Should I Learn Data Science as a Software Engineer?

Most Machine Learning Engineers are in high demand as several industries expand their development, use, and maintenance of a wide array of applications. So, if you are asking yourself, "Can a software engineer become a machine learning engineer?" – the answer is yes.

So, if you already have some coding experience and curious about machine learning, you should explore every professional avenue available. If you want to use your machine learning skills to break into a growing industry of Data Science, do go that extra mile and gain the knowledge needed rather sooner than later. Education industry is currently booming with online options, so you don’t have to quit your current job while getting those in demand skills. 

Companies all over the world are exploring different ways to collect and apply various available data. They are in need of skilled engineers and are willing to invest in talent. At Game 7 Staffing we’ve seen a widening gap in supply vs demand of skilled professionals. We are constantly on a lookout for these specialties, which have a similar foundation in terms of core skills. 

Difference Between Machine Learning Engineer, Data Scientist, and Software Engineer

Of course, there are not just similarities, but also differences between these three specializations. If you are wondering how to break into data science or how to use artificial intelligence in software engineering, we have a few simple explanations for you.

Data Scientist

A data scientist needs to have a background that incorporates knowledge of:

• Programming skills
• Data and business analysis
• Data engineering
• Research on topics such as new neural net architectures and algorithms.

A data scientist's role is to analyze and process data, create models of the available data, and interpret the results. These results are used by companies to develop strategic organizational and business plans.  

Software Engineer

A software engineer writes code to run computer systems, as well as applications. Depending on the programming level, a software engineer must also be proficient in hardware, operating systems, and software application development.

Machine Learning Engineer

Machine learning engineers create a bridge between software engineers and data scientists. If you are thinking about machine learning engineer vs. data scientist, you should know that the two roles frequently work together. The machine learning engineer will feed the data into models created by the data scientist and then design the engineering system that serves them.

Additionally, a machine learning engineer will scale a model to handle vast amounts of data to support advanced systems and applications. According to a report prepared by IBM, the most crucial programming languages a machine learning engineer must master are:

• Python
• Java
• C/C++

Transitioning from Software Engineer to Machine Learning Engineer or Data Scientist

Can an engineer become a data scientist? The answer is yes. A large part of machine learning involves a skill set similar to data science. A data scientist is usually required to possess one or more of the following:

• Master’s Degree or Ph.D. in engineering, mathematics, computer science, or statistics
• Advanced mathematical and analytical skills
• Ability to identify and assess risk factors
• Experience in data mining, cleaning, and munging
• Basic software engineering skills, data visualization, and reporting skills
• Understanding of how big data platforms work, data warehousing, and structure 

Whatever your current background, you will have to take supplementary courses and obtain certifications for them. Thankfully, you can find several schools that offer online courses and specializations in data science, machine learning, or software engineering.

Also, if you are asking – do data scientists get paid more than software engineers – the answer is not clear cut. It really depends! According to the 2018 State of Salaries Report, the average annual salary for both jobs is $137,000. But there are different factors in play. Oftentimes, contingent employees receive higher compensation. Ultimately, If you consider a career change, you should focus on your interests, professional growth opportunities, and the trajectory of the industry. Not remuneration alone.

Why Machine Learning Isn’t “Just Another Language”

Machine learning is not merely a new programming language. It requires a deep understanding of math and statistics.

When you become a machine learning engineer, you need to have a baseline understanding of various concepts, such as:

• What type of data do you have?
• What is their statistical distribution?
• What are the statistical models applicable to your dataset?
• What are the relevant metrics you need to optimize for?

These fundamentals are necessary to be successful in starting the transition into Machine Learning. These are typical issues when you are required to build a specifically tailored machine learning solution.

Skills You Need to Acquire Before Transitioning to Machine Learning/Applied AI 

As part of your transition from a software engineer to a machine learning engineer, you need to focus on acquiring and mastering the following skills:

Statistics 

This is a core skill for applied AI as it helps you understand:

• Ways to measure the success of a model
• Bias/variance tradeoff
• The confidence you can place on the results given by the model.

Machine Learning Theory

Machine learning theory helps you understand what happens while you are training a neural network. An excellent place to start is the Deep Learning course developed by Google and available for free.

Data Wrangling

This represents the largest part of a machine learning engineer's work and includes aspects such as data acquisition, data pre-processing, and data post-processing. Practice using datasets is the best way of developing your data wrangling skills.

Debugging

Debugging in machine learning is very different from the software codes you were accustomed to. It is also continually changing as technology evolves, so you should always keep up with the field's latest developments.

Software Engineering

Last but not least, you need to be able to test, build code, create checkpoints, and set up a distributed infrastructure as part of your job tasks.

Advice for Specializing in Machine Learning Engineering 

As you start in your new specialization, don't forget that you may encounter many moments when you ask yourself: can I do this? These moments of self-doubt are natural, but support and resources are available for each step of your transition.  

It is vital to keep your end goal in mind and approach your career transition in several steps:

• Apply machine learning at work

As you start acquiring skills, put them to test in practical tasks at your workplace. Offer your help and input in machine learning projects and listen to feedback. Do not be intimidated because you are a beginner – everyone has a starting point, and your colleagues will appreciate your collaboration.

• Start small

An old saying goes, "don't bite more than you can chew." This is very true for transitioning to a new specialization. Start with small and simple tasks, and don’t feel tempted to take on anything that exceeds your current abilities and experience. 

• Go big

Some professionals thrive when they have a significant challenge before them. If you are such a person, you should consider joining a company that works primarily with machine learning. This will expose you to a lot of knowledge, training, and hands-on experience.

• Keep learning

Machine learning is a continually evolving field. Being dedicated to staying informed and involved will help you to grow with the technology.

Finding Your Place in the Tech or Engineering Field

Machine learning engineer vs. data scientist, software developer vs. machine learning engineer…there is no competition here. It all depends on your background, where you want to focus your skills, and what fulfills you.

Whatever kind of job you are experienced in or wishing to transition to, Game 7 Staffing can help you find contract or project-based positions in emerging technology.