Are you an engineer looking to switch to a more exciting role in data science? Are you wondering if your background in engineering will help you to enter the big data industry? Are you interested in what is involved with transitioning? You will get all the answers to your why’s, when’s, and how’s below.
Which types of engineering can help you become a data scientist?
Let’s discuss how the skills of different engineering streams can come in handy while training to work in the Big Data industry –
With a background in Mechanical engineering, you will have strong skills in mathematics and physics, which can be helpful while learning various data analytics, machine learning tools and technologies. By getting trained in data science, you will gain insight on how to think from a business perspective and use the above tools and technologies to make business decisions.
As an IT engineer you will be proficient with skills in computer hardware, software, networking tools and end-to-end knowledge about systems that can act in favour while transitioning to data science. This type of engineering is one of the most common pathways to data science. Hear out what Wayne has to say about transitioning into data science after working in IT for 15 years.
Having a software engineering qualification background will take you a long way in the field of big data. One of the strongest skills a software engineer possesses and one of the sought-after skills for a data scientist is the knowledge and understanding of programming languages such as Python, R and so on. Further skills in communication, problem-solving and a business mindset will make you a perfect candidate to get trained in data science.
Telecommunication engineers bring with them a skill set in programming, advanced computing, and knowledge of different types of networks. To get into data science, telecom professionals need to additionally learn advanced statistical methods, data management, visualisation, modelling tools and technologies required to collect, analyse and interpret pools of data that bring insightful decisions for a business.
As a petroleum engineering professional, if you are confident with your quantitative and analytical skills, you are already halfway towards your next big data job. Data scientists need to have passion for mathematics, statistics, data and even physics. With current skills in understanding numbers, analysing, predicting from data sheets coupled with your business mindset will set you apart as a strong candidate for data science.
How hard is it to transition careers from engineering to data science and machine learning?
Transitioning into data science after working for a couple of years as an engineer can sound daunting since data science is bent on a statistical mindset and reasoning. To break the barrier and switch successfully to data science and machine learning, one has to get a clear understanding about the advanced statistical tools and techniques needed to extract insightful information, trends and patterns from data. Having basic computer skills and technical skills makes it is a lot easier for an engineer to change careers when compared to professionals coming from a non-technical background.
Here’s how you can transition smoothly into data science and machine learning
- Learn how to generate hypothesis and analyse graphs, plots and reasoning.
- Data science is a tool used to understand more about products, its features and utility. Learn how you can leverage the data to enhance product features.
- Get an understanding of the business domain you wish to work on. Domain knowledge helps data analysts solve problems based on the business needs.
- Before you can dive into large pool of data, explore how to clean data to get faster and accurate insights.
- Last but not the least, connect with industry leaders and professionals to get a chance to gain practical experience and network, or simply gain advice on how to face the industry by learning from their experience.
Here’s 6 reasons the data science industry is looking to hire data professionals with a background in engineering
- Data scientists with an engineering background enable them to interface with their engineering knowledge to ensure higher quality of data.
- They have an in-depth understanding about different data systems while diagnosing results of experiments and implementing the data products.
- They know how to enhance the productivity and the algorithm code quality by writing simple, performant, readable and maintainable code.
- Software engineers and IT engineers have prior experience in data cleaning and detecting inconsistencies within the datasets, this is a valuable skill for a data scientist to arrive at accurate and effective decisions.
- Some roles such as working in a small machine learning teams, companies require you to wear multiple hats. This includes experimentation, building models, developing insights and software engineering. This would be a perfect opportunity for a data scientist who is also a good engineer.
- Domain knowledge is one of the crucial skills of a data scientist that is hard to teach. It often requires practical experience for a certain period of time along with mentorship. This kind of expertise is usually found in engineering.
How to apply data science, analytics and machine learning skills on the job and continue working as an engineer
Typically, every engineer is exposed to data on a daily basis, irrespective of the type of engineer he or she is. Decisions made by engineers are mostly driven by the data collected however, the amount of data accessed by engineers is variable. More than often, engineers have abundant data in front of them, but aren’t equipped to handle such large amounts of data. That’s when data science skills become a valuable addition to their current skillset for engineers to make sense out of the data and avoid wastage of useful data.
For instance, in the case of civil engineers, data mining, sensing and analysis can assist in monitoring the infrastructure conditions above and below the ground. Data mining and machine learning is also useful for civil engineers in regulating and controlling electricity grids and maintaining water systems. Chemical engineers on the other hand, are now dealing with more complex data than ever. Chemical engineers are slowly progressing towards using data science techniques to manoeuvre through these large datasets.
Additionally, data engineering is also one of the common paths chosen by engineers- specially software engineers. A data engineer is a data professional who is responsible for preparing data infrastructure for analysis. A data engineer along with the analytical and statistical skills, has advanced knowledge of programming languages like Python, Java and so on. If you are passionate about software engineering and love programming, but want to transition into the big data industry, data engineering might be the right option for you.
Career outlook in the next 5 years for professionals who switch from an engineering career to data science and artificial intelligence (AI)
The big data industry is seeing an all time high in career growth and job demand. According to a recent study by Deloitte, The number of data science professionals in Australia will rise to 338,800 in the F.Y. 2012-22, going up from 300,900 in the F.Y. 2016-17, with an average growth rate of 2.4% per annum. The study by LinkedIn found that statistical analysis and data mining was ranked the second most in-demand skill sought after by employers who posted job advertisements.
You can now find data science opportunities in pretty much all major industries such as – Finance, IT, consulting, marketing, telecommunications, infrastructure and the list goes on. The big data industry is where data scientists and other emerging tech professionals can flourish and grow exponentially. Depending on the size of the company, a data scientist has an earning potential of $50k-$250k.
Stop waiting for the right time. If data is where your passion lies, then get started on earning the data science skills industry wants and explore your capabilities within the big data industry.