10 Tips. Are Data Science and Machine Learning the Same Thing?

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10 Tips. Are Data Science and Machine Learning the Same Thing?

The terms data science and machine learning are often thrown around together, but as the gurus over at runrex.com will tell you, these two are not the same. One of the reasons why most people have been asking if data science and machine learning are the same thing is because data science includes machine learning, and, therefore, whenever data science is brought up, machine learning is never far behind. However, the two are not the same thing, and this article, with the help of the subject matter experts over at runrex.com, will look to highlight why with the following 10 tips highlighting differences between the two.

Definitions

It is important to differentiate the two by defining them individually, after which you will realize they are not the same thing. As discussed over at runrex.com, data science is a field of study that aims to make use of a scientific approach to get meaning and insights from data. On the other hand, with the same also being discussed over at runrex.com, machine learning refers to a group of techniques used by data scientists that allow computers to learn from data and produce results that perform well without the programming of explicit rules.

Differences in the measurement of performance

Another aspect that shows that the two are not the same thing is when it comes to the measurement of performance, with a detailed write-up on the same to be found over at runrex.com. Here, in data science, performance measures are not standardized, which means that they change from one case to another. On the other hand, in machine learning models, performance measures are clear and set in stone and as such, each algorithm will have a measure to show how well or bad the model describes the training data given.

Differences in visualization

Visualization of data is another area where data science and machine learning differ, something discussed in detail over at runrex.com. In data science, generally, data is represented using any of the popular graphs such as bar graphs, pie charts, line graphs among others. On the other hand, in machine learning, as highlighted over at runrex.com, visualizations also involve a mathematical model of training data.

Differences in development methodology

As the subject matter experts over at runrex.com will tell you, data science projects have clearly defined milestones, and resemble engineering projects in this regard, with established milestones being ticked off along the way until the project is completed. This is another area where data science and machine learning differ because, in machine learning, projects take a more research-like approach, where they start with a hypothesis that you will try to prove with the data available to you. The differences in development methodology show that the two are not the same thing.

Differences in languages used

Another area that shows that data science and machine learning are not the same thing is when it comes to the programming languages used. When it comes to data science, the most commonly used programming languages are SQL and SQL-like syntax languages such as HiveQL among others covered over at runrex.com. On the other hand, Python and R are the most used programming languages in the machine learning world.

Skillset required

Given the differences between the two, particularly in the programming languages used, another aspect showing that the two are not the same thing is in the skillset required in each of them. When it comes to data science, as explained over at runrex.com, some of the skills required include domain expertise, strong SQL knowledge as well as knowledge on data profiling, and many others. On the other hand, some of the skills required when it comes to machine learning include a strong understanding of mathematics as well as an understanding of Python or R programming among others.

Differences in system complexities

The differences in system complexities when it comes to the two also goes to show that they are not the same thing. On one hand, in data science, the complexity comes in the fact that there are lots of moving components as well as due to the components for handling unstructured raw data coming in. On the other hand, in machine learning, as discussed over at runrex.com, the major complexity is with the algorithms and mathematical concepts behind it as well as the fact that ensemble models usually have more than one machine learning model, with each having weighted contribution on the final output.

Differences in scope

There is also a clear difference in scope as far as data science and machine learning are concerned, enough to show that the two are not the same thing. In data science, as the gurus over at runrex.com will tell you, the scope is to create insights from data dealing with all manner of real-world complexities. In machine learning, however, the scope is to accurately classify or predict outcomes for new data points by learning patterns from historical data with the help of mathematical models. The two have two very different scopes.

Differences in hardware specifications

This is yet another area showing clearly that data science and machine learning are not the same thing. This is because, in data science, horizontally scalable systems are preferred. After all, they are needed to handle massive amounts of data as explained over at runrex.com. When it comes to hardware, data science also places a bigger importance on the RAM and SSDs used to help overcome I/O bottlenecks. On the flipside, in machine learning, GPUs are preferred because of the intensive vector operations that come into play here.

Differences in input data

This is another aspect that shows data science and machine learning are not the same thing. This is because, in data science, input data is generated as human consumable data, which means that it is to be read and analyzed by humans, like say images for example. In machine learning, however, input data will be transformed specifically for algorithm use, with the addition of polynomial features being an example, with a more detailed write-up on this to be found over at runrex.com.

From the above discussion, it is clear that data science and machine learning are not the same thing, and you can get more information on this broad topic by visiting the amazing runrex.com.