Data Scientist vs Machine Learning Engineer

Data Scientist vs Machine Learning Engineer

You live in a world dominated by technology and information, a world where you can’t avoid being a tech user. Technology fields like artificial intelligencedata scienceroboticsmachine learning, and cybersecurity are interwoven with your contemporary life.

Data science and machine learning engineering are two tech fields that can offer great salaries and entail fascinating work. Maybe you want to expand your career skill set to work with one of many interesting topics like machine learning for big data.

What do data scientists and machine learning engineers do? Read on to learn more.

What Does a Data Scientist Do?

Data scientists use computers, algorithms, and systematic work processes to collect, clean, organize, and analyze data in useful ways. They explore data to find patterns, extract actionable insights, and answer important questions.

As a data scientist, you can work with qualitative or quantitative data on nearly any subject like:

  • Stock market metrics
  • Restaurant food inventories
  • Internet website traffic
  • Astronomical solar system surveys
  • Epidemiological disease tracking
  • Population censuses
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A typical day working in data science can entail:

  • Collecting, transforming, and analyzing large amounts and/or unique types of data
  • Performing statistical analyses of data
  • Generating visualizations and models from data
  • Composing reports about insights drawn from data
  • Solving business-related questions or problems with data
  • Communicating with fellow professionals about data

Data science relates to the field of machine learning through the creation and use of models and algorithms for computer learning and data analysis.

Which Skills and Tools Do Data Scientists Use?

Data scientists use knowledge, skills, and analytical techniques from many subjects including:

  • Applied mathematics: math with application to different disciplines (e.g. physics, engineering, biology)
  • Statistics: math focused on data collection, analysis, interpretation, and representation
  • Information visualization: the study and design of visual representations of knowledge (often interactive) to aid human conceptual understanding
  • Analytics: systematic logical analysis of data and/or statistics
  • Computer science: computing that includes computer design, software development, and information processing
  • Communications: techniques and technologies for transmitting information

Data scientists use many computer languages, tools, and platforms to process data such as:

Computer Programming Languages

Data Visualization Tools

Computing Platforms

What Does a Machine Learning Engineer Do?

Machine learning, a subtopic of artificial intelligence, refers to the design, implementation, and operation of computers with algorithms that learn and improve on their own. Machine learning specialists train computers with sample data so the computers can learn and make useful predictions about information.

Machine learning engineers are software engineers who build software to support machine learning computer applications. They create, use, and improve software applications and machine learning models that are useful for finding actionable insights from data.

A typical day working in machine learning can entail:

Machine learning engineers create products that facilitate the work of other specialists including data scientists and data analysts. With machine learning products, machines and people can process, analyze, learn from, and understand ever-greater amounts of data. As technology evolves, computers can process data in progressively shorter periods of time.

Results of machine learning processes are useful for actions like making financial business decisions, operating self-driving cars, or diagnosing diseases.

Read more about the ins and outs of machine learning engineering.

Which Skills and Tools Do Machine Learning Engineers Use?

Machine learning engineers require strong proficiencies in computer programming and software engineering. Subject areas that inform machine learning include:

  • Mathematics: a broad discipline that includes theoretical and applied study of quantities, structures, and space
  • Computer programming: the process of designing and building computer programs to achieve specific tasks
  • Software engineering: a form of engineering dedicated to software development, operation, and documentation
  • Probability: a branch of mathematics focused on describing the likelihood of an event or truthfulness of a proposition
  • Statistics: math focused on data collection, analysis, interpretation, and representation
  • Data structures: computer science formats for storing, organizing, and managing data that enable users to access and modify the data
  • Algorithms: well-defined computer instructions useful for performing computations or solving problems

There is overlap in the computer programming languages that machine learning engineers and data scientists use; many of those listed above as useful for data science apply to machine learning engineering as well.

Machine learning engineers also use computing platforms. Popular ones include:

How Much Can You Earn as a Data Scientist or Machine Learning Engineer?

ZipRecruiter reports the average annual salary for a data scientist is $119,413 in the U.S. in 2021. Salaries range from $92,500 (25th percentile) to $164,500 (90th percentile).

ZipRecruiter also reports the average annual salary for a machine learning engineer is $130,530 in the U.S. in 2021. Salaries range from $103,000 (25th percentile) to $179,000 (90th percentile).

Develop Your Skills and Learn With Udacity

Online learning platforms like Udacity offer opportunities for you to learn and develop real-world skills. In fact, Udacity has many Nanodegree programs on data science and machine learning. If you are interested in enhancing your data science or machine learning skills, you may be drawn to:

Consider registering for a relevant Nanodegree program today!

Source- Udacity Team

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