The demand for data science and machine learning jobs is rapidly rising and the gap between this demand and the number of data scientists available is still very wide. Now, people from an engineering background and pure sciences are shifting their careers in the field of ML. Physics is one such background which falls into this category because of the high level of logic and mathematics required in an ML job.
Physics research requires dealing with a lot of data, just like ML. Physicists are also proficient in at least one programming language — most likely Python, as it is popular in the Physics community as well. There are many physicists today who are data scientists. ML is beneficial for them for their project in many ways. Many from this background are turning their careers towards pure ML. According to a survey, two-thirds of the respondents in data science field had academic backgrounds in computer science, mathematics, statistics, or physics.
Here are some different ways in which a Physics career or a Physics degree can be transformed into an ML one.
Why Shift To Machine Learning
1. Opportunities: The number of opportunities available as ML experts are way too many than opportunities in Physics. Physics also has a plethora of fields that they can work in, from nanoscience to cosmology, but the number of physicists is also large. For ML, the number of experts in the field is not many and the demand is high. It then becomes easy to secure a job in this field.
2. Salary: ML experts earn good pay packages across the world. Physics, in spite of being a challenging and evolving field, does not have similar salary packages. A salary study by Analytics India Magazine found out that the median analytics salary in India for the year 2017 is ₹12.7 lakh across all experience level and skill sets, which was the highest median salary for analytics professionals ever, with almost 8% increase since a year ago.
3. Full use of your knowledge base: The skills that someone with a Physics career acquires can be easily put to use in ML, due to its ability of understanding logic and high level of mathematics, something which is extremely mandatory in ML. Arno Candel of H2o.ai had said in an interview, “If studying physics teaches you one thing, it's logical thinking — and that's quite useful when you’re trying to teach a computer what to do.”
What To Do To Transform To ML?
Usually, companies look for skills, rather than qualifications for hiring ML engineers. For example, Dennis Ritchie, the creator of C programming language also had a degree in Physics and Applied Mathematics from Harvard.
Here’s what you should do to take that jump:
1. Internships: Internships are a good place to start. Big companies will be slightly hesitant to hire you if you are from a Physics background. Doing an internship in ML will give them a feeling that they can trust you with your job as ML engineers, in spite of having a Physics background. Once you proved your skills, it becomes imperative for them as well to impart their trust in you.
2. Online courses: They are great ways to kickstart your learning. There are plenty of beginner well-reputed courses on platforms like Coursera and Udemy and other platforms that can help you big time to crack into this field. There are various schemes that recruit science PhDs and train them for a few weeks for tech jobs.
3. Read books: It is important to also understand a bit of a theoretical knowledge of the subject before forming a career in it. Books are a great way to help you begin with it. Although online courses would serve the same purpose books will also add value to your skills. Here is a list of some of the best books on ML that are available for free. This can be an alternative to online courses.
4. Online competitions: This is one of the most recommended steps to take. Online ML competitions give a great exposure to the practicality of ML and they don’t even demand your qualification in your field. Some platforms like Kaggle are so popular and so well made that you also gain popularity and it adds a good value to your CV as an ML job applicant.
5. Attend webinars: There are many ML career-related webinars hosted by experts and free to attend, to help guide people to start their career in ML. These webinars can help you with many opportunities and the skills that you may need to form a career in the field. Big giants like Amazon also hosts webinars to help you.
6. Collaborating with projects: Collaborating from simple to big projects will give you a good opportunity to live a life of an ML expert, but with a team, so that you have a good amount of guidance and don’t have to do everything ML by yourself. You will know how ML projects work, thereby training you for a career.
7. Build a strong LinkedIn connect: This is a very small, but very fundamental in getting connected to people in ML. Most physicists do not tend to have a LinkedIn profile because they do not generally need to. But in a career of ML, where Linkedin has a very active community, it is important to have a profile. The data science and ML community on LinkedIn is very large and the platform can serve as a powerful tool to learn from people already in the field.
A Physics career can open up many opportunities in the realm of technology. The options might vary based on the expertise, but ML is a well-suited career option for physicists. When asked about making the transition, one of the top Kagglers Owen Zhang shared it is not easy to reinvent the wheel. He also said that physicists are more suited for mathematical or scientific problems of ML.
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Found a way to Data Science and AI though her fascination for Technology. Likes to read, watch football and has an enourmous amount affection for Astrophysics.