Ph.D. Entering Data Science? Avoid these Mistakes.
A five minute read that can save you a lot of time and trouble in landing your first data science job.
With all the gloom in academia (lack of funding, mental health risk, scarcity of bountiful academic careers), and all the buzz around Data Science many Ph.D.’s consider Data Science as an alternative career path. If you want to avoid some of the common pitfalls spend the next 5 minutes reading this.
You can listen to my interview with Cheeky Scientist Radio on my journey to Data Science. I wrote this about four years ago and it still rings true in the current climate.
Is Data Science For Me? DON’T DO IT IF…
- You don’t like mathematics or have a good foundation in linear algebra and multivariate calculus? If this is the case or you don’t have the appetite to learn the basics, perhaps this is not a space that you want to be in. While coding and playing around in Python, R, Scala, etc. ask yourself if it is enjoyable. If you don’t find it to be fun, then you have your answer and can stop reading the rest of this post. At the end of the day, you have to read a lot of papers that are heavy in mathematics.
- Don’t like staring at a monitor, coding, or cleaning data? This will be a relatively big part of your day. If you don’t enjoy it, you won’t thrive in this field. Period.
- Don’t like talking in laymen's terms? You have to communicate your results in a way that is easily understood and actionable for all team members and upper management. Very few people may care about how you got the result, but you have to be able to communicate it clearly. If you don’t like talking and working with people of different backgrounds, this is not a great career for you.
DON’T Chase the Technologies, Get the Foundations Right!
Rather than chasing different technologies, try to get the basics of data science down pat. This includes probability, linear algebra, machine learning, etc. R, Python, Scala, Hadoop, Spark, Cloud Computing, etc. are all helpful skills to have but you shouldn’t keep chasing them. Initially, you should be knowledgeable in the basics before asking yourself, “Is it better to learn R or Python?” Chasing technologies will only overwhelm you because many job posts have a laundry list of them and you can learn many of them quickly on a job.
While we are on the subject of technologies, one of the most overlooked useful tools is SQL. Learn as much of it as you can and practice it. It will help you immensely on your first job!
Online Courses are great but NOT Enough!
Online MOOC websites have revolutionized learning by democratizing it. They are very good to get you started but by no means are enough to prove to your future employers that you can help their business solve their problems. You need to expose yourself to real-world problems where the data is not well-curated, the question is not clear, and you may have to even define the success criteria! One way of improving your skills and getting a better feel of what you will face in business is through consultation jobs/gigs. Start your own consultation gig and see what are the problems that business owners actually have. Try to get your hands dirty by looking into those problems.
DO Interview as Much as You Can!
One way to hone your interviewing skills and more importantly find the gaps in your knowledge base is through interviews. When you do a job interview, you have the opportunity to sit with an expert for half an hour to several hours and learn from them. It helps you find where you need improvement.
Basically, you study and practice new skills, you progress, you interview, you use the feedback, you study more, and you become better and after some iterations, you become a better data scientist.
Feel free to join a program that is helping 500+ Ph.D.’s who are transferring to Data Science. You can use code “shobeirdss2020” to get 30% off.
