Why Choose an MS in Machine Learning Over Data Science?
Machine learning and data science are closely connected, so choosing between the two can be confusing while exploring study masters in USA programs. Data science and machine learning have a lot in common, with both involving data, programming and statistics, as well as some overlap in what you study.
The difference becomes easier to see once you look at what each degree actually teaches. An MS in Machine Learning looks more closely at how models are created, trained and improved. Data science is wider in scope, covering how data is prepared, studied and presented, along with the statistical and machine learning techniques used to make sense of it.
Understanding Machine Learning Models
In data science, you may work with raw data, clean it, study it and use it to find useful answers. Machine learning spends more time on the models behind that work.
What you study will vary by program. Subjects may include deep learning, reinforcement learning, computer vision, natural language processing and optimisation. At Carnegie Mellon, for example, the curriculum combines machine learning with statistics, probability, programming and optimisation, along with electives for further study.
The Two Degrees Can Lead to Different Academic Experiences
It is easy to assume that an MS in Data Science in USA and an MS in Machine Learning are simply two names for similar programs. That is not always the case.
Take the curriculum apart before deciding. A data science program may spend more time on areas such as data management, statistical analysis, visualisation and communicating findings. Machine learning programs tend to be more mathematical and technical, with more time spent understanding algorithms and how models are built.
Machine Learning May Suit You If You Already Have a Technical Base
An MS in Machine Learning usually needs a strong base in maths and programming. Carnegie Mellon, for example, looks for students who already have a good base in maths, statistics and programming.
If these are subjects you enjoy, an MS in Machine Learning may feel like the right next step. If you are more interested in working with data across different areas, data science may suit you better.
Consider the Work You Want to Do Afterwards
Start with the job rather than the degree name. If you are interested in roles centred on machine learning models, AI systems, natural language processing or computer vision, specialised machine learning study may give you relevant academic depth. Data science can make more sense if you want a wider set of skills for working with data across different business and technical settings.
When you study MS in USA, look at where recent graduates are working and the roles they have moved into, rather than judging career prospects by the degree title alone.
Machine Learning Is Not Automatically the Better Choice
The growth of AI can make an MS in Machine Learning sound like the obvious option. That is not a good enough reason to choose it. A specialised program works best when you actually want that specialisation.
An MS in Data Science in USA may give you greater breadth if you are still deciding which part of the data field interests you most. Depending on the university, you may still be able to take substantial coursework in machine learning while also studying databases, analytics, statistics and other areas.
How to Decide Between MS in Machine Learning and Data Science
Start with what you actually want to learn and work on after graduation. An MS in Machine Learning may suit you if you want to spend more time on algorithms, models and intelligent systems. Data science can be a better fit if you prefer working with data, from collecting and organising it to analysing and interpreting the results.
FAQs
It depends on what you want to study. Machine learning goes deeper into algorithms and AI models, while an MS in Data Science in USA usually covers a wider range of work with data. Look at the actual subjects in each program before choosing.
Universities set their own requirements, but maths and programming are usually important. Before applying, check whether the program expects previous study in areas such as calculus, linear algebra, probability or statistics.
It can be if you already know that machine learning and AI are the areas you want to pursue. As part of your Masters study USA, also take into account the cost, location, research opportunities and what you can do after getting your Masters degree.
Identify your subject of interest, and then find universities that are reputable in that field. Read the courses offered in the faculty, and the research that is available, instead of getting caught up in the name of the university.
Conclusion
Choosing between machine learning and data science comes down to the kind of depth you want from your master’s degree. If building models, understanding algorithms and studying AI in greater detail appeal to you, an MS in Machine Learning may be worth prioritising. Look beyond the course name before making the final call.
Planning an MS in Machine Learning in the USA? Imperial Overseas Education Consultants can help you find programs and universities that match what you want to study and where you want your career to go.
