4 Data Structures and Algorithms All Analysts Need to Know

Traditional data marketing was a manual process. Analysts would collect information, extract useful information, and painstakingly identify trends that companies could use to improve operations.

Though the outcomes are the same, the process is made both faster and more powerful with modern technology. In particular, specific data algorithms and structures give analysts the capacity to capture data from virtually any source and extract deep insights that manual inspection would miss.

The Data Analytics and Visualization Bootcamp from QuickStart is one of the fastest ways to learn these skills. Taught by industry experts, the program helps teach in-demand data skills in a fraction of the traditional time.

Let’s explore some of the most beneficial data structures and algorithms data analysts use today.

1. Arrays and Lists 

Arrays and lists are among the most basic and widely used data structures. They allow for the storage and sequential access of data elements. Arrays are fixed in size and offer efficient indexing, while lists are dynamic and can grow or shrink as needed.

Both arrays and lists are important for the data storage process. Whether it's a simple list of numbers or a complex dataset with multiple attributes, these data structures provide a way to store and access data efficiently. Analysts also use arrays and lists when manipulating data, and structuring information for organization, recollection, and analysis.

Arrays and lists offer several advantages. These can include:

• Ease of implementation: Arrays provide a basic structure for organizing data in a contiguous memory block, making them easy to understand and use. Lists, especially in high-level languages like Python, come with a variety of built-in methods for common operations such as appending, removing, and sorting elements, reducing the need for manual coding.
• Fast access to elements: Arrays allow for constant-time access (O(1)) to elements using indices. While lists might not offer constant-time access for non-indexed operations, they still provide efficient element retrieval and manipulation through built-in methods.
• Memory efficiency: Arrays are generally more memory-efficient than lists because they allocate a fixed amount of memory at creation. This can be beneficial when working with large datasets where memory consumption is a concern.

Arrays and list systems are also known for a few drawbacks. These include the fact that both are created with a fixed size, which means the number of elements they can hold is determined at the time of their creation. Inserting or deleting elements into an array or a list can also be inefficient if elements must be shifted to maintain order.

2. Hash Tables

Hash tables (also known as hash maps) are data structures that store data in key-value pairs. They use a hash function to compute an index into an array of buckets or slots, from which the desired value can be found. This allows for fast data retrieval based on the key.

A hash table allows for one of the most efficient forms of data retrieval. Hash tables offer constant-time complexity (O(1)) for average-case insertions, deletions, and lookups. This makes them ideal for applications requiring fast access to data. Hash tables are also straightforward to implement and use, providing a simple interface for associating keys with values.

Limitations of a hash table include a finite memory overhead. Hash tables can have higher memory overhead compared to other data structures because they may allocate more memory to reduce the likelihood of collisions. They are also at risk for potential collisions, when two keys hash to the same index. Resolving collisions can introduce additional complexity and affect performance.

3. Trees

Trees are hierarchical data structures that consist of nodes connected by edges. Each node contains a value and may have references to other nodes, called children. The node at the top of the tree is called the root, and nodes with no children are called leaves. Since trees are used to represent hierarchical relationships, this type of data visualization is particularly useful for data that has a natural hierarchical structure.

Binary trees are a specific type of tree where each node has at most two children, referred to as the left child and the right child. This structure allows for efficient implementation of search and sort algorithms.

Here are a few notable benefits to tree structures in data analysis:

• Data organization: Binary search trees (BSTs) allow for efficient organization of sorted data, enabling quick search, insertion, and deletion operations.
• Hierarchical data representation: Trees are ideal for representing hierarchical data such as organizational structures, file systems, and taxonomies. Each node in the tree represents an entity, with edges defining the relationships between entities.
• Machine learning: Decision trees are widely used in machine learning for classification and regression tasks. They model decisions and their possible consequences, providing a clear and interpretable way to make predictions.

Despite notable benefits, trees are limited by memory overhead. Like hash tables, they can develop significant memory overhead compared to simpler data structures like arrays. Trees are also difficult to balance. Balancing a tree may require additional algorithms (e.g., AVL trees, Red-Black trees).

4. Sorting Algorithms

Sorting algorithms are fundamental algorithms in computer science that rearrange the elements of a list or array in a particular order, typically in ascending or descending order. The significance of sorting lies in its ability to organize data, making it easier to search, analyze, and visualize.

Some common sorting algorithms include:

• Bubblesort: Bubblesort is a simple comparison-based algorithm that repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. This process is repeated until the list is sorted.
• Mergesort: Mergesort is a stable, divide-and-conquer algorithm that divides the array into two halves, recursively sorts them, and then merges the sorted halves.
• Quicksort: Quicksort is a divide-and-conquer algorithm that selects a 'pivot' element and partitions the array into two sub-arrays: elements less than the pivot and elements greater than the pivot. It then recursively sorts the sub-arrays.

Sorting is a fundamental step in many data analysis tasks due to its ability to organize data, which enhances various data processing and visualization techniques.

Here are a few benefits to sorting algorithms:

• Enhanced search efficiency: Sorted data allows for the implementation of efficient search algorithms, such as binary search, which significantly reduces search time.
• Improved data organization: Sorting algorithms organize data into a logical order, making it easier to read, understand, and manage.
• Streamlined data analysis: Many data analysis techniques, such as finding medians, percentiles, or detecting duplicates, are easier and more efficient with sorted data.

Memory usage is a distinctive drawback of sorting​​ algorithm integration. Some sorting algorithms require additional memory for temporary storage, which can be a limitation in memory-constrained environments. Implementing efficient sorting algorithms can also be complex and error-prone, requiring a deep understanding of the algorithm’s mechanics.

Practical Applications and Real-World Examples

Data structures and algorithms are rapidly changing the way organizations collect and process data, across virtually every industry.

For instance, in the world of healthcare, IBM Watson AI helped healthcare providers sustainably scale healthcare practices behind machine learning (IBM). Whereas Siemens relies on data-powered predictive maintenance to minimize disruptions across technology (Siemens). Then you have PayPal leveraging data algorithms for automatic fraud detection (Paypal).

Organizations rely on several popular tools and algorithm libraries to improve internal efficiency and reduce errors when navigating data. Utilizing the right tools and libraries can significantly enhance the efficiency and effectiveness of implementing data structures and algorithms in data analysis projects. Mastering these tools is essential for any data analyst or scientist aiming to excel in their field.

Enhancing Analytical Skills

For analysts looking to deepen their understanding of data structures and algorithms, leveraging the right resources is crucial. Focus on understanding the fundamental concepts behind each data structure and algorithm. Knowing why an algorithm works and how it can be applied in different scenarios is more valuable than merely memorizing the steps. 

That's why QuickStart’s Data Analytics and Visualization Bootcamp is an excellent starting point. 

This comprehensive program covers essential concepts in data analytics, as a baseline for upskilling to a more thorough exploration of key data structures and algorithms within data science. The bootcamp’s industry-aligned curriculum ensures that participants gain practical, job-ready skills through hands-on projects and real-world case studies.

Get Started in Data Analysis Today

Mastering data structures and algorithms is a crucial skill for aspiring data analysts. These fundamental concepts form the backbone of efficient data processing and analysis, enabling analysts to organize, retrieve, and manipulate data effectively. 

Whether it’s optimizing search operations, sorting large datasets, or implementing complex machine learning models, a solid understanding of data structures and algorithms can significantly enhance an analyst’s problem-solving capabilities and overall productivity.

That’s where the QuickStart Data Analytics & Visualization Bootcamp can help. By enrolling in this program, you can gain a solid foundation in data analytics, learn to apply advanced analytical techniques, and earn a certification of completion partnered with a university of your choice that is recognized by employers. Speak with a live admissions advisor to take your first step — or your next step — toward a career in data today.