Data Structures & Algorithms
Think. Solve. Build.

This helps you to understand how to convert logic and write it in the form of diagrammatic and textual way.

• Built-in data types (int, float, double, char, void)
• Variable declaration, initialization, scope
• Constants (literal, symbolic with const and #define)
• Type conversion and typecasting

• Arithmetic, relational, logical, assignment, increment/decrement
• Bitwise operators and shift operators (intro)
• Conditional (ternary) operator
• Expression evaluation, operator precedence and associativity

• if, if-else, nested if-else
• else-if ladder
• switch-case and break
• Conditional constructs for simple problem solving

• while, do-while and for loops
• Loop nesting
• break and continue
• Basic algorithmic patterns (counters, accumulators, tables)

• One-dimensional arrays: declaration, initialization, traversal
• Two-dimensional arrays (matrices) and basic operations
• Character arrays and strings
• Common string operations using library functions (length, copy, compare, concatenate)

In this section, you will learn to write code in a modular way to help you reuse the code in different C++ files and make it more readable and organized.

• Need for functions
• Function declaration, definition and call
• Parameter passing (by value)

• Pointer basics, address and dereference operators
• Pointers and arrays
• Pointers to pointers
• Dynamic memory allocation using malloc, calloc, realloc, free
• Common pointer errors and good practices

STL is one of the most useful things to write code in a faster way. It comes very handy in competitive programming and online coding practice sites.

In this section, we will understand how Bitwise operators like AND, OR, XOR, and NOT work on machine code to help write programs that work faster, since the computer understands machine code.

• Defining and using structures
• Array of structures and nested structures
• Unions (concept and simple use)
• Enumerated types (enum)
• typedef for aliasing types

“In order to understand Recursion we must understand Recursion”, so this is what is focussed here i.e. write code using Recursion. It is used in Dynamic Programming as well as in data structures such as Trees, Graphs, and Heap etc.

In this section, we will start exploring how to form recurrence relations using problem statements and mainly discussing recursion on arrays and strings.

In this section, we will understand how to solve algorithms such as phone search and advanced concept of Knapsack that is mostly asked in Interviews.

It is the most important concept to understand in Recursion and it is thoroughly checked in Interviews and Technical Rounds. If one has to master recursion they need to master backtracking.

It helps us to compare the algorithms on the basis of Time and Space it takes to run the code and help us to master how to write code by considering constraints of the problem.

One of the most important topics to build data structures. It is not only used in every data structure but also helps us to understand how to associate the real world with programming language.

In this topic we learn how to store data in a non-contiguous manner and we understand how the Linked List can work in a better time complexity than other data structures in some operations.

It follows the principle of Last In and First Out. Although this data structure can be easily understood, the concept of LIFO can be used to solve some typical problems such as Histogram Area, Stock Span, and Balanced Parentheses etc.

This data structure works just like a normal queue does and it is mainly used as a helping data structure in Trees and Graphs.

In this data structure, we learn how to store data in a hierarchical format. It is one of the most important topics from an Interview point of view and questions from this topic are frequently asked in companies such as Amazon, Microsoft, Flipkart and Google etc.

This data structure helps you to understand how an infinite stream of incoming data can be accessed to fetch useful information, for example, find the most sold products on any website.

It helps us to understand how we can store, update and delete the data in constant time. It is majorly used in Dynamic Programming and Graphs to help us make the time complexities of the algorithms better.

The most popular data structures in companies like Direct-i, Google, and Arcesium etc. It is usually considered tough because of the dependency on other data structures such as queue, hashmaps, sets, and heap etc. but once you get a taste of how to apply these things together it will make you a better programmer.

It helps us to search in constant time and in case you want to use auto suggestion features of Amazon, Flipkart or other sites' search bar this is the solution. Many problems such as Maximum-Xor, and Maximum Sum etc. use tries to help improve complexity of the problem.

• One Dimensional Dynamic Programming

Understanding the logic to implement real world application like Splitwise and how it works in minimum transactions.