Why do Self-taught Data Scientists See Slow Progress in Their Career?

Published on:

28 Jun 2022, 4:00 am

Exploring the reasons behind the slow career progress of the self-taught data scientists.

The sexiest job title for data scientists is not a hoax at all! 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. Experts believe that learning data science skills will help candidates add value to any role, giving job seekers with this skill set an edge over the competition. If you're currently in a department like marketing or finance, for instance, studying data science could open new career doors for you. However, even though it leads a lot of people to self-learn data science skills, most of the time it turns out to be a failure in the real world. Here are the reasons why self-taught data scientists see slow progress in their careers.

Making Your Own Curriculum

The concept of self-teaching means making your own curriculum and also finding out what to learn or what to read by yourself. At the beginning of any learning, it's quite impossible for the student to fathom the vastness of the subject or the right kind of books and resources that are required for it. So, it takes them a long time to learn from their mistake which tends to slow down their progress in learning that particular subject.

A Plethora of Too Many Ideas

It's very easy to get lost inside the maze of the internet. From youtube to many websites, there is so much information about the self-teaching of data science that newbies won't be able to tell apart the real from the gibberish. There is no way to filter through the plethora of scattered information across the internet and this can be very misleading to someone with little knowledge of the subject.

Lack of Job-oriented Focus

When someone new is starting to self-learn data science, he or she does not always focus on which particular job to apply for. If anyone starts learning a programming language all of a sudden and on completion of it, he or she will not be fit for all the job roles that come with the name of data science. This scattered approach to learning makes the students slow in the path to getting any particular job in this field.

The Tutorial Trap

Another very common trait among self-taught data scientists is to attend online courses. These courses come with resources like books and also tests. However, the problem is similar to the one with the abundance of information on the internet. Even though taking courses is probably the smartest way to self-teaching a subject like data science, for beginners it might be difficult to recognize the right tutor. To keep the interest in the subject flowing, the tutor can easily teach some cool tricks and it might make the learners think that they have learned a lot and making progress, but most of the time, that is not the case. If anything, it hampers the self-taught learners to have consistency in their progress of career growth in data science.

Lack of practical experience

For the 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 practice enough to become an expert in the basics often slows down the career of the self-taught data scientist.