Why do FAANG Companies avoid Self-taught Data Scientists Big Time?

The field of data science is full of potential and opportunities. A general search on the platform of Indeed for "data scientist" returns over 15,000 data science jobs, many of which pay in the $90k to well over $100k salary range. Now, it's only natural that people have their eyes set on honing the skills of data science as they used to for doctors and engineers back in the day. Data scientist is not the only job role, however, where data science skills are valuable. FAANG companies pay a fortune for data science professionals like data scientists, data analysts, data engineers, data architects, ML scientists, data storytellers, ML engineers, etc. FAANG refers to the stock of the five most renowned tech companies in the world; these are Facebook, Amazon, Apple, Netflix, and Google. Global recognition, extremely high packages, excellent learning atmosphere, comfortable working hours, and a lot more; this is what attract job seekers to these top-notch companies. Fun fact: Amazon gets nearly 18-20 job applications per minute, Apple has only a 2-3% acceptance rate, and Google receives almost three million job applications per year, so you need to stand beyond the genius bar to land at these brands. Therefore, starting from aspiring data scientists to experts, everybody wants to get into FAANG companies. But the problem is these companies avoid hiring self-taught data scientists. Why? Let's find out.

Reasons why FAANG avoids Self-taught Data Scientists

Not having a professional degree

It's possible to develop your data analysis skills—and potentially land a job—without a degree. But earning one gives you a structured way to build skills and network with professionals in the field. You could also find more job opportunities, especially at FAANG Companies with a degree than without one. Because when you submit a resume to Big tech companies they dig into your qualification history first to check whether you are the best fit for their company or not. Forget about bachelor's many companies look for Masters in the field of data science if you want a job at their esteemed organization. The reason data scientists need communication skills is that their jobs require conveying information to colleagues. You need to spell out what the data means and how you can use your insights to work toward solving a problem. Though job seekers can often build up their resumes with certificates or single courses that focus on some of the skills they need, likely, they'll still have gaps.

Graduate programs teach students how to connect everything they've learned

More than anything, a data science graduate program teaches students how to use everything they've learned in conjunction with everything they already know. There's a great deal of uncertainty in data science. You need to be able to wield multiple tools in your toolbox at the same time to eventually come up with something concrete.

Lack of practical experience

For self-taught data scientists, there always seems to be a huge gap between learning and practical knowledge. Once they finish all of their learning, it's only natural that they will forget most of it unless they start using that knowledge for practical use. So, when faced with data science problems, self-taught data scientists generally mess up the operations. It hinders them from proving themselves properly in the field of work.

Skipping the Fundamentals

Everything interesting and considered 'cool' attract more self-taught data science than anything else. A lack of patience in first learning the fundamentals and practicing enough to become an expert in the basics often slows down the career of the self-taught data scientist.

There is still a way to get hired by good tech companies

Learn The Tools: There are many tools that data scientists can use to process, analyze, and visualize data. SAS, Apache Spark or simply Spark, BigML, Github, Jupyter notebooks, TensorFlow, D3.js, MATLAB, Excel, ggplot2, Tableau, Jupyter, Matplotlib, Natural Language Processing, Scikit-learn, TensorFlow these are the some of the tools can be overwhelming to learn to become a data scientist.

Level Up Your Soft Skills: Making a career in data science is just as much about people skills as it is technical. In the process of product development, improving customer retention, or mining through data to find new business opportunities, organizations are increasingly relying on data scientist skills to sustain, grow, and stay one step ahead of the competition.

Signing Up for Hackathons: Hackathons are events where you work on a project with other people. It helps to learn how to put all new data science knowledge into practice as well as meet like-minded individuals who were also interested in learning more about data science or had already learned quite a bit.

Learning from Textbooks: Learning from textbooks provides a more refined and in-depth knowledge beyond what you get from online courses. These books provide a great introduction to data science and machine learning, with code including Python Machine Learning.

Practice The Fundamentals: The data science method looks similar to the scientific method, but with the heaviest emphasis on ensuring that all the data used is of the highest quality. Data wrangling comprises the bulk of data science because, without quality data, your insights are meaningless, or worse, incorrect.