In today’s world of big data, machine learning, and predictive AI, it’s common to find vocations that overlap or have different names for the same job. For example, one organization’s Internet marketing is another company’s digital marketing. Whatever the case, there are plenty of opportunities for misunderstanding.
This article tackles a small portion of this issue by exploring how data scientists and machine learning engineers differ. This effort requires establishing the difference between data science and machine learning. We will highlight those differences, comparing and contrasting required skills, roles and responsibilities, average salaries, and how professionals can get started on a career through online AI and machine learning training.
So, let’s begin our data scientist vs. machine learning engineer comparisons by defining the terms.
What is a Data Scientist?
Data science is the field of studying data and how to extract meaning from it. Consequently, data scientists use machine learning, statistical methods, data mining, and predictive analytics to convert raw data into actionable insights. Data scientists work in many industries, from healthcare to finance to government, using their skills to spot anomalies, trends, and patterns in the data they analyze. As a result, they help companies make more intelligent operating decisions.
Also Read: What is Exploratory Data Analysis? Types, Tools, Importance, etc.
What is a Machine Learning Engineer?
Machine learning is classified as a subset of artificial intelligence (AI) that employs algorithms to collect and extract valuable data and use it to predict future trends, events, or outcomes. Software is programmed using models that let engineers conduct statistical analysis to better understand data patterns. So, machine learning engineers use large quantities of data to construct models that can make these predictions.
Additionally, they develop, optimize, and maintain algorithms that can train machines to solve data-based problems. Machine learning engineers usually work in teams alongside software developers and data scientists to train and implement their models used in various business scenarios.
Machine Learning vs. Data Science
Although we plan to explore the differences between data science and machine learning by breaking down the comparisons into different factors, let’s do a general overview of the ML engineer vs. data scientist dichotomy.
The short version is a data scientist’s primary responsibility is to develop solutions using deep learning or machine learning models for different business problems. On the other hand, the primary goal of machine learning engineers is to use the models created by data scientists and place them into production.
Data science determines the necessary tools, systems, and processes for transforming data into actionable insights, and machine learning is a field of AI and a part of data science that uses data to train algorithms and let them learn.
Let’s break down the data scientist vs. machine learning engineer debate into different categories.
Also Read: What is Data Wrangling? Importance, Tools, and More
ML Engineer vs. Data Scientist: Roles and Responsibilities
Here’s a breakdown of the roles and responsibilities of a machine learning engineer:
- Using data science technology, design and implement machine learning models for various problems
- Test and evaluate their developed machine learning models and algorithms on real-world data sets
- Ensure the organization uses machine learning principles in making business decisions
- Design, create, maintain, and improve all machine learning algorithms for the company’s services and goods. This includes writing production-level codes to improve the existing ML models.
Here’s a similar breakdown for data scientists:
- Research and develop statistical models used for data analysis
- Collect and analyze data, as well as make recommendations for business processes
- Ensure the above recommendations and other insights are adequately communicated to non-technical stakeholders
- Use different types of software, including R and Python, to explore and visualize data
- If needed, be able to write code, use statistical software, and work with various data sets, everything from social media analytics to healthcare information
- Processing, cleansing, and checking the integrity of all data used for data analysis.
ML Engineers vs. Data Scientists: Required Skills
Let’s now compare the skills required by a machine learning engineer to those of a data scientist.
Machine Learning Engineer
- Programming. You’d be hard-pressed to find any IT-related occupation that can’t benefit from a bit of programming knowledge. In this case, programming (Java, Python, SQL, etc.) is the most essential skill machine learning engineers should have. It also helps to have a good grip on computer concepts.
- Data Modeling and Evaluation. This skill covers understanding the data structure and finding suitable data patterns that will be suitable by using algorithms.
- Mathematics. This includes vital concepts such as probability, linear algebra, statistics, multivariate calculus, and distributions such as binomial Poisson, normal, etc.
- Natural Language Processing, or NLP. NLP creates devices and systems that understand, interpret, and manipulate human languages. NLP includes libraries and techniques such as Word2vec, Sentiment analysis, Summarization, etc.
- Advanced Signal Processing. This skill focuses on minimizing noise and extracting a signal’s best features.
- Neural Networks. Neural networks can alternately be called an algorithm set loosely modeled on the human brain and designed to recognize patterns. They interpret data by clustering or labeling raw input and machine perceptions.
Data Scientist
- Programming. Here’s programming again! Like machine learning, programming is data scientists’ first and most crucial skill. Comprehensive knowledge of relevant languages such as Python makes it easier to excel in this vocation and helps professionals organize unstructured data sets.
- Knowledge of Analytical Tools. Understanding relevant analytical tools is necessary for extracting valuable information from organized data sets. Data scientists use tools such as Hadoop, Hive, SAS, Pig, and R.
- Mathematics. Various mathematical concepts, such as probability, calculus, linear algebra, statistics, etc., are crucial in data science.
- Machine Learning. You can build predictive models with machine learning. Machine learning algorithms are vital for predicting future events based on past data.
- Data Extraction, Wrangling, and Visualization. These skills are used to extract data from sources like MongoDB and MySQL and transform it to store it in a given format and structure for analysis and querying. The data is then loaded to a data warehouse for analysis. Data wrangling cleans messy, complex data sets to make access easier. Tools like Tableau and Power BI are well-suited for data visualization.
- Model Deployment. This skill involves deploying a machine learning model into a live environment, which is a valuable step in gaining operational value from machine learning.
Data Scientists vs. Machine Learning Engineers: Average Salary Comparisons
Indeed.com reports that data scientists earn an average yearly salary of $124,106. Additionally, Indeed shows that machine learning engineers can expect a salary of $166,001 per year.
Naturally, both salary figures are subject to extenuating circumstances such as geographical location, experience level, the hiring company, demand, state of the economy, and other considerations. Don’t be surprised to see these numbers fluctuate daily; what you see above is just a snapshot.
Also Read: What is Spatial Data Science? Definition, Applications, Careers & More
ML Engineers vs. Data Scientists: Potential Careers
Both machine learning and data science can pave the way towards excellent career opportunities.
Data Science Careers
Data science-related careers include:
- Data scientist. Data scientists use data to better understand and explain events around them, helping organizations make better decisions.
- Data analyst. Data analysts gather, clean, and study data sets to solve business problems.
- Data engineer. Data engineers build systems that gather, manage, and change raw data into useful information for data scientists and business analysts.
- Data architect. Data architects review and analyze an organization’s data infrastructure to plan databases and implement solutions to store and manage data.
- Business intelligence analyst. BI analysts gather, clean, and analyze customer and sales data, interpret it, and share their findings with business teams.
Machine Learning Careers
Machine learning-related careers include:
- Machine learning engineer. Machine learning engineers research, build, and design AI and are responsible for machine learning and improving and maintaining AI systems.
- AI engineer. Artificial intelligence engineers build and implement AI development and production infrastructure.
- Cloud engineer. Cloud engineers build and maintain cloud infrastructures.
- Computational linguist. Computational linguists develop and design computers that work with human language and how it functions.
The Similarities Between Data Scientists and ML Engineers
Sure, both vocations are part of the IT landscape, but do they have anything besides this basic trait? Turns out, they do. Both occupations share these common characteristics:
- Practitioners should be skilled in mathematics, probability, and statistics
- These professionals must have programming skills
- Both jobs need people who are experienced and comfortable working with data
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Data Science and Marketing: Transforming Strategies and Enhancing Engagement
An Introduction to Natural Language Processing in Data Science
Why Use Python for Data Science?
