Data Scientist Vs Machine Learning Engineer: Which Career option to Choose?

With data becoming the most important factor in every organization, it is not surprising to witness a rise in demand for certified Data Science professionals.

Data has become the backbone of businesses in the present world and to harness this data for useful insights, organizations need specialized tools, technologies, and yes, people aka professionals in the relevant field.

This is where the role of Data Science professionals and Machine Learning Engineers gain prominence. While the demand for certified data science professionals is always on the rise, the IT industry has also seen the rise of Machine Learning Engineer. And all this had done nothing but added confusion in the minds of recruiters, job seekers, and aspiring professionals seeking a career in data science.

Thus before we delve into which career option is best for young professionals let’s understand what each of the field entails and what is data science and machine learning.

Data Science — Definition, Skills Required, Qualifications!

Data Science is nothing but a broad study of data systems, processes with an aim to not only maintain data sets but also to derive meaningful insights out of the processed data for businesses.

Certified data scientists use a blend of applications, principles, tools, and algorithms to clean, and process the data clusters that can be both structured and unstructured.

Role of a data science professional is not only to clean and process the data but also to monitor and store it properly for future use. And a data science professional uses data modeling and data warehousing as the applications and tools to track the burgeoning data sets.

The insights gathered from the clusters of data sets helps businesses gain edge over their competitors and also achieve their goals.

Skills required for the role of Data Science professional

The role of data scientist needs a highly qualified professional with either a Master’s degree or a PhD in engineering, statistics, mathematics, computer science and other IT related subjects.

1. You need to be proficient in programming languages like R, Python, SQL and numerous other such technologies as well as trends that the industry demands.

2. Strong grasp of mathematics in addition to both technical and analytical skills are other prerequisite for becoming a data scientist.

3. For a successful data science career, you should gain experience in data mining and statistical techniques. You can either do internships or self-start through various online programs. Data mining techniques are important so you need to master them well. Techniques like regression or linear models, and network analysis play crucial role in the real time roles and responsibilities. So do gain proficiency in them.

4. Knowledge of machine learning techniques like clustering and artificial neural network are also of vital importance. These techniques will not only help you in your data science career but will also help you when you are planning a career transition from data science professional to machine learning engineer.

5. Last but not the least, in order to have a successful career in data science you need to some data science programs. Some of the best data science course and certifications are available online that cover everything from the basics to the latest trends in the data science industry.

Now that the role of data science professionals is clear, let’s have a look at the roles of Machine learning engineer, skills, and qualifications

Machine Learning is nothing but a significant branch of artificial intelligence, which involves the data-driven algorithms to enable machines to perform the tasks without any human intervention.

Interestingly, the machine learning process have various similarities between data mining and predictive modeling mainly because the basic procedures is about identifying patterns in the data and then modifying the program as per the patterns seen.

Often known as sophisticated programmers, machine learning engineers are adept in both developing and training machines to comprehend and apply pre-stored knowledge without any specific directions given.

Skills required for a Machine Learning Engineer include:

1. For machine learning engineers it is important to have a relevant experience with deep neural network, reinforcement learning, and visual processing.

2. Basic enough knowledge of programing languages like Python, R, Java, JavaScript, Scala and C languages like C++ and C are also needed.

3. A strong grasp of both probability and statistics are other prerequisites for a successful machine learning engineer. Strong grasp of mathematical concepts is also required.

Now that we have known what these two fields of data science and machine learning deal with, it becomes significant that we learn the difference between data science and machine learning as well to get a better idea.

Summing it up here are some of the key differences in a nutshell!

Based on the skills required, qualifications, and other prerequisites there are not much comparison between a data scientist and machine learning engineer, as which one is a better career option. Depending on your interest areas you can choose your career option.