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.
