Engineers, for sure, make a unique breed. Their consequential acts of leveraging the rules of our world to build things that nature readily didn't give us are what led to the advancement of humankind in the first place. Due to the very same reason, the field of engineering is never going out of demand.
Today, thousands of engineers believe it's important to be data-savvy. Why is this so? With the massive wealth of data available to them today, engineers can strengthen the quality of their work. Of course, the transition from engineering to Data Science is not easy. But why is it still worth the effort? Why are so many engineers transitioning to the in-demand field of Data Science? Keep reading to find out.
It has been observed that Data Science is a domain in which technical professionals can shine well. Countless industries across the globe are making the most of the data they have at their disposal to beat their competition. In short, a data-savvy engineer will have a competitive edge over engineers who aren’t.
The Data Science industry is already on the lookout for skilled engineers
Data science combines statistics, mathematics, programming, machine learning, etc. Engineers are already adept at math and statistics; some of them are also at programming.
Here is why the Data Science industry is in a hot pursuit of skilled engineers:
- Engineers have a solid understanding of various data systems.
- Engineers can author readable, simple code.
- Skilled engineers can use their engineering skills to maintain data quality.
- Software engineers are good at detecting inconsistencies within data sets and have great data-cleaning skills.
- Engineers can increase both productivity and boost algorithm code quality.
Engineers Need Data
No matter what engineering one pursues, all engineers deal with data on a daily basis. But, the fact remains that not all of them have the technical know-how to manipulate large amounts of data to their advantage. This is why Data Science skills become a valuable credential in every engineer’s resume.
For instance, civil engineers can use data mining, data analysis, and sensing to examine the conditions of infrastructures above and below the ground. Electricity grids and water systems can be maintained better with data mining and machine learning.
Chemical engineering is also slowly gravitating toward Data Science. Modern-ay plants tackle massive amounts of complex data. As a result, this has made data storage, data analysis, and data visualization some important skills in the chemical engineering industry.
How can an engineer transition into Data Science?
Here are some steps to follow if you want to become a data-savvy engineer:
- Join a Data Science Bootcamp
Bootcamps are very intensive training programs that focus on hands-on learning. They put applied learning before theoretical learning. They are also a better alternative to conventional degrees to acquire the most sought-after skills in the world of data.
- Understand the business domain
Of course, an engineer with a programming and statistics background can train a model effortlessly. However, a Data Science professional is also responsible for making critical decisions based on the insights extracted from data. This is where his/her business acumen steps in.
An engineer who knows the following will be able to use data better:
1. The source of the business problem
2. An in-depth understanding of how the business operates
3. And data collection mechanisms
- Gain ample hands-on experience
By taking up personal projects, participating in hackathons, and doing open-source projects, one can gain hands-on experience in wrangling real-world data. Another way to get hands-on experience is to join a Data Science Bootcamp.
With data science skills, not only can engineers make sense of data, but they can also make the most of all that valuable business intelligence.
If you look forward to becoming a data-savvy engineer, join OdinSchool's industry-aligned, job-oriented Data Science Course.
