Whether you’re an aspiring machine learning practitioner or a to-be data scientist, core Computer Science topics might haunt you for a while.
Throughout your journey in the dark alley of machine learning, you might often question yourself, how important are data structures and algorithms for machine learning and data science?
First, let’s go over the difference between the two, and we’ll gradually climb our way to the hot question: the role of data structures in machine learning.
On one hand, you’ll be implementing solutions to real-world problems and create software that requires minimal human interaction. That’s Machine Learning (and quite an understatement here). But you get the memo – it’s going to be intensive on algorithmic thinking and devising solutions.
On the other hand, you’ll be working on tons of data and generate insights and visualize information from the lot. In simpler words, that’s Data Science. This where you’ll need some optimization logic and making sure you’re capable of handling that amount of data.
Mind you – the difference is often neglected on several forums yet always remains. One’s about minimizing interaction, the other’s about extracting meaningful data. You do the math!
Question being, do data scientists need algorithms? Or, perhaps, you might have thought: are data structures used in Artificial Intelligence?
We’ll find that out next
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How Important Are Data Structures and Algorithms For Machine Learning: Let’s Find Out!
There’s this sentence that I often heard being thrown around mercilessly:
“I seem to have used this library on my data-set, and that worked fine.”
I hate to say this here, but the functions and the libraries that you’ve just picked aren’t made for your problem. Simply put, your data and the problem at hand is unique. It requires a specific thought process and the application of new and improvised algorithms to solve it.
Let’s pick machine learning first and see how important are data structures and algorithms for machine learning.
Also, I have previously written an article on How To Learn Data Structures And Algorithms Online. You might want to check it out to get started.
Is Data Structures and Algorithms Required for Machine Learning?
One-word answer – yes!
Let’s say you’re thinking of a way to cluster your data, or perhaps you’re generating a series of Artificial Neural Networks to create accurate predictions. How are you going to approach your problem if you don’t have an understanding of how the computer perceives it?
Here’s the thing:
A simple application is one thing. Understanding it as a whole and really trying to ace the problem is another. This is how you’ll actually optimize a bad application – using algorithms and appropriate structures.
There are two ways you might want to look at data structures for machine learning:
- Implementation – understand the internal operations of the structures and storage patterns
- Operation – only go through the working and functionality without regards to the internal implementation
Let’s go through a bunch of data structures and see how you’ll be using them:
- Array – These are the most straightforward yet most efficient in Linear Algebra, the critical part of ML, especially the usage of vectors and matrices.
- Linked Lists – these long lists of nodes are excellent at parsing your data and has excellent applications in piping
- Binary Trees – these are the powerhouse structures when it comes to automatic sorting
- and others like Stacks, Heaps, etc.
You’ll often be creating custom data structures. These aren’t recipes from a pre-made box; instead, you’ll design a solution based on these essentials, which will optimize your problem.
The same thing is true for algorithms!
Designing an algorithm that’s both optimized and efficient is key to solving a practical problem. Let’s say you want to find a prediction that’s both accurate and precise? The answer lies in the selection of a variety of ML algorithms available to you – Regression, Classification, etc.
Is Data Structures and Algorithms Important for Data Science?
When we talk about Data Science – it’s not just algebra or pure mathematics. It’s a mixture of Statistics and Computer Science. That’s precisely why the cheeky algorithms can snoop their way in and make things so much easier.
You know what’s coming:
Statistical principles go with the essentials of computer science (that’s your classic building blocks, algorithms, data structures) to draw on code. If you’re a champ in utilizing algorithms and think about the problem algorithmically, you’re already halfway through.
There’s another question that’s doing the rounds on data science forums:
What are the algorithms used in data science?
We’re discussing how important are data structures and algorithms for machine learning and data science – but we don’t consider the actual algorithms? No can do monsieur.
Here’s a list of the most commonly utilized algorithms by Data Scientists every day:
- Search and Aggregations – Data won’t often be perfect, and you’ll have to make-do. Wrangling and transformation of data require searching algorithms. Binary searches and simple searches are the two most common options
- Sorting – Data is only going to make a mark if it’s sorted in some order. For that, you have options like Quicksort and Merge sorts. But why are they good choices? Say hello to the Big-O notation!
- and more sophisticated algorithms like least square and polynomial fitting – a sophisticated algorithm used to solve a problem by minimizing the sum of squares of deviation for some variables
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Similarly, utilizing the right set of data structures to structure your test data properly is how you’ll ace at solving your problem. For example, if your data revolves around being sorted, what structure has the least complexity and functions great with huge data?
Optimization of your solutions is essential. For that, you have to understand the concepts of data structures and algorithms, along with the implementation of them. Once you’re able to identify how an algorithm can be applied, you’ll have a much deeper insight into how you design your complex AI solutions.
I hope this article has given you insight into how important are data structures and algorithms for machine learning and data science. Though you’ll often come across the exact opposite of my viewpoint – they’re not necessary.
As an engineer or an ML practitioner, your task isn’t to start jotting down the problems and write chunks of code to solve them. I’m sure by now you have a stern idea as to what will actually save you time and cost – applied computer sciences backed my structures and algorithms!