Machine Learning vs. Data Science
Aug 21, 2020 ● 7 min read
Machine learning and data science seem to be one of the main buzz words within a corporate environment. Furthermore, these two are closely linked together, so it’s not uncommon that someone can mix up what machine learning engineers do and what data scientists do. Since this is the case, it will be useful to examine these two terms and shed some light on their roles.
To give a general distinction, data science deals with research, development, and interpretation of analytical tools. In contrast, machine learning deals with putting those findings into practice or producing algorithms by data science. In other words, data science is a more boring field, and machine learning is one of the critical components within that field. To do both terms of justice, we will do an in-depth analysis and comparisons.
Table of Contents
What is data science?
Data science is a broad concept, and it’s closely connected to the big data concept. It consists of gathering, formatting, clearing, processing, and analyzing data. This is done to collect or extract crucial information that can be used as intelligence in decision making. So, a data scientist needs to gather and compile data from multiple sources, and apply machine learning, along with different means of data analysis to get predictions that are as accurate as possible.
To expand on the previous statements and give a more detailed insight regarding data scientists, here are some of the most crucial skills that are required:
- Strong knowledge or background in Scala, R SAS, and Python, or to put it more broadly modern coding languages;
- Extensive experience with SQL database;
- Understanding of many different analytical functions;
- Knowledge on how to work with unstructured or raw data from multiple sources (e.g., social media, e-commerce sites, videos, etc.);
- Understanding how machine learning works.
The role comes with a lot of responsibilities, and it is indeed demanding, which is why teams of data scientists are pretty significant.
What is machine learning?
Machine learning is an integral part of data science as it consists of practices, tools, and algorithms used for extracting data, learning from it, and predicting future outcomes. Meaning, crucial components of machine learning software are statistical and predictive analysis algorithms that can identify patterns and capture hidden inputs based on what they recognize.
Machine learning is present in our everyday lives, whenever you are suggested friends on Facebook, or videos on YouTube, or when e-commerce platforms try to sell you products you might be interested in. All of that information is based on your interaction history or activity on those websites. Platforms compare you and other users with similar purchasing or activity patterns to identify your possible future interest. Even if you ignore the offered content, you are sending signals or data that are used and processed further.
To become a machine learning expert or engineer, you will need the following set of skills:
- Expert knowledge in coding and different coding languages;
- Data modeling;
- Data evaluation;
- Knowledge of statistics and probability;
- It understands the fundamentals of how computers function.
As a machine learning engineer, you will likely combine different software and write algorithms that need to meet the specs of the leading data scientist.
Which is better data science or machine learning?
This is a tough question to answer as machine learning is an essential part of data science, but at the same time, it has other applications. The roles do have several similarities, knowing how to code and understanding different coding languages like Python, for example. That being said, there are some differences, like the routes people take to become either data scientists or machine learning engineers.
Even if machine learning is a part of data science, that doesn’t mean that you need to be a machine learning engineer to advance to data scientists. A data scientist will likely focus on mathematics, statistics, and the actual science behind data analysis. In contrast, machine learning engineers will most likely focus on software engineering and acquiring machine learning degrees and certificates.
It depends on personal preference if you are attracted by the notion of deciphering vast chunks of data to acquire meaningful intelligence that can be put to use immediately. Data science is a way to go. On the other hand, if you are intrigued by the possibility of creating a complex AI that can perfectly mimic human behavior, or build various systems that can respond and interact with social users, then machine learning will be more appealing.
Is machine learning necessary for data science?
This is an interesting question, as data science or studying data was possible even before we had machines. That being said, doing data science without machine learning would be extremely tedious and unproductive nowadays. Perhaps a better question is, can you be a data scientist, without a background in machine learning? To which the simple answer is yes, but we should also expand on that.
Technically you don’t need to know how to code, create software or algorithms to work in a field of data science. However, you will be more a data analyst than a data scientist in that sense. Data analyst is still a vital position, especially in a corporate environment, and many data analysts rely on data visualization tools, rather than create their software to do the job.
Take a tool like Whatagraph, for instance. It’s made for those who work on data analysis, and it does not require you to have any knowledge of coding, or developer background for that matter. The system easily integrates with other platforms or data sources, which you will later use to do the analysis. Moreover, you get to white label the docs, create impressive charts or graphs, and get relevant feedback, and KPI reports. So, the work you produce is not in any way lackluster, because you don’t know how to code.
Bear in mind, though, that data science and machine learning are meant to be used together, as machine learning can generalize knowledge using various data inputs. Meaning that without data, machines cannot learn, whereas without tools, we can still study and analyze data, although it will be way more tedious. Additionally, if you wish to advance in the field of data science, you will have to take a deep dive into machine learning since one of the most relevant skills is assessment or evaluation of machine learning software and algorithms.
Is machine learning computer science or data science?
Now that we covered whether machine learning is a prerequisite for data science, let’s see if data science is a prerequisite for machine learning. This should also help us answer the question of whether machine learning belongs to the computer science field, or is it more closely connected to data science.
As we all know, machine learning is most commonly associated with the creation of AI, or the ability for machines to perform tasks without the need for those tasks to be programmed. In other words, can you create a tool that can learn from data and perform pattern recognition? As this is the case, data science is an absolute must for machine learning. That being said, we cannot push machine learning further and increase its utility, without computer science. There are multiple machine learning algorithms, and machines need to be able to apply those complex mathematical calculations in the shortest time frame possible. We can only facilitate that by improving the code, and by improving hardware to give machines more computing power or increase the speed at which data is transmitted.
One of the best examples of how far we have come with machine learning is self-driving cars, created by Google. This AI has to do so many different things. Know how to navigate to the desired destination, know how to adapt depending on the traffic environment, and, most importantly, take human error into account, as traffic accidents occur as a result of a mistake. In those cases, the car needs to decide how to protect the owner without endangering anybody else. Without immense processing power and the ability for data to flow back and forth unobstructed, the success of such a project is not possible, and that is why computer science is also an integral part of machine learning.
Another application is AML software, which once again relies on pattern recognition, but also cross-referencing the available data in multiple databases. Financial crimes and corruption are one of the biggest problems of today, and having a reliable fraud detection system is truly valuable. Clearing international payments takes more time, but with a quality tool that can analyze patterns and maybe translate the scan documents, the payment processing time will be significantly reduced.
Lastly, creating a reliable translation software is also a massive challenge for machine learning, as taking lexicology, morphology, and syntax of one language and accurately transfer it to another with different sets of rules is not a cakewalk. Moreover, there are phrases, idioms, proverbs, or other elements that are closely related to culture, and that don’t always have an equivalent in the different languages.
Evolution of machine learning and data science - Deep learning
Deep learning is a final stage of machine learning, as it has to do with creating an AI that can imitate the human brain when it comes to pattern recognition and decision making. It uses a neural network to solve problems, apply learning algorithms, and even process unstructured data.
An example of deep learning is Google’s AlphaGo, as it goes beyond pattern recognition. The system was constructed to learn a board game called Go by analyzing rules and strategies. However, the AI was able to beat top-ranking players as it was able to identify new moves and strategies. Another example is Deep Mind’s AI for the video game StarCraft 2m which is already better than 99,8% of players. It once again can come up with strategies on its own and adapt based on the opponent’s decision.
Data science, along with machine learning, is gaining massive momentum in the 21st century and are without a doubt going to shape the future of our civilization. We cannot help but wonder how far these disciplines will advance within the next ten years.