Data Science Facts: Debunking These 7 Data Science Myths & Why You Need to Know Them
It’s easier now than ever before for ideas to spread around the world, including through fact-based communities like the one inhabited by data scientists. However, that doesn’t necessarily mean that every data science “fact” we hear is actually truthful.
In fact, some commonly known “facts” are little more than hearsay, misinterpretations, or, at worst, outright myths.
To counteract these kinds of data science myths, we’ll debunk seven of the most common ones with real data science facts. We’ll also show you why it’s important to know the truth behind each myth we cover.
With that out of the way, let’s get started with our list of data science facts.
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Myth: the most important thing for data scientists is coding
There’s plenty to be said for coding – languages like Python, R, and C++ are well-known and popular for a good reason. Plenty of people who know at least one language will go on to learn more, with some being far more popular than others:
It’s also true that data scientists work with coding languages in their daily workflows, making it useful to be skilled at things like secure software coding. But that doesn’t mean that coding is the be-all and end-all of data science.
The end goal of being a data scientist is not to be fluent in as many coding languages as possible. In fact, if you’re at the point where you’re investing all your time in mastering all the coding languages you have time to learn about, you’re probably not investing your time optimally.
Instead, you’ve got to remember that coding is a means to an end for data scientists.
Why it matters
In a competitive world like that of data science, most people will want to find ways to stand out from the crowd – but if they do this by focusing on learning more languages, they’re missing chances to work on becoming better data scientists.
That’s not to say that programming languages don’t matter. They absolutely do. It’s just important to remember that there’s much more to being a skilled, well-rounded data scientist than having the most impressive repertoire of languages.
Myth: data scientists and developers are more or less the same
This myth is especially common among people who aren’t too familiar with, or knowledgeable about, the world of coding, programming, and data. They might assume that the same people who create programs and develop apps are those who then go on to analyze those same things.
In actuality, they’re two very different professions and separate specializations. This goes a long way to explain why developers of various kinds are kept completely separate from data scientists in terms of roles companies look to fill:
Data scientists are the ones who interpret and analyze data. They take the information that exists about something specific – a project, an app, or a company’s goal, for example – and then analyze it to generate actionable insights.
That’s not the same thing at all as developing apps, code, or even ideas.
Why it matters
This myth (and the truth behind it) is, like the first one on this list, all about where people should direct their focus.
It’s development teams that should be able to answer questions like “What is MapReduce?” (and “How do you use it?”). Data scientists, on the other hand, need to know how they can leverage tools (like MapReduce) to derive actionable insights from a data set.
In short, this myth matters because once you know the truth behind it, you’ll have a keener understanding of the roles and responsibilities associated with data science.
Myth: there’s a limited demand for data scientists
While the term “data science” is immediately appealing to many who know how much a skilled data scientist can do for them, that knowledge is unfortunately not universal.
In other words, plenty of people might think there’s only so much room for data scientists in today’s job market. They may be tempted to think of data science as a field that can be useful rather than as an essential cornerstone of the way their business runs. In fact, some people even think of data science as being “just hype.”
This couldn’t be further from the truth, and here’s why.
There’s both a demand and a need for data scientists – and both are only on the rise:
As the tools available for data science continues to grow and develop, the need for skilled scientists who know how to handle them will only increase. That’s doubly true when companies keep developing new, more complex tools for the purpose of improved data science.
Far from being “just hype,” data science is actually a vital subject that can drastically change a company’s approach to meeting its goals. Data scientists know how to turn a nebulous goal like “I want to increase the number of sales we make each year” into a set of data-based fact-backed insights that create the foundations of a solid strategy.
Why it matters
If people are under the impression that there’s no demand for data scientists, the world will have fewer data scientists. It’s as simple as that. After all, no young high school graduate wants to specialize in a field that won’t hire them, just as no seasoned professional wants to swap over to a new field with no prospects.
That’s why it’s important to know that there’s a major need for data scientists, and that data science is only becoming more relevant as time goes on.
Myth: data scientists don’t need to know how to gather their own data
When your focus is on analytics, insights, and information processing, the task of actually collecting and storing data might not be the first thing you think of doing. That’s where this myth comes from; plenty of people would imagine that data scientists are limited to synthesizing information and that gathering it is someone else’s responsibility.
If we’re being fully, completely honest, it’s possible to be a data scientist and not be able to gather your own data. You can use data supplied to you, or you can outsource that sort of thing – there are workarounds.
The fact of the matter is that an excellent data scientist knows better.
When you get your data from open-source platforms or other places where you didn’t have a hand in generating or collecting it at all, you can’t vouch for it. It’s impossible to guarantee that open-source data is accurate, let alone bias-free and objective. And if you’re not sure about the quality of the data you’re using, it’s impossible to be fully confident in the insights you get from it.
Why it matters
Collecting and cleaning data is something any data scientist worth their salt should know how to do. The sooner you can dispel the idea from your mind of data scientists not needing to gather their own data, the sooner you can take your data science skills to new heights.
If you’re a little lost on how to handle things like data warehouses, a good place to start is with this Apache Hive Introduction by Databricks.
Myth: as long as predictions are accurate, it doesn’t matter how they’re generated
A major part of the daily work of data scientists involves creating predictive models, as well as making sure that the predictions those models generate are accurate.
However, while some might be tempted to say that’s where data scientists’ work concerning predictions also ends, they’d be mistaken.
Predictions aren’t generated in a vacuum and are rarely generated on a one-off basis. Usually, the models used to form predictions will continue to be used when they work well. But what happens when those models go wrong?
Well, if your data scientists don’t know how the predictive models work, it might take a while before anyone starts to notice the predictions are wrong at all. Then there’s the problem of actually fixing things, which isn’t possible unless the scientists know how they work in the first place.
Why it matters
First, it’s important to establish something; accuracy and precision are two different things. Accuracy involves hitting specific targets, while precision involves hitting the same area consistently. The graphic below helps to visualize that difference.
Based on this, the importance of looking beyond accuracy in data starts to become clearer.
Even the top technology predictions aren’t made in isolation. Someone has to code, test, and operate the model regularly. More often than not, that “someone” takes the form of an entire team, and that team is bound to have biases and preferences (both conscious and unconscious ones).
That’s why data scientists need to know where their predictions are coming from, what’s influencing that data, where accuracy and precision intersect, and so on. Getting good results is, in other words, only half of the work.
Myth: data science projects start with data
The daily workflows of data scientists revolve around data, that is for sure. Your average data scientist handles a lot of information, both directly and indirectly. But does that mean that data is the true starting point for their projects?
It might be tempting to say “Yes,” but this misses the whole truth, which we’ll get into now.
Any data science project actually starts with business needs. The data pertaining to those needs follows after, in all instances.
For example, let’s say you’re looking to improve a piece of software’s app store rating. You wouldn’t actually begin that sort of project by gathering information on the actual ratings they’re getting and what’s behind them. Instead, you’d consider the goal, which is getting higher ratings, and then plot how to use data to achieve that goal.
Only once the plan is in place does the data actually come in.
Why it Matters
It’s vital to go into any project knowing what you’re doing, especially for data scientists and those working with them. When hiring a data scientist, you need to know what to expect and how to set them up to succeed.
Myth: data science is only useful to companies that work with big data
Since the term “data science” directly suggests working with data, it’s easy to make the (incorrect) assumption that data scientists exclusively work with companies based on data. The rapidly growing market for big data only makes it even easier to jump to this conclusion.
However, it’s still a myth, and for good reason.
Just about every company currently operating generates data in some capacity. For example, if you’re using contact center software, you’ll necessarily be generating data on your customers. Alongside that, your agents’ performance can be measured through the data they generate, creating data that describes your company’s reputation and customer satisfaction and so on.
Simply put, everyone can generate enough data to merit employing a data scientist.
Why it matters
If you’re unsure whether you can make the most of a data scientist, you’re unlikely to invest in one. After all, why take a risk if you’re unsure whether it would pay off?
The thing about data science is that it can always be made to pay off, so long as you’re using it correctly. That’s why it’s important not to arbitrarily exclude yourself and your company from the benefits that come with having a data science team on board, even if you’re not a group that works with big data regularly.
Fact and fiction: key takeaways
It’s easy to take what you’ve heard to be true and run with that version of things. However, this mentality leads to myths becoming ever more popular, leading to incorrect assumptions at best and serious consequences for data scientists and the companies that employ them at worst.
The best way to avoid falling prey to myths is to fact-check at every opportunity.
If you’re not sure whether something is true, make sure to double-check. If you’re reasonably sure, there’s no harm in confirming that you’re right. And even when you’re completely certain, it’s always good to be ready to be proven wrong.
After all, it’s always better to learn from myths than to believe them, and we hope this list of data science facts has helped with that.
About the Writer
Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, databricks orchestration, online SaaS business, and e-commerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing.