With the development of artificial intelligence, there are some new jobs in the market. But many of us can’t tell the difference between these new professions, especially the difference between the roles of machine learning engineers and data scientists.
After reading different articles, blogs and watching some videos, I want to introduce them more clearly by comparing the differences between them.
Let’s start with an analogy. What’s the difference between a writer and a professor? It can be said that both of them know the “rules and grammar” of a language. One is a storyteller, the other is a strict practitioner of the “rules”.
Data scientists process and analyze the raw data, connect points and tell stories using other visualization tools. They usually have a wide range of skills and no more than one or two in-depth knowledge. They are more inclined to be creative, like an artist.
Machine learning engineers regard data as something that must be received and efficiently output in some appropriate form. Their skills need to be efficient in terms of implementation details.
There may be a lot of overlap between the two, but data scientists can be machine learning engineers, and vice versa. Maybe as they gain more experience, machine learning engineers are data scientists, and that will come true.
Machine learning and Wien diagram of data science
In terms of insight or learning, data science needs talents with business brain, while machine learning needs talents with system prediction. For example:
Data science: “in this part of town, there is about a gas station every two miles.”
Machine learning: “we’ve walked two miles since we saw the gas station, so now we have to start looking for another gas station.”
Let’s take another look at Netflix.
We all know that Netflix intelligently recommends movies based on previous choices. The recommender system can be used with machine learning algorithm to provide suitable movie selection.
When talking about Data Science in Netflix, the patterns we’re looking at include the number of reviewers we watch at a particular time, their age and gender composition, and many other situations. These decisions are used to improve business prospects. When enterprises need data to answer or solve problems, the job of data scientists is to provide useful insights from raw data and unstructured data.
Skills needed by data scientists：
Data mining and cleaning
Unstructured data management technology
Programming languages such as R and python
Understanding SQL database
Using big data tools such as Hadoop, hive and pig
Skills required by machine learning Engineers:
Fundamentals of Computer Science
Data evaluation and modeling
Understanding and applying algorithms
natural language processing
Data architecture design
Text representation technology
To sum up, the work of data scientists and machine learning engineers is very different, so don’t confuse them. Determine which position is more suitable for your skills and personal interests, and consciously cultivate your skills in a certain direction to prepare for the future.