Data Science vs. Machine Learning
Data Science vs. Machine Learning: Two Tools for Understanding Data
All businesses, no matter what size or industry, need to use and understand data in order to be efficient and successful. In our connectivity-based culture, that requires a knowledge of two new information tools that are changing the landscape of digital analysis: data science and machine learning.
Data science and machine learning are two tools that help businesses in a variety of ways: they determine which services are in demand, which buyers to target, how to interpret business reports, how to identify problems quickly and how to measure operations alignment. Supervisors who understand data science and machine learning are better able to analyze business information, ask questions and make informed decisions. They start by analyzing what data science means for amassing, transforming, reading and retrieving their data—and deciding how machine learning fits into this picture.
What Is Data Science?
Data science is a systematic approach to collecting, filtering, modeling and interpreting information that firms typically encounter, in order to solve problems or achieve a business objective. In many businesses, huge amounts of data stream into connected systems very quickly, and interpreting this raw data can be a daunting task. Data science allows managers to zero in on relevant information, integrate it with other data sources, make sense of the data and model its structure for continued use. Leaders then conclude their queries and give logical explanations for why a specific course of action is recommended.
Data science combines three disciplines—statistics, analysis and machine learning—to help solve business problems. A data scientist needs to know what questions to ask about the business in order to understand its needs. Armed with that information, they can then make a logical case for recommended actions, and share those results with higher-level managers.
A data scientist also needs to know the basics of statistics, analysis and machine learning to provide the technical background for problem-solving. Statistics, by definition, means the application of mathematical conceptions and calculations of data. People who interpret and visualize statistics understand how to create a data sample, read the implications based on sampling size and draw conclusions about the probability of an event (i.e. overdrawing on a checking account). Statistics form a subset of analysis.
Analysis, meanwhile, breaks a problem down into components and examines it in detail to identify its cause or causes. Using analysis, managers can learn what reports to construct, understand whether the output is valid or needs to be rerun, and form conclusions about what the information means. Leaders who understand analysis are able to read information and reproduce results because their methods are consistent and clear.
Statistics and analysis can overlap with machine learning, but not always. Machine learning applies computer programming during data science activities, which minimizes human intervention because algorithms master the task.
What Is Machine Learning?
Machine learning, by definition, is a subset of artificial intelligence. AI is a burgeoning field which studies and models how humans think and behave, and then applies that knowledge to computers. Algorithms that use machine learning can modify themselves based on discovered data patterns, meaning they can identify the rules in new situations and act based on this information.
A trained program requires little, if any, human intervention to complete a business task, because machine learning grows and iterates based on new information (i.e. image recognition and augmented analytics). When a machine learns through image recognition, it can deduce all kinds of information, such as if an item on a shelf is something appealing to the target audience or if a photograph can be linked with a specific person. Meanwhile, augmented analytics are used to identify customer preferences, but can also be employed to detect fraud and criminal activity. Alexa is a great example of this type of machine learning.
Managers who understand basic machine learning concepts know how to prepare and integrate quality data and algorithms that computers need for training and testing. They also can evaluate whether a program's output meets the desired objective and how to remove bias from the data sets. Furthermore, leaders can assess security risks wherever machine learning is applied. For example, they know the dangers of how data systems could be compromised, and how to avoid the resulting harmful consequences. Supervisors also can plan which business projects are best suited to machine learning and how operations can increase in speed.
Data science and machine learning continue to garner interest and momentum in the business world. Managers need to understand both to gain a competitive edge, anticipate failures and use their data as effectively and successfully as possible.
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