How to become a self-taught data scientist at FAANG in 2022?
Data science, once thought of as a novel industry, has almost established itself as an essential part of day-to-day businesses, and hence the growing demand for data scientists is understandable.
But when it comes to the question of actually pursuing a data science career, many questions haunt you – if a career in data science is sustainable in the long term, what are the challenges, and so on so forth. The very first dilemma that surrounds is if you can land a job as a data scientist with FAANG companies, which is a dream come true for many job aspirants.
Well, it is a matter of opportunity, and opportunities choose only prepared minds. Having said that, it is necessary to understand that it is not required to have a degree from an elite college or an institution to secure a data scientist job. FAANG companies have long left the practice of choosing candidates based on the degrees or certificates they have. Instead, the focus now is on skill and outlook-based approaches in their selection process.
Is it difficult to learn data science all by yourself?
Going through the curriculum of a data science program certainly would be intimidating for people who do not have a math or science background. Truth be told, it is definitely difficult but not impossible. The math and stats part of the syllabus requires a basic understanding of the concepts which we might have already studied in school.
Some dusting here and some brushing up there would do to safely pass through this difficult part. Once done with that, move on to the programming part. Though it is considered an exclusive area for science students, non-science students can master too if they are curious enough and are prepared to practice.
Languages like Python, and R are user-friendly higher-level languages that have easy-to-understand syntax. Data science tools like matplotlib, Tableau, and TensorFlow help in processing and visualizing data. A number of sources are available online in text and video format.
Here it is important to remember that these sources only provide a basic understanding of the concepts but do not help in gaining practical expertise. Believe it or not, it makes for 70% of the journey. The rest of the topics are almost theoretical and do not even count for the learning process as it is in doing where the joy of learning data science lies.
Give the process a head-start
It doesn’t matter where you start but it is important to start. David Joyner, Ph.D. Executive Director, Online Education & OMSCS, College of Computing, Georgia Tech says, “I think the best way to learn is to take a computer science class, learn what’s possible and then decide, ‘Using what I’ve learned here, what could I build that would be of strong personal use to me?’ Even if it’s just a personal project.”
With data science, it is extremely difficult to choose the so-called ideal path to mold your knowledge to the desired level. In self-learning mode, the learning curve is often erratic, distracting, and makes students lose focus.
This is because there is a mad rush to learn everything under the influence of FOMO. You need not master every topic in data science to become a data scientist.
Master the art through practice, the way most data scientists learn even though they complete a full-term course. Join data science online communities to find projects to work on and collaborate with others on the platform to not only enhance skills but also to keep up with motivation.
When you aim to secure a job at FAANG company, having as many projects as possible in your kitty is a must because these companies only look for your thinking capacity and approach to problems. Your resume should speak for itself with the number of projects, boot camps, and hackathons you have won or been to.
Ciara Brocklebank, Technical recruitment manager at Facebook, New York talked about what they look for in a potential employee, she once said to Businessinsider, “It could be a solid academic background, relevant work experience, interesting side projects, and contributions to open source projects, just to name a few.” This is not an isolated case when recruitment is pivoted around abilities rather than around degrees, nor the ideology of FAANG companies has changed.
Source: Analytics Insight