Data Science Myths – 10 Common Data Science Myths You Should Unlearn Now
Do you believe these common data science myths? It’s time to unlearn them and better understand this field.
Despite the recent buzz surrounding data science, few people are interested in it. In comparison to other tech careers, data science is perceived by many techies as being complicated, unclear, and involving too many unknowns. However, the few people who enter the field are constantly exposed to demoralizing data science myths and ideas.
Data Science Myths
But did you know that the majority of these stories are simply untruths? Data science isn’t the simplest career path in technology, but it’s also not as terrifying as most people think. Therefore, this article will dispel 10 of the most widespread data science myths.
1. Data Science Is For Math Geniuses Only
Although data science involves some mathematics, you are not required to be an expert in the subject. This field has many other, less strictly mathematical aspects besides the usual statistics and probability.
Math-related subjects won’t require you to re-learn abstract theories and formulas in great detail. This does not, however, completely disprove the necessity of mathematics in data science.
Data science demands a foundational understanding of some math areas, like most analytical career paths. These include algebra, calculus, and statistics (as previously mentioned).
Therefore, even though data science doesn’t heavily emphasise mathematics, you might want to give it another thought if you’d rather stay away from numbers altogether.
2. Nobody Needs Data Scientists
Unlike more well-known tech careers like software development and UI/UX design, data science is still growing in popularity. However, the demand for data scientists is still growing steadily.
For instance, between 2021 and 2031, the US Bureau of Labor Statistics predicts a 36% increase in demand for data scientists. This estimate is not shocking given that many sectors, including the public sector, finance, and healthcare, have started to recognize the value of data scientists as a result of the growing amount of data.
Many businesses and organizations without data scientists struggle to release accurate information when dealing with large amounts of data. Therefore, even though your skill set might not be as in demand as in other tech fields, it is still important.
3. AI Will Reduce Demand For Data Science
Nowadays, AI seems to have an answer for every problem. AI is reportedly used in self-driving cars, programming, essay writing, the military, and even in schoolwork. Nowadays, every professional is concerned about a robot taking its job in the future.
Does this worry apply to data science, though? No, it’s one of the many myths about data science. Although AI may decrease demand for some fundamental positions, data scientists’ critical and judgmental thinking abilities are still needed.
AI significantly aids data scientists, allowing them to produce knowledge, gather data, and manage much larger amounts rather than replace them. Additionally, most AI and machine learning algorithms rely on data, necessitating the employment of data scientists.
4. Data Science Encompasses Predictive Modeling Alone
Data science could involve building models that predict the future based on past occurrences, but does it revolve around predictive modelling alone? Certainly not!
Predictive modelling appears to be the fanciest and most enjoyable aspect of data science. However, background tasks like cleaning and data transformation are just as important, if not more so.
The data scientist must remove the necessary data from the collection after gathering large amounts of data to maintain the quality of the data. Predictive modelling is not present but is an essential, non-negotiable component of this field.
5. Every Data Scientist Is A Computer Science Graduate
Here is a common misconception about data science. Fortunately, the ease of transitioning to a career in tech is one of its beauties. Therefore, you can succeed as a data scientist regardless of your college major if you have the right tools, training, and mentors. Whether you have a philosophy or computer science degree, you can learn data science.
You should be aware of one thing, though. The ease and speed of your learning will depend on your course of study, even though this career path is open to anyone interested and motivated. For instance, computer science or mathematics graduates are more likely than those from unrelated disciplines to pick up on data science concepts quickly.
6. Data Scientists Only Write Code
Any skilled data scientist would correct you if you held this belief. Coding is only the tip of the iceberg in data science, even though most data scientists write some code along the way, depending on the nature of the job.
Writing code only completes a portion of the task. However, the programs and algorithms that data scientists use for prediction modelling, analysis, or prototypes are built using code. Coding is a misleading data science myth because it only facilitates the work process.
7. Power BI Is The Only Tool Required For Data Science
With its robust features and analytical capabilities, Microsoft’s Power BI is a leading data science and analytics tool. Contrary to popular belief, however, becoming proficient with Power BI is just a portion of what you need to succeed in data science; it entails much more than this particular tool.
For instance, even though data science doesn’t focus primarily on writing code, you still need to learn a few programming languages, typically Python and R. You’ll also need to work closely with databases, extracting and compiling data from them, and have a working knowledge of programs like Excel. Consider taking courses to assist you in mastering Power BI, but remember that this is not the end of the road.
8. Data Science Is Necessary For Big Companies Only
The next statement is also harmful and untrue, but sadly, most people believe it. The perception among data science students is that only major corporations in any industry will hire them. In other words, any data scientist not hired by organizations like Amazon or Meta will be out of a job.
But today, there are many job opportunities for qualified data scientists. A data scientist is necessary for the best performance in any company dealing directly with consumer data, whether a startup or a multimillion-dollar business.
That said, dust up your resume and look at what your data science skills can achieve for companies around you.
9. Larger Data Equates To More Accurate Results And Predictions
Even though this assertion is typically true, it is only partially accurate. Large data sets have lower error margins than smaller ones, but accuracy isn’t solely a function of data size.
First and foremost, the calibre of your data is important. Large data sets will be useful only when the data collected is appropriate to address the issue. Additionally, higher quantities are advantageous with AI tools up to a certain point. More information after that is harmful.
10. It Is Impossible To Self-Learn Data Science
One of the most prevalent myths about data science is this one. Self-learning data science is very much feasible, just like other tech career paths, especially with the abundance of resources currently at our disposal. You can accelerate your development in data science by taking courses on platforms like Coursera, Udemy, LinkedIn Learning, and other nifty tutorial websites. These courses are both free and paid.
Of course, it doesn’t matter what level you’re currently at, novice, intermediate, or pro; you have a course or certification. So while data science might be a little complex, this doesn’t make self-learning data science far-fetched or impossible.
There’s More To Data Science Than Meets The Eye
Despite the interest in this field, the aforementioned data science myths and others cause some tech enthusiasts to steer clear of the position. Now that you know the truth, what are you waiting for? Start your data science journey immediately by looking through the many in-depth courses available on e-learning platforms.
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