Debunking Common Data Science Myths and Misconceptions
While the term “data science” was not popularized until 2008, this phrase can trace its roots back to the 1960s. The term and the field it refers to really caught fire in the 2010s.
During that era, organizations across many industries began to realize the value of using data science to process the massive amounts of information they were collecting.
The field of data science is off to a hot start in the 2020s as well. According to PennWest California, the data science field experienced a 39% “growth in employer demand” in 2020 compared to the year prior.
Additionally, data science professionals with a bachelor’s degree enjoy an earning potential nearly $9,000 higher than other 4-year degree recipients.
To say that the data science profession has exploded would be a gross understatement. However, like any new and rapidly evolving field, the data science industry is clouded in mystery, myths, and misconceptions.
Unfortunately, these myths can discourage would-be data scientists from exploring a career in the field. Additionally, the misconceptions can make organizations hesitant to invest in data science because they fear it would be a wasteful endeavor.
To clear things up, the Rolai team has debunked five of the most common data science myths:
Myth 1: All Data Science Professionals Are Math Addicts
Perhaps the most notable misconception about data science is that the field is reserved for individuals who eat, sleep, and breathe high-level mathematics. While data scientists do need an affinity for crunching numbers, they do not need to be doctoral-level math whizzes.
This myth has discouraged countless professionals from entering the data science profession due to fears that they would need to run the gamut of collegiate math classes. It also leads hiring teams astray when they create job descriptions for data science professionals or start screening prospective candidates.
Reality: Professionals from Non-Mathematics Backgrounds Can Excel
The truth of the matter is that many data scientists come from non-mathematics backgrounds. Admittedly, math plays a key role in data science. However, the high-level formulas are calculated using advanced computer software, not by math majors using scientific calculators.
Some of a data scientist’s biggest responsibilities involve interpreting and presenting data, not crunching numbers.
While data science professionals need to be comfortable navigating large data sets, the ability to interpret and relay insights is more important. That is why quite a few data scientists enter the field after obtaining managerial skills.
Myth 2: Only Top-Level Programmers Can Leverage Data Science
So data science professionals don’t need to be math masters, but what about all of those different coding languages? Surely data scientists must be fluent in at least a few languages like Python, R, or Java?
If you have bought into this myth, you are certainly not alone. Many people falsely believe that data scientists must have high-level coding and programming skills.
This myth, like many others, is rooted in information that was once factual. Early data analytics platforms required users to be fluent in coding languages and perform high-level programming.
Reality: Modern Data Science Solutions Require Little to No Coding
As with virtually all other things in the business world, the surge in demand for data science solutions fueled significant innovation. As more businesses bought into the field of data science, opportunistic software developers began crafting more user-friendly tools.
This development created a circular growth cycle that encouraged even more companies to adopt data science technologies, thereby further incentivizing developers to improve their software.
Modern data science tools are incredibly easy to use. Most of them offer low-code or no-code analytics, which means that data scientists only need minimal programming skills, if any.
By the end of the decade, data science solutions will become even more user-friendly. The data science platform market is projected to experience significant growth. According to Allied Market Research, the market had a value of $4.7 billion in 2020. By 2030, the value should skyrocket to approximately $79.7 billion.
Myth 3: Data Science Tools Are Complex and Mysterious
Myth 3 is closely related to myth 2. However, it was worth mentioning because some organizational leaders have concerns about the complexity of data science platforms, even if they know that the technology requires minimal coding.
This myth is dangerous because it can discourage businesses from fully tapping into the power of data science. It can also cause those interested in the profession to become hesitant to take the leap and sign up for courses or programs.
Reality: Top Data Science Tools Are Accessible and User-Friendly
Over the last few years, software developers that produce analytics solutions have made a conscious effort to make their products more user-friendly. Every aspect of the user experience on top platforms is designed to make life easy for data science professionals and the companies they serve.
Data analytics software is not the only thing that has received an overhaul. Training and educational materials have also been modernized to enhance learning and information retention. These solutions provide students with meaningful information in a digestible format.
Myth 4: Data Science is a Rigid Science
Since data science and analytics centers around structuring and reviewing statistical data, the profession can often be viewed as rigid or even boring. Nothing could be further from the truth. Data scientists do not spend their days endlessly running calculations but rather blend creativity with proven scientific processes.
Reality: Data Science Blends Scientific Principles and Creativity
Data scientists are tasked with tackling their organizations’ most pressing pain points. A data scientist may be asked to determine why an organization is experiencing a 25% churn rate or assess how current overhead expenses compare to costs from a year ago.
Data scientists are not forced to work in a rigid framework. Instead, they are given the latitude to think creatively and leverage big data to solve problems.
After data scientists have conducted analysis, they have to craft charts and graphs that make their findings easier to interpret. In short, data science can be an incredibly exciting and rewarding profession.
Myth 5: Sourcing Data Science Talent Is Nearly Impossible
Myth 5 is a particularly challenging one to debunk because it is, in part, factual. Whether your organization has just begun contemplating creating a data science program or is actively looking for talent, you know the candidate pool is scarce.
If you were wondering, no, it is not just your business struggling to source data science talent. There is a shortage of data scientists, and there has been for at least a few years.
So the fact component of this myth is that the data scientist shortage is very real. However, a reduced talent supply does not make it impossible to cultivate a successful data science program. You just need to think outside the box.
Reality: Nurturing In-House Talent Is Effective and Practical
If you cannot find talent in the candidate pool, why not nurture existing staff and equip them with the skills they need to contribute to your data science program?
Upskilling your staff will reduce attrition rates, boost employee morale, and help you fill critical vacancies. Sounds like a win for all involved, provided you can find well-designed, effective training resources.
Fortunately, Rolai can help with that last part. Our innovative platform is designed for businesses and educational institutions. It is loaded with expertly designed training materials, case studies, and practical information that is designed to maximize information retention. You and your staff may even be eligible to Get Started.
Contact Rolai to learn more about our program for businesses and educational institutions, or create an account to explore our courses.