There has always been some confusion regarding the roles of machine learning engineers and data scientists, primarily because the terms are relatively new. However, if you dig a little deeper, the distinctions become clearer.
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Responsibilities of Data Science vs Machine Learning Engineers
They both use large sets of data to make improvements in the organization. Data Scientists are more involved in gathering, storing and interpreting information whereas, machine learning engineers are more focused on technological goods for consumers.
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Data Scientists
For any questions or issues that need to be solved by gathering and analyzing data, companies require a data scientist. These professionals are in direct contact with the stakeholders and leaders of the company in order to study the economic efficiency and customer goals. Using the data and information gathered, these data scientists develop computer programs and software that provide complex algorithms to help the business find patterns in large sets of data. This data is then used to learn more about customer engagement, sales and other business-related issues.
Job Responsibilities of a Data Scientist Includes:
- Remove errors from data sets to avoid distorted and asymmetrical results
- Understand the customer’s need and provide them with a solution
- Data mining
- Making sure the data is perfectly accurate and authentic by processing, cleaning and verifying the data
- Perform market research
- Identify correlations and patterns in complicated data sets
- Identify new opportunities to improve the process management
- Work with professionals and developers to help understand the needs and requirements of customers
Also Read : Roles & Responsibilities of a Data Scientist
Machine Learning Engineers
Machine learning engineers are tasked to develop programs that control robots and computers. Extensive research on machine applications allow these professionals to understand how machines can help reduce human effort. The algorithms created by machine engineering allow a machine to find patterns in its own programming data and teaching it to understand commands. The artificial intelligence seen in automatic vacuums and automatic cars is the doing of these machine learning engineers.
Job Responsibilities of a Machine Learning Engineer Include:
- Research new technology and implement them in various machine learning programs
- Identify the best design and hardware to support the build of the machine
- Study and transform prototypes
- Research and implement appropriate machine learning tools and algorithms
- Develop ML applications according to the needs and requirements of the project
- Selection of the right data sets and representation methods of the data
- Run ML tests and experiments
- Perform statistical analysis using ML test results
- Be updated of developments in the field
Also Read : Career Prospects in Machine Learning
Data Science vs Machine Learning: What do they do?
If you look at the roles of these two members, a data scientist is responsible for the statistical analysis that is required to determine which machine learning approach will suit the best to a given project and then model the algorithm and prototype for testing. Whereas, a machine learning engineer takes the prototype model and makes it ready to work in a production environment.
Also Read : Important Tools to Master for Becoming an Expert Data Scientist
Final Thoughts
It is not a question of data science vs machine learning, it is all about what you are interested in and where do you see yourself working in the future. Whether you decide to become a machine learning engineer or a data scientist, you are going to need the guidance of a data science course online or a similar course for machine learning. A data science course online will help you gain the statistical eye you need to analyse huge sets of data without any clogs and a machine learning course will help you excel at the programs that control computers and robots. The choice is yours, so choose wisely.
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