Introduction
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.
Therefore, before we delve into which career option is best for young professionals, let’s understand what each of the fields entails and what data science and machine learning are.
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.
The 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 help businesses gain an edge over their competitors and also achieve their goals.
Skills required for the role of Data Science professional
The role of a data scientist needs a highly qualified professional with either a Master’s degree or a Ph.D. in engineering, statistics, mathematics, computer science, and other IT-related subjects.
- 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.
- A strong grasp of mathematics in addition to both technical and analytical skills are other prerequisites for becoming a data scientist.
- 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 a crucial role in real-time roles and responsibilities. So do gain proficiency in them.
- Knowledge of machine learning techniques like clustering and artificial neural network is 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.
- Last but not least, in order to have a successful career in data science, you need some data science programs. Some of the best data science courses 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 data-driven algorithms to enable machines to perform tasks without any human intervention.
Interestingly, the machine learning process has various similarities between data mining and predictive modeling mainly because the basic procedures are 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:
- For machine learning engineers, it is important to have relevant experience with deep neural networks, reinforcement learning, and visual processing.
- Basic enough knowledge of programming languages like Python, R, Java, JavaScript, Scala, and C languages like C++ and C are also needed.
- A strong grasp of both probability and statistics are other prerequisites for a successful machine learning engineer. A strong grasp of mathematical concepts is also required.
Now that we have learned what these two fields of data science and machine learning deal with, it becomes important 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!
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Differences between Data Science and Machine Learning Engineer
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Data Science
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Machine Learning
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Includes numerous data operations
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It is a subset of Artificial Intelligence
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Data Science is about sourcing, cleaning, and processing data for the analytical process
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Machine learning is about using available data to deliver results without being told explicitly
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A Data Scientist will use popular tools like SAS, Tableau, Apache Spark, and MATLAB for the data process
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Popular tools for Machine Learning will include Amazon Lex, IBM Watson Studio, Microsoft Azure ML Studio
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Data Science deals with structured and unstructured data
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Machine Learning is about using statistical models
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Fraud detection and healthcare analysis are renowned examples of data science
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Some of the popular examples of Machine Learning include Recommendation systems like Spotify and Facial recognition
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Average base salary of a data scientist is about $113K per year
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The average base salary of a machine learning engineer is about $114K per year
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Based on the skills required, qualifications, and other prerequisites, there is not much contrast between a data scientist and a machine learning engineer, as to which one is a better career option. Depending on your interest areas you can choose your career option.