, character data type and string data type..
Week 3Module 1: Introduction to Programming and Algorithms
- Unit 5: Fundamental Data Structure · 4 hours
  Explain data structures and give related examples.. Outline the different types of data structures.. Explain the different types of linear data structures..
- Unit 6: Practical Exercise I · 3 hours
  Develop a working algorithm and a corresponding flowchart for the algorithm..
Week 4Module 1: Introduction to Programming and Algorithms
- Unit 7: Fundamental Data Structures · 4 hours
  Explain the operations of the different types of data structures.. Differentiate between trees and graphs..
- Unit 8: Exercise I · 3 hours
  Develop an algorithm and a flow chart for the algorithm..
Week 5Module 2: Performance Analysis of Algorithms
- Unit 1: Performance Analysis Framework · 7 hours
  State the criteria for estimating the running time of an algorithm.. List the efficiency attributes of an algorithm.. Describe how time efficiency of an algorithm is measured..
Week 6Module 2: Performance Analysis of Algorithms
- Unit 2: Order of Growth · 7 hours
  Explain order of growth.. Explain the different asymptotic notations..
Week 7Module 2: Performance Analysis of Algorithms
- Unit 3: Worst-case, Best-case and Average-case Efficiencies · 7 hours
  Explain the three methods that are used to measure the efficiencies of algorithms.. Explain how to identify basic operations within an algorithm..
Week 8Module 2: Performance Analysis of Algorithms
- Unit 4: P, NP and NP-Complete Problems · 7 hours
  Distinguish between a polynomial and non-polynomial problem.. Understand some basic issues in algorithm time efficiencies.. Understand P, NP, NP –complete problems..
Week 9Module 2: Performance Analysis of Algorithms
- Unit 5: Practical Exercise II · 7 hours
  Practice estimating the non time growth of an algorithm..
Week 10Module 3: Sorting and Some Special Problems
- Unit 1: Introduction to Sorting and Divide-and-Conquer Algorithm · 7 hours
  Explain the meaning and significance of sorting.. Explain the meaning and practical understanding of divide and conquer algorithms..
Week 11Module 3: Sorting and Some Special Problems
- Unit 2: Merge Sort · 4 hours
  Give the practical meaning of merge sort.. Present the merge sort algorithm.. State the performances of the merger sort algorithm..
- Unit 3: Quick Sort · 3 hours
  Evaluate the procedures involved in a quick sort algorithm.. Transverse array using the quick sort algorithm..
Week 12Module 3: Sorting and Some Special Problems
- Unit 4: Binary Search · 7 hours
  Explain how the binary search operates.. Explain how the binary search is used to traverse an array of elements.. Explain the algorithm of the binary search..
Week 13Module 3: Sorting and Some Special Problems
- Unit 5: Selection Sort · 4 hours
  Describe how selection sort operates.. Explain how selection sort is used to transverse elements in an array.. Describe an algorithm of the selection sort..
- Unit 6: Bubble Sort · 3 hours
  Explain how bubble sort operates.. Explain how bubble sort is used to transverse array of homogenous elements.. Describe the algorithm of bubble sort..

Preparing for the exam

What to do

Review all tutor-marked assignments and their solutions to understand key concepts and problem-solving techniques.
Create concept maps linking different sorting algorithms (Merge Sort, Quick Sort, Bubble Sort) and their time complexities.
Practice writing pseudo-code for common algorithms like binary search and quicksort without referring to the course material.
Focus on understanding the differences between P, NP, and NP-complete problems and their implications for algorithm design.
Allocate time to practice coding the algorithms discussed in the course, focusing on efficiency and correctness.
Study the examples provided in the course material and try to solve similar problems on your own.
Create a study schedule that allocates sufficient time for each module and unit, and stick to it.
Join online forums or study groups to discuss challenging concepts and problem-solving strategies with other students.

Questions students ask about this course

What is CIT237 about?

How many units does CIT237 have?

CIT237, Programming and Algorithms, has 21 units across 3 modules, over 138 pages of course material. You can read it one unit at a time.

How many credit units is CIT237?

CIT237 carries 3 credit units, at 200 level in Sciences.

Is CIT237 hard?

CIT237 is rated intermediate level, with intermediate mathematical content. It is mostly theoretical, practical and problem solving work, and it has a practical component.

How long does CIT237 take to study?

About 150 hours of study, spread across its 21 units.

How is CIT237 assessed?

CIT237 is assessed by assignments, tutor marked assignments and final examination.

What do I need before starting CIT237?

Basic computer literacy Fundamental mathematics

What can I do with CIT237?

Software Developer, Data Analyst, Systems Analyst, Database Administrator and Computer Programmer.