Data Science vs. Machine Learning
Jobs in data science, machine learning, and artificial intelligence are growing at an increasing rate, and skilled people in these fields are in high demand in the job market.
According to the U.S. Bureau of Labor Statistics, employment of computer and information research scientists is expected to grow 16% by 2028, which the Bureau describes as “much faster than average.”
If you’ve spent time researching jobs in data, you may have come across the titles “machine learning engineer” and “data scientist.” Machine learning and data science are often used together to describe jobs in data science, but if you’re thinking about pursuing a career in data, you should know the similarities and differences between these fields.
This guide will discuss the basics of both machine learning and data science, how these fields compare, as well as the responsibilities held by both machine learning engineers and data scientists.
What is Data Science?
Data science is a field focused on gathering, collecting, and analyzing structured and unstructured datasets. The main goal of data science is to help organizations and individuals make better decisions through the use of data to find solutions to problems.
Data science is a broad term and encompasses a wide range of technical concepts. For instance, data scientists need to have experience with mathematics and statistics, as well as programming concepts like data mining and visualization.
By using concepts from data science, businesses are able to gain a competitive advantage and identify new market opportunities. This is because analyzing data can give them a better idea of their target customers, which they can then use to improve their operations.
When a business has a problem to solve – or a question to answer – going to a data scientist is the first move they will make. Data scientists will use their knowledge of mathematics, data, and statistics to break down a problem, collect data to solve that problem, then analyze that data and present their findings to the business.
Let’s walk through an example of how a data scientist could help a business. Suppose a t-shirt company is looking to launch a new product, and is not sure what features their new product should have. The t-shirt company could turn to a data analyst, who would use their skills to understand current customer preferences – which products are most and least popular – and present recommendations to the company.
What is Machine Learning?
Machine learning is a specialist field within the data science industry.
Machine learning engineers are responsible for using mathematical and data concepts to build models and algorithms that can make decisions and predictions. Machine learning engineers may also use the data collected by data scientists to build programs that can control computers and robots.
For instance, an entertainment streaming company is looking to provide a more personalized customer experience to their users. A data scientist may collect data on existing user preferences, then a machine learning engineer will use that data to create a model that predicts future user behavior. This model could be capable of making recommendations to users on what content they may want to consume next.
An example of a machine learning model in action is the Netflix recommendation engine. When you finish watching a show on Netflix, it will keep track of the fact you have watched that show, and recommend similar shows to you based on what other people who have watched that show have enjoyed.
Machine Learning vs. Data Scientist: Job Responsibilities
Now that we’ve discussed in depth what these fields involve, we can go deeper and ask ourselves: what responsibilities do machine learning engineers and data scientists have?
Data Scientist Job Responsibilities
Data scientists use their knowledge of mathematics and programming to solve problems using data. Data scientists are involved with collecting, storing, and cleaning large amounts of data. They will then use that data to identify valuable insights on a particular problem, and present their findings to relevant stakeholders within an organization.
The job responsibilities for a data scientist include:
- Building models to analyze data
- Creating systems to store large amounts of data
- Analyzing large data sets to identify trends
- Presenting information using data visualization
- Identifying valuable data sources and collecting data from those sources
To be a successful data scientist, you need to have a combination of skills. Here are the top requirements that you are likely to see in a data scientist job description:
- Experience using programming languages such as Python, R, and SQL
- Strong mathematical and analytical skills
- Experience using statistical and data mining techniques
- Experience using web service platforms such as Azure and AWS
- Experience analyzing data from sources such as AdWords and Google Analytics
- Experience visualizing data using Tableau, ggplot, and D3
Overall, data scientists need both a firm understanding of math and statistics, as well as practical experience using these skills in the context of data analysis.
Machine Learning Engineer Job Responsibilities
Machine learning engineers have different responsibilities depending on the project on which they are working. For instance, a machine learning engineer may be assigned to build a recommendation engine for an e-commerce site, or to build a payment protection system against fraud at a payments provider.
Machine learning engineers will use their knowledge of data, statistics, and machine learning models to build predictive systems and to use data gathered by data scientists to improve existing machine learning models.
Here are a few of the job responsibilities taken on by a machine learning engineer:
- Run machine learning experiments
- Train and retrain machine learning systems using new and existing data sets
- Design and implement machine learning systems according to a set of requirements
- Understand and implement data science algorithms
- Research and implement machine learning algorithms
- Monitor the effectiveness of a machine learning algorithm
Successful machine learning engineers have a vast array of skills. Here are the main job requirements you’re likely to see in a job posting for a machine learning engineer:
- Experience programming in a language such as Python, Java, or R
- An understanding of probability theory and statistics
- Experience working with distributed systems
- Experience working with messaging tools
- Experience using MATLAB and other data analysis packages
- Experience with common machine learning algorithms
Often, machine learning engineers have a background in data science, and then go on to pivot into a career in machine learning.
Data Science vs. Machine Learning: Salary
Both machine learning engineers and data scientists command impressive salaries.
What’s more, in 2012 Harvard Business Review referred to “data scientist” as “the sexiest job of the 21st century,” which makes sense, given how important data has become in all of our lives, and to business’ competitive prospects.
Data Science vs. Machine Learning: The Final Comparison
As we’ve discussed, data science and machine learning both involve a similar set of skills: knowledge of data, statistics, and mathematics. But, these two jobs are very different.
Data scientists are focused on collecting, storing, analyzing, and processing data. With this data, they’ll help a business answer the questions and solve the problems they are facing. On an average day, data scientists figure out how to collect data, build collection systems, and identify trends in the data they have gathered.
Machine learning engineers are more focused on building and deploying machine learning models, which are capable of making predictions. On the average day, a machine learning engineer uses collected data to create models. They are also responsible for refining those models over time.
Both data scientists and machine learning engineers earn strong salaries, even for a job in tech, and the job prospects for careers in data are impressive.
It’s worth noting that both these fields complement each other. Data science, for example, often involves employing machine learning models, and machine learning draws on a wide range of techniques from the field of data science.
If you’re interested in going through big data sets and solving tough problems with data, becoming a data scientist may be a good career path for you. On the other hand, if you love solving difficult challenges and want to build predictive models, machine learning could be a good fit.