Almost 1.7 megabytes of data is generated by every person each second. Every app or service you use today is driven by data. Thus, being able to wade through these gallons of data has become essential to all business decisions. In the 21st century data scientist has become one of the most desired jobs. Being an engineer makes for an added advantage to undertake the domain of data science. Therefore, easing the process of switching from mechanical engineering to data science.
As per the US Bureau of Labor and Statistics, the demand for data scientists is expected to grow by 15 percent by 2029 which is reported to be about 11% more than the average growth of other occupations. Data scientists come from different backgrounds, but are mostly accommodative for engineers especially mechanical since it overlaps explicitly with data science in a lot of ways. Before understanding how to switch from mechanical engineering to data science, let us first get a holistic difference between the two.
Data Science and Mechanical Engineering use different technologies, tools and have different education paths. Therefore, it is imperative to distinguish these two areas of expertise before switching from mechanical engineering to data science.
Data Science
Mechanical Engineering
Education
An undergraduate or postgraduate degree in a relevant discipline such as Information Management, Computer Science, Economics, Business information systems, Mathematics and Statistics.
You need to have many years of experience in a particular ME concentration to be considered a skilled mechanical engineer. However, a higher education degree is not always needed.
Tools
There are a wide range of tools in data science and your choice of tools might differ from the rest of your team as there is no particular way of getting your work done.
Has a limited set of tools designed for hardware design, machine control, etc. and they don’t need frequent updation as they remain valid for a long time.
Technologies
Given that it’s a wide field, you need to keep yourself updated with the changes in technology that take place frequently. This is not just required for artificial intelligence but also in software engineering.
The technologies in the field of mechanical engineering are not subject to frequent changes. For instance, technologies like PID and Kalman are quite old but still in use even today. Therefore, you don’t have to be on the top of the game to keep yourself updated.
Problem-solving
The scope for problem-solving requirements in data science is wide. It seeks to solve problems by analyzing the data and since it is unpredictable, so are the solutions.
Unlike data science, most of the problems in mechanical engineering are well-defined and have tested solutions. Since machines are quite consistent and predictable, and you need to choose the right tools and solutions as a main part of your job.
How is the transition from Mechanical Engineering to Data Science?
Switching from mechanical engineering to data science will be possible once you realize all things you have learnt with an ME background and then strategically develop the new skills required in data science.
The skill sets required in data science are categorized into three cohorts: Mathematics / Statistics, Domain Knowledge and Programming. Each of these sections will be assessed on a “re-usability score”, so as to explain how hard it would be to re-use the knowledge gathered in ME to data science.
Programming – Reusability score for data scientist: Medium, for ML Engineer: Hard
Three things to consider before switching from mechanical engineering to data science
1. Career opportunities
At present, the demands for data science professionals have reached a record-breaking height. The Emerging Jobs Report of 2020 by LinkedIn suggests that the domain is expected to witness an increase in data science. On switching from mechanical engineering to data science, you will be able to join the IT sector, FinTech, Travel and Hospitality, Healthcare, e-Commerce, Banking and Insurance, Agriculture and Retail.
As per a report by PayScale, a data science professional can expect a handsome remuneration. On having 1-4 years of experience, you can earn INR 5-7 lakh annually, and with 5-9 years of experience,the average yearly salary can range upto INR 13-14 lakhs per year.
2. Best mode of learning
There are several options available for fresh graduates and professionals looking to upskill themselves in the data science domain. There are both in-person classroom sessions offering real-time engagement as well as online courses with live mentor-led teaching.
3. Most useful tools that can be learnt by a beginner
Some of the most popular tools that are used in the industry and should be learnt by data scientists are MS Excel, Tableau, Python, Google Analytics, MATLAB, R, TensorFlow, BigML, Apache Hadoop, SAS and Apache Spark.
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