Technology has made it easier for companies to optimize their workflow and business operations. It has allowed IT professionals to showcase their abilities and develop productive software and applications to benefit enterprises. Additionally, data structures and algorithms have become essential to modern web development models. IT professionals must be familiar with data structures and algorithms to improve their firm’s growth. In his article for Cleverism, Martin Luenendonk shares his insights on data structures and algorithms. He also talks about their benefits and applications.
Understanding Data Structure
Luenendonk defines data structure as a systematic arrangement of data that can be accessed for easy data retrieval. To implement an effective data structure, you should have enough disk space. In addition, you should have adequate operating time and skillset to execute the process.
Categorization of Data Structures
Data structures can be classified into two categories.
Primitive data structures are the ones that are created from scratch. They do not provide variations per user needs and demand high user involvement.
Abstract data structures are usually dependent on the primary data structure.
- Static or fixed data structures focus on determining the data set size in the initial phase. It allows simple and effective assessment of data when required. However, one of the negatives of fixed data structures is that they prioritize filling up the memory rather than the relevance of the data.
- Dynamic or variable data structures do not focus on filling the storage. Instead, they offer a risk of data underflow and overflow.
- Linear data structures focus on the relationship of data units with their adjacent elements.
- Non-linear data structures are attached to multiple data items simultaneously.
Abstract Data Structure
Abstract data structures can be classified into the following categories:
- Lists (linear and non-linear)
Tips to Select a Data Structure
Here is a list of steps you should prioritize while implementing data structures:
- Identify the problem
- Evaluate the resources
- Execute the required operations
An algorithm is a process of solving problems with quick decision-making skills. There are several benefits of algorithms:
- Allows management of big data
- Offers data flexibility
- Enables programmers to write efficient code
- Increases the integrity of data
Luenendonk classifies algorithms into five categories:
- Search algorithm
- Sort algorithm
- Insert algorithm
- Update algorithm
- Delete algorithm
Attributes of Algorithms
Make sure your algorithm has:
- More than 0 inputs and more than 1 outputs
- A definite termination point
- Clarity and certainty
- Adequate availability of resources
You can analyze an algorithm by two methods:
- A priori analysis
- A posterior analysis
When evaluating an algorithm’s performance, you must focus on time complexity. It specifies the total time taken by a program to complete an operation. Space complexity focuses on the memory space required to store a process.
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