Course Introduction
This course is designed for placement preparation and will mainly cover data
structures and algorithms using C and C++. Even if you do not know C++, you will
still be able to follow along easily. The notes will be made available as a PDF in
the description below.
Data Structures and Algorithms
Data structures are used to arrange data in main memory for efficient usage while
algorithms are a sequence of steps to solve a given problem. In this course, we
will cover arrays, linked lists, and graphs as examples of data structures and dive
into solving problems using different algorithms.
Programming Languages
C and C++ will be the primary languages used in this course but Java can also be
used to implement the algorithms. I do not recommend Python or JavaScript for
beginners but rather suggest learning C to get a solid foundation in programming.
Conclusion
Learning data structures and algorithms is a responsibility and I will teach this
course in a way that is easy to understand for beginners. Don't worry if you make
mistakes or have trouble at first, just follow along step by step and everything
will become clear.
Data Structures & Algorithms for Placements
This course is primarily for those preparing for placements or job interviews.
Time is limited when preparing for placements, so this course is structured to
value your time. A 15-hour video on C with notes is available on the channel, which
will be covered first. If you're an advanced Java user or can program algorithms in
Python, then it's possible to do so. However, it's recommended to learn C and C++
first.
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. The sequence of RAM usage is
important when loading a program like "chrome.exe" for Windows. Fiddling with data
occurs in main memory, which must be arranged optimally using data structures to
minimize RAM usage.
The theory of databases is not covered in this course, but you should know their
basic concepts. When opening a new tab, a large amount of data is stored in a
database that must be retrieved and updated regularly. Data warehouses store data
permanently for faster retrieval and updation for analysis purposes. Legacy data
needs to be stored separately from the main system.
Sorting Algorithms
The example used here is sorting arrays in ascending or descending order. An
algorithm is a series of steps to create a process. When sorting an array, steps
must be taken to sort in ascending or descending order. The steps taken to sort an
algorithm into an array define the algorithm.
Data Warehousing and Big Data
Data is the fuel of big algorithms these days, so it's essential not to lose the
data. To prevent data loss, the data is separated from the main system and stored
in what is known as legacy data. 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. Big data is a separate field that requires a different set of
algorithms and analysis.
This course is designed for placement preparation and will mainly cover data
structures and algorithms using C and C++. Even if you do not know C++, you will
still be able to follow along easily. The notes will be made available as a PDF in
the description below.
Data Structures and Algorithms
Data structures are used to arrange data in main memory for efficient usage while
algorithms are a sequence of steps to solve a given problem. In this course, we
will cover arrays, linked lists, and graphs as examples of data structures and dive
into solving problems using different algorithms.
Programming Languages
C and C++ will be the primary languages used in this course but Java can also be
used to implement the algorithms. I do not recommend Python or JavaScript for
beginners but rather suggest learning C to get a solid foundation in programming.
Conclusion
Learning data structures and algorithms is a responsibility and I will teach this
course in a way that is easy to understand for beginners. Don't worry if you make
mistakes or have trouble at first, just follow along step by step and everything
will become clear.
Data Structures & Algorithms for Placements
This course is primarily for those preparing for placements or job interviews.
Time is limited when preparing for placements, so this course is structured to
value your time. A 15-hour video on C with notes is available on the channel, which
will be covered first. If you're an advanced Java user or can program algorithms in
Python, then it's possible to do so. However, it's recommended to learn C and C++
first.
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. The sequence of RAM usage is
important when loading a program like "chrome.exe" for Windows. Fiddling with data
occurs in main memory, which must be arranged optimally using data structures to
minimize RAM usage.
The theory of databases is not covered in this course, but you should know their
basic concepts. When opening a new tab, a large amount of data is stored in a
database that must be retrieved and updated regularly. Data warehouses store data
permanently for faster retrieval and updation for analysis purposes. Legacy data
needs to be stored separately from the main system.
Sorting Algorithms
The example used here is sorting arrays in ascending or descending order. An
algorithm is a series of steps to create a process. When sorting an array, steps
must be taken to sort in ascending or descending order. The steps taken to sort an
algorithm into an array define the algorithm.
Data Warehousing and Big Data
Data is the fuel of big algorithms these days, so it's essential not to lose the
data. To prevent data loss, the data is separated from the main system and stored
in what is known as legacy data. 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. Big data is a separate field that requires a different set of
algorithms and analysis.
