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Introduction to Data Structures & Algorithms
Course Introduction
This course is designed for placement preparation and will mainly cover
data structures and algorithms using C and C++
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The notes will be
made available as a PDF in the description below
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In this course, we will cover arrays, linked lists, and graphs as
examples of data structures and dive into solving problems using
different algorithms
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I do not recommend
Python or JavaScript for beginners but rather suggest learning C to get
a solid foundation in programming
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Don't worry if you make mistakes or have trouble at first, just follow
along step by step and everything will become clear
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Time is limited when preparing for placements, so this course is
structured to value your time
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If you're an

advanced Java user or can program algorithms in Python, then it's
possible to do so
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Data structure is an arrangement of data in main memory, which refers
to RAM (Random Access Memory) of 2, 4, 8, 16, or 32 GB
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exe"
for Windows
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The theory of databases is not covered in this course, but you should
know their basic concepts
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Data warehouses store data permanently for faster retrieval
and updation for analysis purposes
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Sorting Algorithms
The example used here is sorting arrays in ascending or descending
order
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When
sorting an array, steps must be taken to sort in ascending or descending
order
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Data Warehousing and Big Data
Data is the fuel of big algorithms these days, so it's essential not to lose
the data
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Data warehousing,
on the other hand, deals with how to store legacy data efficiently in
different types of algorithms, analysis, and distributed systems that can
handle huge databases that regular applications or algorithms cannot
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It's essential to understand data warehousing and big data, though they
are beyond the scope of this course
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The best way to learn data structures and
algorithms is to study C programming, specifically stacks and heaps
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Memory Layout of a C Program
The stack frame or activation record of the main function is always
created first, and subsequent activation records of other functions such
as fun1 and fun2 are created when called
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To better understand memory usage,
think of it as "looking back at your memory" and "reading your memory
back from the page" to see what you need to access
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Understanding Heap Memory
When writing code, variables are stored in memory in a space called the
stack
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However, in
addition to the stack, there is also a space called the heap, which can be
used to allocate memory dynamically through the use of pointers
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In
C, you can use the malloc function to request dynamic memory in
the heap, while in C++, you can use the new operator
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Heap Memory
The heap is a large pool of memory in your computer's RAM that can be
allocated using a pointer
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Time and Space Complexity
Efficient use of memory is an important part of writing algorithms and
data structures
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Time and space complexity are both
important considerations when designing algorithms
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In this video, we will
discuss asymptotic notation and how it can help us evaluate the
efficiency of different algorithms
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Asymptotic Notations
If you want to compare algorithms, you need to use asymptotic
notations
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Understanding notations like big O, omega, and theta becomes crucial
when it comes to exams or interviews
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By the end of this video, you'll have
a clear understanding of these three notations
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It's represented by the capital letter O and refers
to the upper bound of an algorithm's time complexity
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In English, we have the concept of Big O notation
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We say

that f(n) is O(g(n)) if there exists a constant c and a constant n such that
0 ≤ f(n) ≤ cg(n)
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Imagine a
graph with function f(n) on the y-axis and n on the x-axis
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This is what we
mean when we say f(n) is O(g(n))
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A function f(n) is said to be O(g(n)) if there exist a
pair of constants (c) and (n0) where the following condition applies:


n ≥ n0



0 ≤ f(n) ≤ cg(n)

Essentially, this means that the growth rate of f(n) is no greater than the
growth rate of g(n)
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A constant n-node is defined as follows:


0 ≤ f(n) ≤ cg(n)

Algorithm Definition and Life Events
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Algorithm Definition: An algorithm is a set of instructions designed
to solve a particular problem or accomplish a specific task
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

Testing and Debugging: Once an algorithm is developed, it must
be tested and debugged to ensure that it works correctly
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Maintenance: Algorithms must be maintained over time to ensure
they continue to work correctly and efficiently
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The best case scenario is the outcome that would occur if everything
goes perfectly according to plan
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The expected case
scenario is the outcome that is most likely to occur
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

Worst Case Scenario: Outcome that occurs if everything goes wrong
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Definition of Log
The logarithm, or log, is a mathematical function that gives the number
of times a certain number must be multiplied by itself to produce a
given number
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For example, log 2 (8) = 3, because 2 multiplied by itself three times
equals 8
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How to Calculate Time Complexity of an Algorithm +
Solved Questions (With Notes)
Tricks to Simplify Time Complexity
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Understand the basics of data structures and algorithms
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

Consider the worst-case scenario for the algorithm
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

Use big O notation to express the time complexity
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It's
important to consider the worst-case scenario for the algorithm, as well
as any loops or recursive functions it may contain
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By mastering the tricks outlined above, you can gain a better
understanding of time complexity and become more confident in
analyzing and solving related problems
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This means you drop whatever non-dominant
terms you have
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I am currently writing a simple for loop in the C programming language
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To understand what it does,
follow these steps:
1
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2
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3
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4
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5
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When given a problem, it's important to break it down into smaller
parts
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Another variable, k,
is being manipulated within the loop
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Does this operation depend on the value of n ? No, it does not
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