Data Visualization Principles and Web Implementation
Visualization Fundamentals
The ability to analyze data, process it, extract value, visualize it, and communicate it is an extremely important skill, given the ubiquitous availability of data today. The primary goals of visualization include:
- Recording information.
- Analyzing data to support reasoning, such as confirming hypotheses (e.g., John Snow’s work during the London Cholera Outbreak in 1854).
- Communicating ideas to others.
Visualization functions effectively by addressing the fundamental limitations
Read MoreData Structures and Algorithms Concepts Explained
1. Algorithm Fundamentals
Definition and Characteristics of an Algorithm
An algorithm is a structured step-by-step procedure designed to solve a specific problem efficiently and correctly.
- Input Requirement: It may accept zero or more input values that provide necessary data for producing meaningful results.
- Output Requirement: It always produces at least one definite output representing the final answer of the computation.
- Finiteness Property: Every valid algorithm must complete execution after a limited
Sorting Algorithms, Inversion Counting & Complexity
Hybrid Merge-Insertion Sort
[2-1 Hybrid Merge-Insertion Sort]
Stops recursion when a subarray size ≤ k and uses insertion sort for that subarray; MERGE is unchanged.
Pseudocode
INSERTION_SORT(A, p, r)
for j = p+1 to r
key = A[j]
i = j - 1
while i >= p and A[i] > key
A[i+1] = A[i]
i = i - 1
A[i+1] = key
HYBRID_MERGE_SORT(A, p, r, k)
if r - p + 1 <= k
INSERTION_SORT(A, p, r)
else if p < r
q = floor((p + r) / 2)
HYBRID_MERGE_SORT(A, p, q, k)
Algorithm Analysis and Design: Key Concepts Explained
Q1. Explain the significance of Asymptotic Analysis in algorithm design.
Asymptotic analysis helps in evaluating the efficiency of an algorithm by measuring its time and space requirements as the input size grows large. It allows comparison of algorithms independent of hardware, programming language, or machine implementation. By using notations like Big-O, Big-Ω, and Big-Θ, it focuses on growth rate rather than exact execution time, helping designers choose scalable and efficient algorithms.
Q2.
Read MoreData Structures Concepts: Lists, Dictionaries, Trees, Graphs
Lists
Ordered (different from sorted) and linear.
Duplicates allowed.
Implementation: Array list: good for iteration, resizable; add = slide to right, remove = slide to left to close gap. Linked list: nodes & pointers, no shifting, grows dynamically, good for lots of insertions/deletions.
Core operations: add(newEntry), add(pos, newEntry), remove(pos, newEntry), replace(pos, newEntry), getEntry(pos), contains(entry), toArray(), getLength – numberOfEntries, isEmpty(), clear(). Helper methods: ensureCapacity,
Data Structures and Algorithms Code Snippets
Data Structures and Algorithms Implementations
Merge Sort Implementation
The mergeSort method recursively divides the array and then merges the sorted halves.
public static void mergeSort(int[] array) {
int length = array.length;
if (length <= 1)
return;
int middle = length / 2;
int[] leftArray = new int[middle];
int[] rightArray = new int[length - middle];
int j = 0;
for (int i = 0; i < length; i++) {
if (i < middle)
leftArray[i] =