Hoping to make a career switch to data science, there are a ton of questions to tackle: Which languages should I learn? Which skills do I need? Should I shell out money for a training program? But most of all, you might be wondering, Where do I start?
With this article, we hope to provide a starting point.
The dominant traits of anyone who has the goal to become a data scientist include an intense curiosity and the dedication to seek for information.
Therefore, coming from the best, it’s clear that you don’t have to be the most technically-sound person in town to become a data scientist. This should come as an encouragement for all of you out there who are from a non-technical background and do the same thing.
Here is a simple yet effective tips for those who want to transition from a non-technical background to become a data scientist.
It would be highly recommended to enroll for a well-curated course. An ideal curriculum should cover the basics of programming in Python and R and, deep learning, data visualization and Big Data handling, Statistics, and probability.
The best part about having a degree in data science that it would not only amp the value of your CV but also enhance your knowledge in the field through several assignment and examinations.
The most important first step is to speak and think like a Data Scientist. What does that mean? First, learn how data scientists speak. What terms do they throw around frequently (e.g., scikitlearn, matrix-factorization, eigenvectors)? Don’t be afraid, just take notes on the words you don’t understand. Why? Learning the vocabulary is the first step in learning and communicating data science.
I eluded to this a bit earlier but, learning by doing is ultimately the best way to learn. Spend time looking at the kernels in Kaggle competitions to learn from how other Kaggler’s approached the competition. At first, this will be extremely daunting, you won’t understand 95% of the code you’re reading, let alone, you probably won’t be able to run the code on your own computer even after you’ve cloned it.
The most important part of Kaggle to an aspiring Data Scientist is the “Kernels” section. Here, fellow Kaggler’s post their solutions to the problems posed by the competition. Spend at least an hour of your time, TYPING and CODING out their solution — practice typing each line, line-by-line in your own Jupyter Notebook. Run the code and see what happens
This is where you need to be persistent.
You aren’t going to learn anything if you get frustrated, so ease yourself into engaging with these challenges and soon enough you’ll be able to understand the kernels you read.
Remember, when setting goals, be realistic about them (e.g., SMART goals): Specific, Measurable, Attainable, Realistic, Time-Bound (SMART).
In other words, don’t think you’ll be reading Kaggle kernels within a week.
Give yourself a specific, realistic and time-bound goal —
Set small goals, write them down and check them off when you achieve them. When you feel frustrated, go back to these checkmarks and see how far you’ve come since yesterday.
Find a project you’re passionate about, whether it be a problem you’d like to solve or a library you’d like to learn — turn this into a project that you’ll put onto your github as a portfolio piece.
Finding a problem is best done through conversations. Engage with your community, your friends or… Even strangers. Find out what bothers them, or talk to them about ideas you’ve always had.
Hash out your idea, make it simple. Your project isn’t going to change the world. The most important part here is to start on one.
When you step into the field of Data Science, you are more likely to have peers or superiors in the field with a STEM background. Remember that to become a data scientist, knowledge of certain core subjects is indispensable. Although it’s encouraging to know that willpower can get you anywhere in life, there has to be a methodical approach to what you do.
Strengthen your basics and read up on all that you can get your hands on related to data science. Understand that you are never going to finish learning, but you have to keep up the spirit of intellectual curiosity at all times.
This mentality will make your transition from a non-technical field to data science both hassle-free and interesting! For more inspiration, check out this link on real-life examples of people who made it in data science despite their non-technical background.
A career transition is never easy, especially if you’ve just begun your journey. During my transition, I kept this quote close to my heart:
“The best time to start was yesterday, the next best time is NOW.”
The fact that you’ve read this entire article and are engaging with this sentence today, should show yourself you’re ready to start your transition.