Learn the difference between various roles in data science

When it comes to data analytics, many companies are searching for an expert to do the job. These roles include data engineers and scientists or machine learning engineers. While these sound similar and are sometimes even used interchangeably, there are significant differences between them.

You could decide on the roles of these people, but the most important thing is to understand the primary purpose behind each of them. This ensures you hire only those who can do what your company needs. Let's talk about the differences between these roles.

Data Engineer

A data engineer is responsible for working with databases and writing clean code that allows other team members to access and analyze data. They work with various tools, including Hadoop, Spark, and Hive, and may be called upon to work on coding solutions for issues that arise in production when data scientists aren't available.

Data engineers may also implement ETL (extract, transform, load) programs that help organize data from specific sources into databases.

What do they study?

Data engineers typically have computer science, software, or computer engineering degrees, though they may also hold degrees in statistics or mathematics.

What does their day-to-day look like?

A typical day for a Data engineer includes:

  • Writing scripts to retrieve data from databases or API endpoints and load it into their own data warehouse or database for analysis
  • Exploring and analyzing that data to understand it better and find additional insights

Data engineers do a lot of what would be considered IT work—

  • Setting up servers, installing packages, creating databases—all in the service of performing analyses
  • Creating ETL (Extract, Transform and Load) processes to transform the data from raw state to useable data
  • Using Machine learning algorithms to classify and mine the data for patterns and trends
  • Writing SQL queries for reporting the results

Data Scientist

Data scientists specialize in statistics, analysis, programming, and machine learning. They use their skills to identify patterns in raw data sets to solve complex issues. As a rule, they work with large amounts of structured and unstructured data (for example, they can run queries through databases). Their job is to come up with recommendations based on this information. Data scientists are often employed by businesses specializing in data analysis — but it's not unusual for them to work at technology companies.

What do they study?

Data scientists typically have a Ph.D. degree in a technical field such as computer science, statistics, or mathematics. They may also be involved in writing code for machine learning models used by organizations.

What does their day-to-day look like?

A typical day of a Data scientist includes:

  • Building models based on existing datasets
  • Conducting experiments on these models
  • Testing them on new datasets and sharing their discoveries with other members of the organization
  • Getting/purchasing/collecting/cleaning up data from various sources like web pages, PDFs, etc.
  • Writing code in Python or R programming language to do stats and analysis on the raw data
  • Visualize using tools like Shiny or Bokeh

Machine Learning Engineer

Machine Learning is the science of getting computers to act without being explicitly programmed. Machine learning (ML) engineers use statistical techniques such as regression analysis and classification algorithms to train computers to automate tasks usually performed by humans. This means they'll need to understand the different types of machine learning algorithms available. The majority of the work of machine learning engineers is done on computers, but they also need to be able to interpret the results of statistical tests.

What do they study?

ML engineers typically have an undergraduate degree in computer science or electrical engineering and then complete an MS program focused on artificial intelligence (AI).

What does their day-to-day look like?

  • Designing and implementing algorithms such as neural networks, support vector machines, clustering techniques, and many more
  • Improving the existing algorithms
  • Building prototype models from scratch
  • Handling large amounts of data (often terabytes and petabytes)
  • Testing, debugging, and validating your solution to ensure accuracy
  • Building and maintaining analytical models for the organization by using statistical software like R or SAS

[Summary] What’s the difference between these roles?

Data engineers, data scientists, and machine learning engineers are all highly-skilled professionals who work together to create high-quality products. The three roles often overlap and depend on one another to complete their work. Each has a unique set of skills that allows them to contribute differently to the development process.

Data engineers create the systems that allow data scientists and machine learning engineers to perform their work. Data engineers build software systems that other developers can use within an organization or by customers who purchase them. They also manage databases and other forms of storage, ensuring that they are secure and accessible.

Data scientists analyze large datasets using complex algorithms, statistical models, and machine learning techniques. They use this information to identify patterns in data sets that may be useful for predictive analysis or other purposes.

Machine learning engineers take the results of data science projects and translate them into computer programs that can be used by computers on their own without human intervention. Machine learning engineers also work with data scientists when developing new algorithms for use by machines themselves. These algorithms allow computers to learn from past experiences without being explicitly programmed by humans first, ahead of time.

Which one do you need in your team?

There is no shortage of reasons to employ data engineers, scientists, and ML engineers–but it's also essential to understand the limits of their capabilities. While the roles can be considered somewhat synonymous, each one offers unique skills that don't overlap in the way some might think. The reality is that many businesses could use the skills of a scientist and a data engineer in their business. Suppose a company wants to combine the output of data analytics with machine learning techniques. In that case, it will need to employ someone who understands scientific research, has an understanding of machine learning, and can integrate it into an IT team. Your company likely needs all three roles. Understanding the roles and responsibilities these jobs require can help your organization define the right mix of skills needed, whether you hire separately or source a multitasker.

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