Two of the hottest topics in IT are data science and machine learning. Although these terms are related, they refer to different fields of study and practice.
- Data science is broader, encompassing many techniques to extract value from information.
- Machine learning (ML) is more specific and only pertains to one category of data analytics.
Let’s look at some examples to clarify the differences. For instance, machine learning enables computer vision, in which the system gradually gets better at recognizing objects in its camera view. A similar ML application is natural language processing (NLP) — the computer learns how to discern words and phrases from speech and text. Search engines refine their results with machine learning, and generative chatbots use ML to fine-tune their conversational abilities.
Because data science is a more general term that encompasses machine learning, these examples fall within its purview as well. However, data science also uses a number of techniques that are not considered part of machine learning.
For example, a data scientist may investigate a financial institution’s records to identify plausible cases of fraud. Or you could conduct statistical analyses of sales data to determine which weather maximizes revenues. These cases can be handled with techniques not involving ML, like calculations and data visualizations created by an expert.
Despite the overlaps, there are also important differences. Machine learning only applies to programs that improve through feedback rather than explicit instructions. This represents many use cases within data science. However, the fact that data science uses a range of other statistical methods to derive value for business gives it an edge.
Data Science: The Art and Science of Data
Data science is a multidisciplinary approach to generating returns from the massive amounts of data now in circulation. It largely rests on statistical foundations, incorporating scientific techniques to isolate useful pieces of information from the background noise.
Sophisticated data science tools collect and process data from a broad array of sources. Information can come from web pages, financial transactions, medical devices, hardware sensors or practically any other input. The analyzed data can then be visualized to help managers and employees. Results may also connect through software interfaces with other applications to automate business processes.
Pros and Cons of Data Science
Data science improves your ability to discover patterns and trends in data, which bolsters decision-making. Furthermore, these powerful analytical methods not only address intricate issues but also foster the development of innovative products and services.
But these advantages also come with challenges. To use statistical techniques effectively, you must find and prepare the relevant data for analysis. You must also identify the right business questions to ask. After finding a solution, you then have to communicate the results with stakeholders.
And throughout the process, it’s imperative to ensure data security and privacy. Data science is an art built for the present data age. But as with any activity of social importance, an organization must exercise responsibility.
Robust Data Analytics
Companies are increasingly relying on data analytics to handle the unprecedented volumes of available information. Industries such as manufacturing, finance, healthcare and telecommunications have all become data-dependent. By applying scientific thinking, businesses can better handle customers, supply chains and risk.
For instance, you could infer optimal maintenance times or forecast popular products. Data analytics will optimize the value of your structured and unstructured data, offering answers you couldn’t find before. A strategic technology provider like Growth Acceleration Partners (GAP) can help you deploy data science solutions effectively.
Machine Learning: The Power and Potential of Data
As with data science, machine learning puts data to use for businesses. Machine learning is a subset of artificial intelligence (AI) in which computers are taught to perform a process. It’s somewhat akin to how humans are educated.
Instead of programming specific steps to calculate, as in a conventional computer application, a flexible mechanism for attempting a task is implemented. Then, the software undergoes training to become more skillful at the task over time. There are several varieties of training, such as supervised by humans versus unsupervised — each appropriate for certain circumstances.
Data is at the core of machine learning, since the algorithms practice on data to refine their abilities. After the ML system has worked through training data, it can apply the calibrated algorithms to business data. The software then makes predictions or decisions on its own, such as estimating your company’s sales for the upcoming quarter.
Automating data analysis and decision-making will cut the financial costs and time of these processes. Instead of having to slog through mountains of data with manual tools, the machine learning algorithms quickly and accurately deliver results.
You can also expand on ML tools to develop new capabilities and applications. For example, a healthcare company could develop a diagnostic solution that automatically classifies plausible diseases based on patients’ symptoms or medical images. This would cut the time and expense for doctors to do their work.
Implementing Machine Learning
More businesses exploit machine learning each year, but it’s not that easy to create. The difficulties in developing machine learning solutions include selecting the right algorithms and models for the task.
In addition, there are many different neural network types and other techniques within ML software, and they each vary in terms of performance on each task type. Knowing which approach to use can be challenging.
It’s also necessary to ensure data quality and quantity. If a machine learning application is trained on insufficient amounts of data or poor-quality data, it won’t perform well — no matter how expertly the code is developed.
By the same token, it’s important to avoid feeding bias and errors into the system, as that will impact the outputs. For instance, a model trained on a non-representative customer population may fail to extrapolate to other populations.
Finally, after running a machine learning model on business data, it falls on the organization to apply the results. If the ML program is making forecasts, these forecasts should be explained to managers. In other cases, the ML outcomes automatically feed into other systems, such as a machine learning data extraction app that automatically updates an enterprise database.
The Relationship Between Data Science and Machine Learning
Data science is a superset of machine learning — i.e., it encompasses machine learning as one of its methods among many. Both approaches may use cluster analysis, data mining and other techniques. Unlike machine learning, however, data science also involves aspects such as data engineering and visualization that put the business information in context.
Let’s look at an analogy. Consider “medications” (as data science) versus “psychiatric medications” (as machine learning). All psychiatric medications are medications, but not all medications are for psychiatric use. Psychiatric medications are just one piece of the puzzle, suitable for a specific category of needs. Similarly, machine learning is just one category of techniques for certain business needs.
The machine learning branch of data science focuses on building systems that make their own inferences. They can figure out how to do some tasks that until now have required costly and error-prone manual labor.
ML techniques — and, by extension, data science techniques — often rely on fields of knowledge like computer science, math and statistics. The solutions use this type of logic to understand which data is relevant to your organizational situation. While data science emphasizes extracting meaning from data, machine learning emphasizes employing data to improve performance or make predictions.
Machine learning algorithms train on data taken from data science. The information is found in sources relevant to the business, then used by ML to develop its abilities. For example, a data scientist may locate a repository of information on loans and economic data. A machine learning model could then be trained on the data to estimate under what conditions people ask for loans. On this basis, a financial institution could optimize the loan rates it offers in response to unfolding economic conditions.
The Edge of Data Science Over Machine Learning
As the larger field, data science has an edge over machine learning. In fact, data science holds the advantage not just in size but also in versatility and applicability. It addresses a wider range of problems than machine learning alone can resolve.
Data science has become a major part of several industries, including energy, manufacturing, retail, finance and healthcare. But machine learning has a narrower role in classifying specific outcomes.
For instance, an ML algorithm may classify insurance company leads into quality levels or make other straightforward classifications. Basically, machine learning translates between clearly defined inputs and outputs. By contrast, data science can make extensive contributions to insurance companies, such as risk calculations, price optimization, customized product development and claim analysis.
Data science can also adapt to varied platforms and domains that don’t necessarily share the same characteristics. This methodology works on diverse infrastructure and caters to more organizations’ requirements. On the other hand, machine learning often uses specialized hardware like graphics processing units and tensor processing units that constrain its use.
You can apply data science techniques that complement machine learning results, expanding the realm of possibilities. Where ML may recommend a service or predict sales, data science extends to address more complex business opportunities through domain knowledge, strategy, communications and other disciplines working together.
The Future of Data Science and Machine Learning
Data science and machine learning have already become central tools in business, but they’ll continue to grow in the future. As more companies invest in these powerful technologies, they’ll become increasingly feature-rich.
More and Better Data
Expect to see more available and diverse data sources and types. As more of our lives go online and the physical world becomes connected by Internet of Things (IoT) sensors, future data will make today’s “big data” look puny.
Factories, retail stores, hospitals, banks and many other environments will emit streams of data requiring analysis. Data science and ML techniques will rise to the occasion by processing these new inputs.
More Data in the Cloud
Cloud computing continues to expand too, which will further increase the demands on data science and machine learning. With more organizations completing their cloud migrations, it will become imperative to manage the data there effectively.
Data science’s tools will help meet this goal while simultaneously benefiting from greater cloud resources to accelerate its calculations.
Easier-to-Use Tools and Techniques
In addition to using more computational resources, data science and machine learning will advance due to the development of new algorithms and models for complex tasks. As a result, hardware and software will team up to make these techniques more efficient.
More platforms will support ML, and easy-to-use tools will allow more people to contribute to data analysis.
What’s more, businesses of the future will have to navigate increasingly treacherous duties to keep up with an intricate economy. Think of the number of companies that will sell into highly competitive international markets. Products and services will become more complex, and their client bases will grow. This will call for teams to leverage data science models that the whole company can use, unlike today’s siloed model builders.
Better Collaboration With Machines
With data becoming more deeply embedded in the economy, we should also expect to see a closer merging of man and machine. The ongoing integration of human intelligence and creativity with AI will unleash the kind of innovation that drives businesses beyond what we can now accomplish.
Finally, people will interact more fluently with ML models to teach machines new ways of thinking and speed up human actions. Automation or “operationalization” will help standardize best practices. And data science will inform business decisions more comprehensively than now feasible — with a heightened awareness of security commensurate to the newfound importance of data.
Grasp the Future of Data Science With GAP
Data science and machine learning are racing ahead to improve the performance of businesses. While machine learning enables computer programs to become capable of new feats through rehearsal, data science is the overarching field that includes ML and other techniques.
As the more general term, data science has an edge over machine learning for long-term growth. However, both will develop together to enable more productive business activities. By harnessing computer science and mathematics, these technologies identify useful patterns from the huge amounts of data confronting companies now and in the coming years.
The world-class data scientists and engineers at Growth Acceleration Partners can help you manage the challenges of data science and machine learning. GAP simplifies development and boosts productivity by converting data into valuable insights. Our expertise covers AI, predictive modeling and analytics. And GAP teams have experience applying their knowledge to industries ranging from finance to healthcare and beyond.
Contact GAP today to take your next step toward the future of data science.
