Next Steps

We hope that after completing this course, you have gained an in-depth understanding of tries as a data structure and concept. If you wish to enhance your knowledge even more, we encourage you to look at the following data structures, which will help you further increase your technical understanding. 

Learn data structures

A data structure is a storage technique to store and organize data. It's a method of arranging data on a computer to be accessed and updated efficiently.

Apart from storing information, a data structure is also used for processing and retrieving data. As a result, basic and advanced data structures are used in all software systems. 

Data structures can be categorized as follows:

• Linear data structure: In this case, data elements are arranged sequentially or linearly, and each element is attached to its previous and next adjacent elements. Arrays, stacks, queues, and linked lists are examples of linear data structures.  

• Static data structure: This has a fixed memory size, making it easier to access its elements. Arrays are a classic example of static data structure. 

• Dynamic data structure: In this case, the size is not fixed and can be randomly updated during runtime. This is considered to be efficient in terms of the code's memory (space) complexity. Examples of this data structure are queues and stacks.

• Nonlinear data structure: In this case, data elements are not placed sequentially or linearly, and we cannot traverse all the elements in a single run. Trees and graphs are nonlinear data structures. 

Learn advanced data structures

Advanced data structures are one of the important disciplines of data science. They are used for storing, organizing, and managing data to make it more efficient and easier to access and modify. They're the foundation for designing and developing efficient and effective software and algorithms. A skilled programmer must know how to create and construct a decent data structure. With the rise of new information technology and working practices, their scope is expanding.

Many algorithms' efficiency and performance depend on the data the particular algorithm uses for calculations and other data operations. In addition, data structures perform the basic operation of storing data during the runtime or execution of a program in the main memory, making memory management another important concept to understand since it directly affects the space complexity of a program or algorithm. So choosing an appropriate data structure plays a vital role in a program's efficiency and performance.

Examples of advanced data structures are:

• Hashset

• Hashmap

• Tries

• Binary Indexed Trees 

• Fenwick trees

• Suffix trees

Learn algorithms

Algorithms are instructions used by computers to solve a given problem to meet an end goal. To learn algorithms, it's essential to have an understanding of the basics, such as:

• Time and space complexity analysis

• Big O notation

• Recursion

• Data structures like arrays, queues, stacks, queues, graphs, trees, and so on

Time and space complexity analysis is a critical topic to learn to analyze algorithms. With a grip on time and space complexity analysis, you can move into more advanced algorithms. 

Learn system design and build projects

System design defines the architecture, interfaces, and data for a system that satisfies the specified requirements. System design involves meeting business needs through reliable and efficient systems. Once your organization defines the requirements, you can build them into a tangible system design that addresses your customer's needs. 

The system design provides a systematic approach to building and engineering computer systems. A well-designed system takes care of everything from infrastructure to hardware and software to the data and how it’s stored. In addition, you can learn more about using various data structures while designing a system. 

So go ahead and build a project from end to end, using all these disciplines: algorithms, data structures, system design, and coding.