Making the Move from Data Scientist to Machine Learning Engineer

The nature of technology across all fields is that it evolves and improves.Data science tools are no exception. Data scientists today use data analytics technologies that rely on machine learning to organize, analyze, and visualize data as opposed to the spreadsheet software that dominated data analytics in the 20th century. 

Automation, data engineering, and artificial intelligence are widely considered to be the future of data science. Data science students and professionals interested in capitalizing on the demand for skills in these areas should consider machine learning a promising path to a long-term career.

Data Science Vs. Machine Learning

Data science is an interdisciplinary field, drawing widely from statistics, mathematics, engineering, and computer science and generally encompassing data storage, organization, analysis, and visualization. The data science field has multiple subfields and specializations, including machine learning, which is a specialization created by the overlap of data science and engineering.

Machine learning is a form of artificial intelligence that data science students and professionals use to organize and analyze data. Algorithms or statistical models are trained to recognize patterns and trends in data and apply what they learned to new data without additional human intervention. 

Data scientists learning a programming language like Python can apply automation and machine learning to reduce time spent organizing and analyzing data. Machine learning engineers use automation and machine learning to build artificial intelligence that parses data. 

Moving from Data Scientist to Machine Learning Engineer

The work of a Data Scientist overlaps to a degree with that of a Machine Learning Engineer. More advanced data scientists have skills that are highly transferrable to careers as machine learning engineers and can apply their programming language skills to an understanding of automation and algorithms. Machine learning engineers also have experience in statistics and the development of machine learning models. 

Data scientists and analysts with Python training can advance their knowledge by creating projects with data science libraries or tools that rely on machine learning. Python-trained data scientists who also know Automated Machine Learning, or AutoML, which enables them to work with various machine learning models, are well-positioned for careers as machine learning engineers. Machine learning skills include the ability to understand neural networks that model the human brain and the classification and clustering models used to clean data. There are distinct differences between data science and machine learning, but there are enough similarities to make transitioning from Data Scientist to Machine Learning Engineer, or AI engineer, relatively easy.

Beginning a Career As a Machine Learning Engineer

Data science students interested in machine learning can concentrate their training and research on the engineering of algorithms and artificial intelligence. Machine learning is a subset of data science and engineering, and its applications differ depending on the field and industry. For example, in finance and investing, algorithms are used to trade stocks and make predictions, while in database design data scientists use algorithms to organize a database. Beginner data scientists should determine their area of interest and ensure that it is reflected in the training they choose and look for programs that offer opportunities to work on real-world problems or develop skills in a specific field. 

Aspiring machine learning engineers interested in STEM fields related to automation and artificial intelligence can train in software engineering and development in preparation for careers in product or service development. For example, social media and technology companies use machine learning to automate recommendation systems and web scraping bots. Advanced training in Python for data science and statistical analysis also provides in-depth coverage of the programming and models used to develop machine learning algorithms. Ultimately, many skill-development paths can lead to a successful data science career in machine learning.

Want to Learn More About Machine Learning?

Whether you are a beginner in the data science industry or a seasoned professional, Noble Desktop's data science classes offer several bootcamps and certificate programs that pair data science skills with training in machine learning. The Data Science Certificate includes training in Python, SQL, and Machine Learning for an introduction to data science. Beginner data scientists interested in pursuing a career in Python engineering can also take the Python for Data Science & Machine Learning Bootcamp. These programs include hands-on training in data science tools and the creation of a data science portfolio that demonstrates your skills to future employers.