Today’s business world is increasingly data-driven, with modern companies turning to large volumes of digital information to support corporate operations and guide decision-making. Amazon, for example, offers a compelling example of how data can be used to successfully target consumers — and maximize sales. The company pioneered the use of so-called recommendation engines, which suggest products to shoppers based on their purchase and browsing history, as well as on purchases made by others with similar buying histories. It’s thanks in some part to such cutting-edge and profit-maximizing innovations that Amazon has become the success it is today.
The recommendation engines spearheaded by Amazon rely on the use of big data. The term “big data” refers to data sets that are so complex and large that traditional data processing tools cannot handle them. Advances in information and computer technology make it easier than ever to amass and store large quantities of data, much more so than was possible in the past. However, efficiently and securely searching, analyzing, updating, transferring, and visualizing that data poses a range of whole new challenges.
Tech-savvy professionals, such as machine learning engineers and data scientists, are needed to take on the rapidly expanding world of digital transformation and problem-solving. Although both of these individuals work with big data, their precise focus areas and day-to-day responsibilities vary. This article helps explain the difference between a machine learning engineer vs. data scientist.
Machine Learning Engineer vs. Data Scientist
Machine learning is a form of artificial intelligence (AI) that enables software applications to accurately predict outcomes on the basis of data-driven algorithms. Machine learning engineers develop these algorithms, which use statistical models to predict an output based on input data. As the provided data is modified and updated, the output changes accordingly, without further human input. The end goal is to create AI tools that support business operations and efficiency.
Fraud detection mechanisms are one example of an AI tool. Banks and other businesses in the financial services sector use machine learning to safeguard against scams. Algorithms can detect unusual patterns, such as a credit card being used outside of its usual geographic range, to send an automated alert to block the card. What’s more, a machine can do this much faster than a human, resulting in a faster shutdown of the card. This means the timeframe in which fraud can be committed shrinks, saving the bank money.
Data science involves the examination of data, its origins, and the analysis of its meaning. Data scientists collect data, transform it into a usable format, and identify patterns (such as cause-and-effect) in that data. The end goal is to identify trends that inform smart business decisions.
Personalized healthcare is one example of how data can influence operational decisions. Hospitals may use data science to reduce readmission rates, which tend to result in (often avoidable) added costs of resources and manpower. Hospitals can collect patient data to pinpoint factors — such as patient income or residential area — that are potentially related to a patient having a higher risk of returning to the hospital. Subsequent analysis of these data points can detect patterns.
Say, for instance, readmission is high among patients in a certain neighborhood; it turns out there is no pharmacy in the area and these persons are readmitted for infections because they don’t get the antibiotics they need. Hospital administrators can use this information to rethink how they tailor care.
Although their duties are divergent, the role of a machine learning engineer vs. data scientist requires many of the same skills. To succeed in either position, it’s essential to have a comprehensive knowledge of various programming languages, such as SAS and Python. Professionals must also have a solid understanding of big data analytics, statistics, and predictive modeling.
The ability to collaborate with others is also essential. Working with big data sets is often a matter of teamwork, which involves other IT and computer science experts. Finally, both machine learning engineers and data scientists must be able to communicate their findings to non-experts. In more senior roles, they may be required to use visualization software and tools to present results to senior executives.
Job Outlook: Machine Learning Engineer vs. Data Scientist
Both machine learning engineers and data scientists can expect a positive job outlook as businesses continue to look for ways to harness the potential of big data. Forbes predicts that data volumes will continue to grow, especially in light of handheld and internet-connected devices that make it easier to collect information. This will also mean new challenges — such as those surrounding the heightened need for data privacy. In addition, the U.S. Bureau of Labor Statistics (BLS) has flagged big data as a major driver of future employment, particularly in the math and science sectors.
According to the BLS, computer and information research scientists are projected to see job market growth of 16% from 2018 to 2028, much faster than the national average. Not only will there be plenty of opportunities, but they will also be lucrative. According to PayScale data from September 2019, the average annual salary of a data scientist is $96,000, while the average annual salary of a machine learning engineer is $111,312. Both positions are expected to be in demand across a range of industries including healthcare, finance, marketing, eCommerce, and more.
Machine Learning Engineer vs. Data Scientist: How a Bachelor’s in Data Science Prepares You for Either Role
For individuals who are interested in a career in either data science or machine learning, a bachelor’s in data science can help pave the way. Maryville University’s online Bachelor of Science in Data Science is an excellent option. The program teaches students how to collect, evaluate, and analyze large data sets as well as how to visualize them. The flexible program also offers an aligned business minor, which teaches the leadership skills that define more senior positions.
Relevant coursework includes the following:
- Foundations of Data Science — Students work with real-world data to master core concepts of statistical inference and computational work. This introductory course is even for those with no statistics or computer science background.
- Introduction to Python — Students cover practical issues related to data analysis in Python. They learn how to debug Python code as well as master data exploration and visualization skills. Through project-based learning, students hone their problem-solving and collaboration abilities.
- SAS Programming — SAS is another programming language that both machine learning engineers and data scientists should know. This course covers the use of SAS to read, export, sort, print, and summarize data. It also teaches how to modify and combine data sets and write flexible code.
- Predictive modeling — Students learn how to use statistics to develop tools that can analyze mass amounts of data. The course refers to real-life examples, covering linear models as well as advanced models such as data clustering and classification and regression trees (CART).
- Machine learning — This course specifically covers machine learning using R/Python programming. Topics include generalized additive models, support vector machines, tree-based models, and more.
Completing coursework like this helps ensure that graduates have the skills they need to enter and be successful in the workforce. Thanks to the program’s project-based learning approach, graduates will have a portfolio of work that is ready to show employers.
Learn More About a Master of Science in Data Science
For those who want to continue their education, Maryville University also offers an online Master of Science in Data Science. This additional credential allows for a more in-depth understanding of data science issues, helping better position graduates to climb the career ladder and rise to more senior roles.