Data Science & Machine Learning – How Are They Different From Each Other?

Data science and machine learning are buzzwords that we get to hear a lot these days. And although used together and at times interchangeably, they are certainly different. And machine learning is included in the umbrella of machine learning. They are vastly different with their unique tools.

Defining Data Science

Data science deals with big data and also incorporates data cleansing, preparation, and analysis. A specialist in data science will acquire data from multiple sources and then apply multiple forms of analysis to derive critical insights from the collected data. Data scientists utilize these critical insights to create prediction models and information sets that power essential decisions of a business.

Defining Machine Learning

Machine learning is the utilization of algorithms to gather data, learn from it and then create a prediction for the future for a specific topic. Traditional machine learning algorithms rely on statistical and predictive analysis that identifies patterns and derives insights from the given data set.

Facebook and Netflix are good examples of machine learning utilization. For example, Netflix recommendations or Amazon-recommended products are all a result of machine learning algorithms. They sift through the data sets of past interests and create possible predictions based on that.

The Difference Between Data Science & Machine Learning

Data science is a broad term that includes several different areas associated with data. For example, one of the associated avenues of data is machine learning which falls under the data science umbrella. However, they differ in terms of the skill sets required by experts in both fields and the exact scope in which they operate with data.

Machine learning, as the name implies, sifts through data and learns and then predicts. The entire process is automated. On the other hand, data science may or may not utilize machine learning and involve a more manual process.

Let’s take a look at how both disciplines differ in terms of the required skill sets.

Skills Needed For Data Science

An expert in data science requires skills in three main critical areas: analytics, programming, and domain knowledge. Going deeper into what are the necessary skills for a data scientist, the following skills are required.

• Programming knowledge with Python, SAS, R, Scala, and any other language as required.
• Understanding of how to use SQL database coding.
• The ability to data mine and clean said data from various sources. These data sets are usually taken these days from social media.
• A thorough understanding of various analytical tools for data analysis.

Skills Needed For Machine Learning

Machine learning is a different take on statistics. And although it is considered under the broader term of data science, it requires a different set of skills. The following skills are critical for any expert in machine learning.

• The fundamentals of computer science so that they have a solid foundation.
• The ability to create statistical models.
• Working knowledge of data evaluation and modeling processes.
• Natural language processing is an important aspect, especially regarding machine learning that is used behind services like Netflix or Amazon.
• Knowledge of data architecture design.
• Knowledge of how best to represent text.

Machine learning algorithms are as good as the programming they are working on. And with the rise in the amount of data available for analysis through social media and other digital forums. As a result, the need for machine learning experts is growing.

Another critical difference between data science and machine learning is that in data science is that there is a higher degree of exertion in terms of manual control of the processes. In contrast, machine learning is purely automated and, if not programmed well, has a higher probability of missing insights or creating inaccurate predictions.

Final Thoughts

Different people and companies have different ideas of what constitutes data science and machine learning. Or some even see them as similar or interchangeable. There is, however, a significant separation between both data science and machine learning.

And yes, some skills between the two overlap, but an expert in data science is more focused on statistics, model building, and prediction of outcomes. On the other hand, an expert in machine learning takes those models, scales them, and automates them to churn out predictions.

Both are useful in their domains and have a wide range of mobile applications. But it is only as strong as understanding the underlying nuances that result in proper utilization of either.