We live in the so-called Zettabyte Era, which started in the middle of the 2010s when the amount of digital data and network traffic exceeded one zettabyte, or a trillion gigabytes. That might give you an idea just how much data is created and consumed nowadays. Not to mention that this amount grows at an increasing rate, and is projected to reach 181 zettabytes by 2025.
Even though only a fraction of that data is stored for longer periods of time, the resulting data volume is still pretty intimidating. And all that information can potentially have a lot of business value, if only you can manage to extract knowledge from this ocean of data.
Multiple business organizations deal with great volumes of data in their day-to-day operations and need to make sense of it, to be able to make important decisions and plans for the future based on the collected and processed information. When it comes to analyzing big data, data science and machine learning are the two terms that are not only used most often but sometimes also happen to be mentioned interchangeably.
It is important to distinguish between these two notions though, so that you can better understand what benefits they can bring for your organization and what kind of specialists you need to achieve your specific business goals.
What is Data Science?
Data science today is a field of study that deals with analyzing data to gain meaningful insights. Typically, besides data analysis, data science processes include data preparation, data cleansing, and data visualization. The tools used in data science for processing raw data include statistical models, predictive analysis, and machine learning algorithms, which already hints at the relationship between data science and machine learning.
Data science is a broader term that includes such subsets as machine learning, data mining (applying different techniques to the database search), and data analytics, another term that is often used interchangeably with data science. In fact, data analytics focuses on the analysis of structured data, critical to a certain enterprise, that is collected from primary and secondary sources, cleaned and organized. The purpose of the analysis is to solve tangible business problems by using discovered trends and patterns. Data science, on the other hand, deals with raw data, algorithms and predictive model design, and building tools for data analysis and visualization, which are used to extend knowledge, propose new business values, and make predictions for the future. Here you can learn more about the differences between data science and data analytics.
Example of Data Science
Let’s take buying a laptop online as an example. When you select a specific laptop, you see the site recommending you some other devices, like earphones or a mouse, that other users who purchased this laptop before bought together with it most often, or it can offer similar models of laptops that might have better functionality. Data science is behind the complete process of gathering user data, preparing the data for evaluation by filtering and cleaning it, looking for patterns and trends, then creating a model that is used to recommend some product to other users, and optimizing the resulting data.
How is Machine Learning different?
Machine learning focuses on how to get computers to learn to solve problems without being explicitly programmed step-by-step, and as such is a subset of artificial intelligence. Machines learn by applying algorithms to large volumes of historical data. There are three types of learning methods: supervised, unsupervised, and reinforcement (by trial and error), each with their own sets of algorithms. There is also a set of learning algorithms called Deep Learning that uses neural networks similar to the human brain structure. The purpose of learning is to get the machines to perform human-like tasks. The algorithms allow machines to build models that predict certain behaviors based on specific data sets.
What is Machine Learning
Machine learning is a branch of computer science and a part of statistics, but since these methods are used to automate data analytics, it is also a subset of data science. More specifically, machine learning algorithms are used in the data modeling step of the data science lifecycle, so they are just one of the tools that a data scientist uses.
Machine Learning algorithms
Typical machine learning algorithms are able to find patterns in data, but that is not the full extent of their capabilities. The ML algorithms can solve various problems with data classification, data segmentation, find relationships between specific data features and predict target values (regression), forecast time-dependent data, learn optimal decisions strategies and recognize speech and texts, just to mention a few of the most important capabilities.
Benefits of Machine Learning
With machine learning there is no need for a human to program every step of implementing complex algorithms, and the machine can also further optimize its work as it learns, so this presents a faster and more efficient way to process extremely large data sets. Compared to the techniques used in traditional statistical analysis, machine learning allows us to create data models and algorithms that work differently than traditional tools, which can sometimes bring better results.
Example of Machine Learning
If we go back to the example of buying a laptop online, machine learning algorithms are responsible for creating the model that will be used to recommend other products to you based on the purchases other users have made.
Practical application of Data Science vs. Machine Learning
To better understand where we might encounter data science and machine learning in everyday life, it is important to keep in mind that there is a lot about Data science besides machine learning.
Data Science in practice
Data science uses different types of analytics, such as prescriptive analysis, predictive analysis, and descriptive analysis, to solve various problems and business-related tasks. For example, the US sports industry extensively uses the data science paradigm to find new players for sports teams by tracking their history of games, metrics, results, achievements, analyzing their development, discovering trends, and predicting their future potential. Another application is in the financial sector, predicting global trends in stocks or currency rates. Data analysis is also used to create improvement plans for businesses, plan airline routes, and ensure curriculum effectiveness in education.
Machine Learning in practice
Machine learning algorithms and the data models that they build are applied to solving more practical tasks like email SPAM filtering, image processing and recognition in medicine, recommendation systems used for social networks and streaming services, and so on. In general, machine learning tools can be used in any area where data science is applied, in the data modeling step of the data science process.
Data Science vs. Machine Learning - which one to choose?
If you need to solve a certain problem, it might be wrong to say that you have to choose data science or machine learning, one or the other. It is the scope of your task that will define the exact processes and tools that need to be engaged.
If you need to predict, for example, how popular a new book might become to determine if a publisher should accept it, that would be a part of data science. It would involve analyzing book sales by genre, ROI projection, determining the right price and so on. The focus would be on processing lots of domain-specific data to make conclusions that can help to get value for the publishing business.
Certain stages of this grand project, like ROI projection, can require a machine learning algorithm. The algorithm will focus on learning what revenue the books of a certain genre or of a certain author usually bring, taking into account general book sale trends, etc. The resulting model would be one of the elements used to draw insights concerning the financial potential of publishing a new book.
Data Scientist vs. Machine Learning engineer
Both the professions of data scientist and machine learning engineer are among the most sought after right now and can be pretty lucrative. Whether you are thinking of getting into a career like that yourself, or you are just considering what specialists you need in your organization for a specific project, it is crucial to understand the difference between these professions to make the right choices.
Typical responsibilities of Data Scientists
Although there may be a certain overlap in what data science experts and ML engineers do, primarily their responsibilities are different. A data scientist works on a more global level, creating new solutions or taking existing models and optimizing them for certain business problems that can be solved using machine learning tools in an organization or a project and choosing the right data sets that can yield the most powerful insights.
The work of a data scientist involves creating algorithms and models that will be used for a particular problem-solving case. Data scientists develop data annotation strategies, build custom tools for the modeling workflow optimization, and communicate with various stakeholders, for example, when they need to identify the exact data sets for the analysis or explain the findings to maximize the business value.
Typical responsibilities of Machine Learning engineers
ML engineers typically perform practical tasks based on the preparations done by the data scientists. They deploy machine learning and Deep Learning models to production, and then monitor the performance of the deployed model.
Machine learning engineers can create custom tools to optimize the deployment process, and also optimize the model itself to achieve better performance. There are also other tasks like inference testing, data model version control, and so on.
So, it is possible to say that data scientists create solutions, while ML engineers take these solutions to production. Typically, data scientists have the bigger responsibility, and while they can perform the same tasks as ML engineers, more often ML engineers provide the support for the data scientists’ work, especially at the ML model’s deployment stage. Usually, there are multiple machine learning algorithms that can be used for a specific purpose. Making experiments on the same data using different algorithms that need different computation infrastructure is a key problem an ML engineer can help to solve
Skills required for Data Science
Specialists in these two fields have to master multiple technical skills, however a lot of them might overlap as well. Data scientists, depending on their specialization, usually need to know how to collect data and visualize data, and besides that learn advanced mathematics, statistics, programming languages like R/Python, big data tools like Hadoop, and SQL database coding, etc. Some soft skills could also be useful due to the amount of collaboration and communication they typically have to do.
Skills required for Machine Learning
Machine learning engineers should be proficient in applied mathematics, software development, statistical modeling, data evaluation, algorithms application, natural language processing (e.g. sentiment analysis), text representation techniques, but, again, it depends on the exact project they have to deal with.
Since both data science and machine learning offer multiple career paths, you also need to take that into consideration when deciding what to study or looking for team members with specific skills. In the data science field there are not only data scientists, but also data architects, data analysts, business intelligence developers, and other professionals.
Machine learning offers the opportunity not only to become a machine learning engineer but also, for example, a software engineer with a focus on machine learning, natural language processing scientist, etc.
Conclusion
We often encounter the question, which is better, data science or machine learning. The answer is either or both at the same time can be used in different cases. Data science is a broader term that covers not just using the machine learning algorithms for processing and analyzing data, but also data visualization, integration, engineering, and making business decisions based on the insights gained as a result. Machine learning is just one of many technologies that data scientists use in their analysis and insight-gathering workflow.
Both machine learning and data science also have certain limitations. For instance, machine learning techniques can work effectively only with rather large data sets and correct data. Models built on small data sets and incorrect data can turn out a waste of time, and the results of the analysis will be meaningless. The quality of data is important for data scientists as well, since it directly affects the recommendations that they can make for your organization based on their analysis. Furthermore, machine learning algorithms that can be applied to get great results in situations where they are called for, shouldn’t be used just because artificial intelligence is a hot topic right now. There can be cases when machine learning algorithms can needlessly complicate the workflow, while using some readily available software would be enough.
It is important to know the difference between data science and machine learning so you can always choose the right specialists and tools for the particular problem that you need to solve. Artificial intelligence is not yet capable of autonomously deciding what problem it should work on solving, but data scientists can use it to get the answers they need and extract valuable meaning from big data. The resulting insights can bring priceless business value for your organization, or even make the world a better place. The potential of both data science and machine learning is clearly extraordinary.
