If you’re able to read this article, you are already a part of the modern world running on information and constantly catapulted by technology. So welcome, tech user! Even if you don’t think much about the terms like artificial intelligence, data science, robotics, machine learning, and cybersecurity in your daily life, they are already a huge part of it.
Data science and machine learning engineering are two prominent and evolving tech fields that can offer great salaries and open doors for path breaking and fascinating careers. But what is it that machine learning engineers and data scientists do? Are they almost similar or strikingly different? Keep reading to learn more.
What is the role of a Data Scientist?
Data scientists work with computers, algorithms, and systematic work processes to collect, clean, organize, and analyze data to establish patterns, deduce actionable insights, and find answers to crucial questions in any field of their interest such as:
- Stock market metrics
- Restaurant food inventories
- Internet website traffic
- Astronomical solar system surveys
- Epidemiological disease tracking
- Population censuses
Here’s how a typical working day as a data scientist could look like:
- 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
What skills do Data Scientists need?
Data scientists can use knowledge, skills, and analytical techniques from an array of subjects like:
- 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
Below is a list of computer languages, tools and platforms that enable to data scientists to process data such as:
Computer Programming Languages
Data Visualization Tools
Computing Platforms
What is the role of a Machine Learning Engineer?
Machine learning refers to the design, implementation, and operation of computers with algorithms that can learn and improve on their own! They develop, deploy, and improve software applications and machine learning models that can deduce critical and actionable insights from data.
Here’s how a typical working day as a data scientist could look like:
- Programming computers using different programming languages
- Designing and developing machine learning algorithms, data structures, models, and devices
- Testing machine learning models and software architectures
- Reviewing machine learning models and devices
- Composing technical documentation explaining your work
- Communicating technical concepts with fellow professionals
Machine learning engineers create products that help with 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 this technology evolves with time, computers would be able to process data in exponentially shorter durations of time. Financial business decisions, operating self-driving cars, or diagnosing diseases are some of the widespread areas where machine learning could be deployed.
Read more about the ins and outs of machine learning engineering.
Which skills do Machine Learning Engineers use?
Machine learning engineers need to be adept in computer 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!
These are some of the additional computing platforms that machine learning engineers use:
How Much Can You Earn as a Data Scientist or Machine Learning Engineer?
According to ZipRecruiter, the average annual salary for a data scientist is $119,413 in the U.S. in 2021. Salaries could range anywhere between $92,500 (25th percentile) to $164,500 (90th percentile) based on skill sets and experience.
Additionally, according to ZipRecruiter reports, the average annual salary for a machine learning engineer is $130,530 in the U.S. in 2021. Salaries could again range from $103,000 (25th percentile) to $179,000 (90th percentile) based on proficiency and past experience.
Learn With Udacity
We at Udacity offer you multiple Nanodegree programs based on your current skill sets to become proficient in data science and machine learning. Here’s the list you can choose from:
Gift yourself a rewarding learning journey in these modern technologies with Udacity! Choose a relevant Nanodegree program today!
