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Table of Contents
- Data Science
- Deep Learning
When doing a search for data science versus deep learning, the results are surprising. Most of the articles that show up are comparing data science to machine learning, which is of course useful, but not as relevant as comparing it directly to deep learning. With that being said, that is the purpose of this article — to compare, directly, these two popular fields of study. While there are comparisons out there, I wanted to give my professional comparison from my experience — hence, the opinion label of this article. Keep on reading below if you would like to find out why these two fields are different, and what makes them similar.
To start, data science is incredibly general — which is perhaps the biggest difference between it and deep learning. Another way to think of this comparison is that deep learning is actually a part of data science. With that being said, then, what is the rest of data science? The main concept of data science is to make informed decisions from data. However, I do believe the general definition needs to be refined.
If I were to summarize data science in one phrase or new definition with more specification, it would be the following (of course, this definition is not completely applicable to every case/job, but from my personal experience):
- A data scientist is a business-oriented professional who unveils a problem that can be solved with a dataset by training past data in an algorithm to predict a future target, automatically
I would say that most definitions out there are too similar to that of a data analyst or business analyst. They do share a lot of the same skills and goals, however, data science usually requires programming in languages like R or Python, as well as the use of algorithms. But, there is one important part of this definition that is algorithms. This part of the definition is where deep learning comes in, it is a part of data science, and more specifically, is a part of machine learning.
To gain a better sense of what a data scientist does on a daily basis, or project-ly basis, here is a summarized outline of steps to expect:
- Understand the business — what it does, what its problems are, what data it has, who its customers are
- Analyze data or work with a business stakeholder, like a product manager, for example, to see what specific problems there are that can ultimately be solved by data science
- Define the goals of the project like improving upon common KPIs (Key Performance Indicators)
- Build a dataset from a single source or multiple sources
- Identify features that you want to test and include in your data science model
- Understand and identify if you need a regression algorithm or classification algorithm, etc, are you predicting a continuous target or a binned target/category?
- Conclude final model with sharing results, and ultimately deploying automatically into the business
As you can see, a data scientist leads the way of the solution to a problem using an algorithm(s) that uses data. Most of the time, a data scientist is also trying to automate something that was manual beforehand.
Now that we have gone through what a data scientist does, and what data science usually is, with examples, let’s go into what deep learning is. Deep learning is a facet of data science, also under the facet of machine learning algorithms (I say algorithms because I think a lot of jobs out there that are labeled ML Engineers, for example, are really software engineers who are helping to deploy an algorithm — as in the operations surrounding the algorithm and final model). What makes deep learning unique is its implementation of layers in artificial neural networks. You tend to choose a deep learning specific algorithm when you are working with large data with GPUs involved, as a lot of data science models can surprisingly reap the benefits of using just traditional machine learning algorithms.
Whereas traditional machine learning algorithms include Decision Trees, Random Forest, XGBoost, and so on, deep learning is more specific and includes algorithms like:
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Long Short Term Memory Networks (LSTM)
So, when compared to a data scientist, a deep learning engineer actually might be the same thing. Most of the time, a data science role can include deep learning specific requirements within it, so the day-to-day process is very similar to that of a data scientist. The main difference is the unique deep learning algorithms used, and possibly more thought into costs, efficiency, since these algorithms can be more complex and costly.
As we compared these two popular fields and jobs, I hope you have gained a better sense of what makes them similar and what makes them different. There is still more to be said about the tools used and knowledge required for these fields, but they are in general, they are really just a part of one another. You can be a data scientist who performs deep learning, or you can be a deep learning engineer specialist.
To summarize, here is the key takeaway from this article:
* You can be a data scientist who performs deep learning* Or, you can be a deep learning engineer specialist
I hope you found my article both interesting and useful. Please feel free to comment down below if you agree or disagree with my explanation of these two popular fields. Why or why not? These can certainly be clarified even further, but I hope I was able to shed some light on the main differences, and the common mistakes between interpreting them, especially in reference to job titles. Thank you for reading!
Please feel free to check out my profile, Matt Przybyla, and other articles, as well as reach out to me on LinkedIn.
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