Data Scientist Vs Machine Learning Engineer – Which is Better!

Data Scientist Vs Machine Learning Engineer – Which is Better!

The year 2021 has seen an upward surge in the two most popular roles of Data Scientist and Machine Learning engineer in the IT industry. Whether there are some distinctions or overlapping of the roles, depends largely on the organizations people choose to work with. Each organization defines the roles in their unique way and individuals need to prepare accordingly.

Skills Preparation for ML Engineer and Data Scientist Considering there is an overlapping of roles and skills for both data scientist and an ML engineer, it is not surprising to read that certain skillsets are common to both. The difference lies in how they apply in those skills in their day-to-day workings.

Implying – You should not only master the skills but should be proficient in applying them for different roles.

But before that, understand the major differences in how both data scientists and ML engineers approach their work.  A data scientist works more on the data models, while an ML engineer’s focus is more on the deployment of those data models.  A data scientist’s focus is more on understanding the algorithms, whereas an ML engineer will be more concerned on shipping those models into a production environment, which interacts with the users.

Now that we are aware of the differences in the working styles, let’s have a look at the individual skills required for both data scientist and ML engineer.

Data Scientist – Skills Required The year 2021 has seen an emergence of various new tools and skills for data scientists, however, there are top three tools and skills that are used by most of the data scientists in solving everyday queries. They are – 1. Python/R: Need we say more on the use of these two popular programming languages by data scientists. Most of the practicing data scientists use Python, while some of them use R. R is used more for statistical data and Python is quite user-friendly and compatible with other languages as well.

2. Jupyter Notebook or any other popular IDE: Most of the data scientists you will meet in the initial stages of your career, use Jupyter Notebook.

Reason: It is the central place for coding, writing text, and viewing various outputs including results and visualizations. While there are other popular IDEs

like PyCharm, and Atom, Jupyter Notebook has been considered as a goto IDE for data scientists and industry experts feel that is unlikely to change sometime soon.

3. SQL: Or Structured Query Language as it is known popularly is an essential tool. Reason: data is at the helm of a machine learning algorithm, which will finally become a part of the data science model’s data. Data scientists use SQL for the initial part of their data science process such as querying the first data along with creating new features. But that’s not all where SQL is used. SQL is also used at the end of the data science process when the model is run and deployed, and the results are saved in the organizational database, which in turn also uses SQL. With these three skills and tools mastered, you will be good to go as a successful data scientist. No doubt there are other skills that you will learn as you earn. However, the base is built of these three skills – 1. A programming language 2. A visualization platform or an IDE 3. Last but not the least a querying language Machine Learning – Skills to Ace! As discussed earlier, the role of an ML engineer comes into play when a data scientist has built the model. Reason: The main purpose of a machine learning engineer is to take a deeper dive into the code and shipping and the process is known as deployment. As an ML engineer you may not have to know the workings of random forest, however, you will be expected to know how to save and load a file, which can be then predicted in a production environment. To sum it up a machine learning engineer is more focused on software engineering. So some of the skills and tools that you need to master include 4. Python: Yes, it is common for both data scientists and machine learning engineers. However, the similarity ends here. While a machine learning engineer is more into object-oriented programming (OOP) in Python, whereas a data scientist is not concerned with the OOP, as data scientist’s primary job is to build models and concentrate on the statistics involved along with analytics. Machine learning engineers need to be more trained in Python, however, if you are well-versed in Python then you can work both as a machine learning engineer and data scientist.

5. GitHub/Git: This is one of the common tools to store code repositories.

Usually, machine learning engineers use Git and GitHub, as it is code management tool and platform that is essential for machine learning engineers to make code changes as well as pull requests. Occasionally, both

data scientists and machine learning engineers are well-equipped in git and GitHub. 3. Deployment tools: One of the skills where machine learning engineers and data scientists differ is deploying a model. While there are some data scientists who may know how to deploy a model, it is a core function of ML engineers. There are organizations that prefer data scientists to be proficient with both data science and machine learning skills. So, if you know all the skills, you have plenty of opportunities at your feet.

So, whether it is the role of a data scientist or a machine learning engineer, the above-mentioned skills will help you gain the footage. Yes, don’t forget to upgrade your learning skill by going for certifications from the reputed institutes.