FIVE Myths about Data Scientists
Have been working as a data scientist for a few years, I’ve heard some myths/misconceptions about data scientists. Today, I’d like to share the top 5 myths that I frequently heard about. Hopefully, this could provide with you some facts about data scientists by breaking out the myths.
Kindly refer to my YouTube channel for detailed information!
1. Data Scientists are well-paid
Well…It depends on what kind of industry you’re working in right now, which state you currently reside in, and the years of experience. Based on the salary data published by Indeed, on average, a data scientist in the United States makes about 120K per year.
As I mentioned earlier, the salary varies from region to region, and from industry to industry. For example, a data scientist in New York City makes about 140K per year, while the one in Jacksonville makes about 110K per year according to Indeed. Also, take Google for example, on average, a data scientist can make about 170K per year. Yet, as you can see here, the salary range varies a lot from 42K to 280K, which confirms that the variance in salary will not only based on the type of industry but also years of experience (ref link).
2. You need to obtain a doctoral degree to be a data scientist
Na-na. Trust me, you don't have to obtain a doctoral degree to be a data scientist in the real-world. If you take a look at Indeed or LinkedIn, indeed you would see a couple of companies specifically require a doctoral degree for data science related jobs. Yet, believe me, it’s not always like that. My friends and I only have master’s degrees, and we all work in big companies, so don’t be upset if you don’t have a doctoral degree. It might be good to have, but it’s definitely not required.
Another proof that I can provide is the analytical results from my previous YouTube video using the Kaggle survey dataset, which was sent out in October 2020 and got 20K valid responses back. Based on the demographical data analysis, the top two highest levels of education for data scientists worldwide are master’s and bachelor's degrees. Hope this can give you some confidence. Remember, you may pursue a Ph.D. if you enjoy the process of research in a field, don’t do it just for being a data scientist.
3. The day-to-day job of a data scientist is to build a perfect model
Not really. There are multiple steps required before you start building a model. Normally, each data science project begins with data preprocessing, which I would say takes about at least 70% of the time, and followed by correlation analysis, visualization, and then building the model. Hence, the day-to-day job of a data scientist is not just changing parameters to build some fancy models.
4. Coding skill is all you need to be a data scientist
Of course not but don’t get me wrong. As I mentioned in the Top 5 Must-Haves to Be a Data Scientist (Blog, YouTube), coding skill is critical for sure, but it’s not the only skill that a data scientist should have obtained. The ability of coding is important, but the ability to tell a good story is more important. Storytelling is the essential soft skill that can help you transform the data into useful information for your audience.
5. AI will eventually replace data scientists
This myth pops up in my daily conversation a lot. People always ask me about — Wouldn’t you guys as data scientists will be replaced by AI eventually? My answer is always a hard NO. AI is more like a tool for data scientists as it provides high computational power which neither of us can achieve with our own brain and never get emotions involved. However, without data scientists, AI won’t know how to automatically resolve data quality issues, imputing missing data, selecting suitable algorithms, and tuning the parameters based on various scenarios. Also, when it comes to visual recognition and/or text annotation, AI won't produce a high performance without data scientists.
That’s it! Thanks so much for your time and please smash the clap button if you like my post! Be sure to check out my YouTube video as well!
