How to get into data science after mechanical engineering?

Data science is one of the most important and talked-about fields today. Many professionals from different fields of study, including engineers, are looking to learn data science. As big data and advanced analytics become increasingly popular and utilized in today’s world, there is a great demand for professionals who can analyze large data sets, find patterns, and solve various business problems.

Data science can be defined briefly as the use of statistics, data analysis, and machine learning. A major part of data science is based on statistics, which offers the tools needed to gather, organize, analyze, and interpret numbers. Data analysis is concerned with processing raw data to gain meaningful information. At the same time, machine learning involves algorithms that enable a system to learn from data and subsequently improve its operation without having to be programmed.

Mechanical engineers who design, analyze and manufacture physical systems know analytical skills, problem-solving skills, and a strong mathematical background, all of which can be easily migrated into the data science field. However, changing the workflow to follow a career in data science means gaining certain new abilities and knowledge. In this blog, we will look at different ways of transitioning from mechanical engineering to a data science career path.

How do data science courses help mechanical engineers in making a career switch?

The following section describes the steps for entering data science with a mechanical background.

1. Understand the basics of data science

The process of becoming a data scientist for mechanical engineers begins with getting an overview of the field. Introductory courses in data science can equip students with data structures, statistical analysis, probability, and data manipulation tools. It assists engineers in gaining initial insights into the key aspects of data science, enabling them to understand how data can be utilized to drive decisions across diverse fields such as production, procurement, and product design.

Data science courses may also include practical problems, real-life cases, and scenarios that an engineer can easily relate to, unlike computer-related problems that are complex and require computer engineers to solve using new algorithms.

2. Acquire programming skills

Programming is essential in data science, and mechanical engineers need to develop proficiency in languages like Python and R, which are widely used for data analysis, machine learning, and statistical modeling.

Python, in particular, is favored for its simplicity, readability, and robust libraries, such as Pandas, NumPy, Scikit-learn, and TensorFlow, which support data manipulation, machine learning, and deep learning tasks.

Courses designed for beginners often start with the basics of programming, gradually introducing more complex concepts and applications relevant to data science. This learning process allows mechanical engineers to build confidence in their coding skills and apply them to data-centric problems, bridging the gap between theoretical knowledge and practical application.

3. Gain knowledge in data analysis and machine learning

Mechanical engineers often rely on mathematical models, simulations, and optimizations to find solutions to complex problems. Data science courses expose engineers to a new world of data analysis and machine learning, where similar analytical skills are used to find patterns, forecast results, and make smart decisions.

Data analysis courses provide engineers with knowledge of data cleaning and preprocessing, along with graphical tools that help them discern patterns and make relevant conclusions. Supervised and unsupervised learning, neural networks, and deep learning form the basis of machine learning courses, which help engineers understand predictive modeling and automation.

4. Work on projects

The data science curriculum is diverse and contains practical exercises, case studies, and final projects that enable students to use data science concepts in real life. Some core examples include working on data science projects like predictive maintenance, quality control optimization, and energy consumption forecasting. These kinds of projects serve as evidence that students have understood how to use data science concepts in real-world business scenarios.

5. Take specialized courses

After mastering the basics of data science, mechanical engineers can benefit significantly from taking specialized courses in one or two specific areas, such as machine learning, data visualization, or predictive analytics. 

These specialized courses help deepen the understanding of selected topics, allowing engineers to gain expertise in areas that align with their career goals. By focusing on advanced subjects, such as neural networks or time-series analysis, engineers can develop niche skills that set them apart in the job market and enhance their ability to solve industry-specific problems using data science techniques.

6. Network with professionals

Networking is fundamental when changing careers or looking for an upgrade in your existing career. Networking offers students a chance to interact with established data scientists, tutors, and fellow learners who can recommend, teach, or recommend leads for jobs. To this end, actively participating in offline and online forums, webinars, and data science meetups or hackathons can contribute to building an engineer’s extensive professional network.

Being in touch with your network can keep you updated on the latest market news, tools, strategies, job openings, and best practices to adopt for a career switch to data science. You can also choose to collaborate with these individuals on data science projects and ask for feedback on your projects.

7. Tailor your resume and apply for jobs

The following are tips on how mechanical engineers with an interest in data science should draft their resumes and apply for jobs:

• Most data science courses have a career support component that assists learners in writing their CVs, learning interviewing techniques, and securing internships or full-time jobs.
• Highlight data science skills for mechanical engineers that you have learned, such as programming, data analysis, and machine learning. Emphasize hands-on project experience and showcase your ability to solve data-driven problems, particularly those that align with the industries you’re targeting.
• Connect with professionals on LinkedIn, attend industry meetups, and join data science forums. Use job portals like LinkedIn, Glassdoor, and specialized data science platforms to find openings that match your skills and interests, and consider reaching out directly to recruiters for a personalized approach.

Also read: Types of resumes for data science aspirants

8. Prioritize continuous learning

Data science is an emerging discipline; therefore, it is crucial to keep learning even after you have completed your courses—to update your information and knowledge. Mechanical engineers must familiarize themselves with data science, AI, and related technologies by enrolling in more advanced courses, attending workshops, or obtaining certifications in their data science career path.

Online course platforms are known to revise their curricula periodically to incorporate industry changes, meaning that engineers can easily update their knowledge and stand out from potential competitors by continuously learning.

Also read: Essential data science interview tips: Master your next job interview

Can mechanical engineers get into data science careers?

Data science presents mechanical engineers with an exciting opportunity to switch or enhance their careers. Mechanical engineers already have a strong analytical background and strong skills in mathematics and problem-solving. On top of that, data science courses qualify them to decipher data patterns and outcomes and how to use data in businesses.

Some organizations appreciate the combination of skills that engineers who study data science bring. Many organizations in the manufacturing, power, automobile, and aerospace industries prefer mechanical engineers who have knowledge of both the sciences. Thus, data science is a great field for mechanical engineers to enter.

Make a seamless transition into data science with online courses from MAHE!

The MSc in Data Science (MSc-DS) offered by Manipal Academy of Higher Education (MAHE) is for professionals who plan on making a career in data science. By combining machine learning, big data analytics, statistics, and visualizations, the program aims to equip learners for analytical and leadership positions in multiple industries. It aims to improve students’ understanding of machine learning applications, predictive modeling, and data-related problems and solutions to prepare them for a fruitful career.

MAHE’s online courses offer a flexible learning environment tailored for working professionals, allowing mechanical engineers to upskill without disrupting their current careers. With access to experienced faculty, hands-on projects, and a focus on real-world applications, these courses provide a comprehensive learning experience that equip learners with the skills needed to thrive in the competitive data science field.

You may like to know more about the importance of MSc Data Science in the AI era.

Conclusion

Changing your career path from mechanical engineering to data science is a wise decision that yields many prospects in the current data-driven world. Undergraduate and postgraduate data science courses complement engineering skills and help engineers transition to successful data science careers. Through constant learning, working on projects, and networking with people in this field, mechanical engineers can smoothly transition and excel in data science careers.

Also read: Choosing the right data science program for your career: Tips and strategies