The field of Data Science is diverse, with many people from many different backgrounds, working in many different domains. With all talk about this booming industry, there has been a lot of misinformation and myths created about Data Science. Here are 6 of the biggest myths about Data Science that have been thoroughly debunked.
AI Systems can, and will, evolve and generalize by themselves after creation.
Sorry, all you science fiction fans, this myth is about to be terminated. The state where artificial intelligence starts evolving by itself is called Artificial General Intelligence or AGI, and we are not and have never been at this state yet. It doesn’t exist. Yes, there has been progress made via DeepMind and other high-level research organizations but it’s just not there yet. For the time being, humans are still a very much needed part of Data Science.
Another common take on this myth is that AI will eventually take over the jobs of Data Scientists, which is also untrue for the same reasons mentioned above.
You have learned a tool and now you can be a Data Scientist.
Learning a tool is only a prerequisite for becoming a Data Scientist. Data Science is much more complicated than just knowing a language or tool to help you do the job. The assumption you are making is that being able to write code using existing libraries is enough to call yourself an expert in the field of Data Science. It’s simply not true, in this case.
Data Science requires many different skills, and programming is not the main component for becoming a Data Scientist. It’s much more advantageous to develop critical thinking skills and understanding how an algorithm works from scratch than it is to be able to write code. It’s no good if you know how to use a tool but cannot apply the knowledge of how to apply it properly or when to use it at all.
Not only that, but you also need good business acumen and great communication skills, because more often than not, you will have to report your findings to the stakeholders, who may not be so technological minded. So, understanding how to explain your results in simpler and a more intuitive way is essential.
Data Science is only about building models and nothing else.
If someone asked you about what a Data Scientist’s job is, this is the answer you would give and it’s a complete myth. “Data Science is not just about building any type of models, all day every day. It’s about data collection, data cleaning, analysis, visualization, and so much more” explains Thelma Wilber, from a tech blogger at Paper Fellows and State of writing.
And no, a data scientist’s job doesn’t include only making predictive models either. Things like anomaly detection where you figure out outliers in the data as well as market-basket analysis are other jobs that data scientists do that don’t rely on predictive model making.
Data-Science competition participation translates to projects in real life.
While data science competitions are great for building your data science skills, recruiters don’t tend to consider them as valuable experience. The reason for this is that competitions are not accurate representations of how you will work within an organization and its end-to-end pipelines. You need to have more real-world experience to land a data science project.
Data collection is a simple process; therefore, we don’t have to spend time on it.
The truth is data is being collected at an unprecedented pace but collecting and cleaning the data is not getting any easier. You must build a proper pipeline to collect the data, or your project is going nowhere fast.
You need to have a Ph.D. to become a Data Scientist
“If you are an Applied Data Scientist, who only works with existing algorithms, then you absolutely do not need a Ph.D.” says Rosa Horton, a tech writer at Boomessays and Essay roo. Most people who apply for this role already have the qualifications they need from their undergraduate degrees or work experience, making having a Ph.D. overkill.
These are the 6 biggest and most common myths in Data Science today.
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