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. In the words of DJ Patil, the current Chief Data Scientist of the United States and previously the Head of Data Products at Linkedin, a “desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested” is a must-have for any aspiring data scientist.
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.
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Here is a simple yet effective 6-Step guide for those who want to transition from a non-technical background to a become a data scientist.
1. Upskill Yourself With A Planned Course
For novices in data science, it would be highly recommended to enroll for a well-curated course. An ideal curriculum should cover the basics of programming in Java and R and, deep learning, data visualization and Big data handling, Statistics, and probability.
This is an example of a well-structured curriculum in Data Science.
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. In the absence of an organized syllabus, it would be a herculean task for anyone to collect and collate necessary materials to kickstart self-study.
2. Get In Touch With Mentors In The Field
When you start afresh in any field, it’s hard to navigate on your own. That’s when having a mentor to guide you becomes significant. The same applies to those moving from a non-technical background to data science.
The perks of having an experienced mentor with not less than 5 years of experience in the field are plenty. These range from great networking opportunities, gaining valuable lessons from those who have gone through highs and lows in their careers and having a sounding board for goals, ideas, and questions.
Check out Acadgild’s online courses in Data Sciences that have seasoned mentors assigned to each student.
3. Don’t Miss Data Science Events In Town Or Online
Attending Data Science events is a great way to not only gather the latest information in the field from industry experts but also to network with the who’s who in the field. You never know how crossing paths with someone could benefit you or your organization in the future. In-person events are irreplaceable even in the age of social media. Therefore, stay open to conversations.
4. Attend Mock Interviews
If you are keen on getting placed in the data science domain, you should also be aware of what the hiring managers are seeking. Mock interviews are the best way to gauge your expertise level. Some courses have career transition assistance that provides placement support to candidates. Here’s one from Acadgild.
5. Don’t Compromise With The Basics
Any data scientist swears by knowing a programming language like R or Python, statistics, and basic mathematics as integral to the foundations of learning. Familiarity with the above fields will attune your intellectual capacities to analyze and interpret data better.
6. Remain Forever Open To Learning
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.