![\"Debunking](\"https:\/\/ieeecs-media.computer.org\/wp-media\/2022\/07\/25213958\/Debunking-Data-Science-Myths.jpg\")
It\u2019s 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\u2019t necessarily mean that every data science \u201cfact\u201d we hear is actually truthful.<\/p>\n
In fact, some commonly known \u201cfacts\u201d are little more than hearsay, misinterpretations, or, at worst, outright myths.<\/p>\n
To counteract these kinds of data science myths, we\u2019ll debunk seven of the most common ones with real data science facts. We\u2019ll also show you why it\u2019s important to know the truth behind each myth we cover.<\/p>\n
With that out of the way, let\u2019s get started with our list of data science facts.<\/p>\n
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Want More Career-focused News? Subscribe to Build Your Career Newsletter Today!<\/strong><\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n There\u2019s plenty to be said for coding \u2013 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:<\/p>\n It\u2019s 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\u2019t mean that coding is the be-all and end-all of data science.<\/p>\n \u00a0<\/p>\n The end goal of being a data scientist is not to be fluent in as many coding languages as possible. In fact, if you\u2019re at the point where you\u2019re investing all your time in mastering all the coding languages you have time to learn about, you\u2019re probably not investing your time optimally.<\/p>\n Instead, you\u2019ve got to remember that coding is a means to an end for data scientists.<\/p>\n \u00a0<\/p>\n In a competitive world like that of data science, most people will want to find ways to stand out from the crowd \u2013 but if they do this by focusing on learning more languages, they\u2019re missing chances to work on becoming better data scientists.<\/p>\n That\u2019s not to say that programming languages don\u2019t matter. They absolutely do. It\u2019s just important to remember that there\u2019s much more to being a skilled, well-rounded data scientist than having the most impressive repertoire of languages.<\/p>\n \u00a0<\/p>\n This myth is especially common among people who aren\u2019t 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.<\/p>\n \u00a0<\/p>\n In actuality, they\u2019re 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:<\/p>\n Data scientists are the ones who interpret and analyze data. They take the information that exists about something specific \u2013 a project, an app, or a company\u2019s goal, for example \u2013 and then analyze it to generate actionable insights.<\/p>\n That\u2019s not the same thing at all as developing apps, code, or even ideas.<\/p>\n \u00a0<\/p>\n This myth (and the truth behind it) is, like the first one on this list, all about where people should direct their focus.<\/p>\n It\u2019s development teams that should be able to answer questions like \u201cWhat is MapReduce?\u201d (and \u201cHow do you use it?\u201d). Data scientists, on the other hand, need to know how they can leverage tools (like MapReduce) to derive actionable insights from a data set.<\/p>\n In short, this myth matters because once you know the truth behind it, you\u2019ll have a keener understanding of the roles and responsibilities associated with data science.<\/p>\n \u00a0<\/p>\n While the term \u201cdata science\u201d is immediately appealing to many who know how much a skilled data scientist can do for them, that knowledge is unfortunately not universal.<\/p>\n In other words, plenty of people might think there\u2019s only so much room for data scientists in today\u2019s 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 \u201cjust hype.\u201d<\/p>\n This couldn\u2019t be further from the truth, and here\u2019s why.<\/p>\n \u00a0<\/p>\n There\u2019s both a demand and a need for data scientists \u2013 and both are only on the rise:<\/p>\n 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\u2019s doubly true when companies keep developing new, more complex tools for the purpose of improved data science.<\/p>\n Far from being \u201cjust hype,\u201d data science is actually a vital subject that can drastically change a company\u2019s approach to meeting its goals. Data scientists know how to turn a nebulous goal like \u201cI want to increase the number of sales we make each year\u201d into a set of data-based fact-backed insights that create the foundations of a solid strategy.<\/p>\n \u00a0<\/p>\n If people are under the impression that there\u2019s no demand for data scientists, the world will have fewer data scientists. It\u2019s as simple as that. After all, no young high school graduate wants to specialize in a field that won\u2019t hire them, just as no seasoned professional wants to swap over to a new field with no prospects.<\/p>\n That\u2019s why it\u2019s important to know that there\u2019s a major need for data scientists, and that data science is only becoming more relevant as time goes on.<\/p>\n \u00a0<\/p>\n 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\u2019s 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\u2019s responsibility.<\/p>\n \u00a0<\/p>\n If we\u2019re being fully, completely honest, it\u2019s 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 \u2013 there are workarounds.<\/p>\n The fact of the matter is that an excellent data scientist knows better.<\/p>\n When you get your data from open-source platforms or other places where you didn\u2019t have a hand in generating or collecting it at all, you can\u2019t vouch for it. It\u2019s impossible to guarantee that open-source data is accurate, let alone bias-free and objective. And if you\u2019re not sure about the quality of the data you\u2019re using, it\u2019s impossible to be fully confident in the insights you get from it.<\/p>\n \u00a0<\/p>\n 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.<\/p>\n If you\u2019re a little lost on how to handle things like data warehouses, a good place to start is with this Apache Hive Introduction by Databricks.<\/p>\n \u00a0<\/p>\n 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.<\/p>\n However, while some might be tempted to say that\u2019s where data scientists\u2019 work concerning predictions also ends, they\u2019d be mistaken.<\/p>\n \u00a0<\/p>\n Predictions aren\u2019t 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?<\/p>\n Well, if your data scientists don\u2019t know how the predictive models work, it might take a while before anyone starts to notice the predictions are wrong at all. Then there\u2019s the problem of actually fixing things, which isn\u2019t possible unless the scientists know how they work in the first place.<\/p>\n \u00a0<\/p>\n First, it\u2019s 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.<\/p>\n Based on this, the importance of looking beyond accuracy in data starts to become clearer.<\/p>\n Even the top technology predictions aren\u2019t made in isolation. Someone has to code, test, and operate the model regularly. More often than not, that \u201csomeone\u201d takes the form of an entire team, and that team is bound to have biases and preferences (both conscious and unconscious ones).<\/p>\n That\u2019s why data scientists need to know where their predictions are coming from, what\u2019s influencing that data, where accuracy and precision intersect, and so on. Getting good results is, in other words, only half of the work.<\/p>\n \u00a0<\/p>\n 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?<\/p>\n It might be tempting to say \u201cYes,\u201d but this misses the whole truth, which we\u2019ll get into now.<\/p>\n \u00a0<\/p>\n Any data science project actually starts with business needs. The data pertaining to those needs follows after, in all instances.<\/p>\n For example, let\u2019s say you\u2019re looking to improve a piece of software\u2019s app store rating. You wouldn\u2019t actually begin that sort of project by gathering information on the actual ratings they\u2019re getting and what\u2019s behind them. Instead, you\u2019d consider the goal, which is getting higher ratings, and then plot how to use data to achieve that goal.<\/p>\n Only once the plan is in place does the data actually come in.<\/p>\n \u00a0<\/p>\n It\u2019s vital to go into any project knowing what you\u2019re 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.<\/p>\n \u00a0<\/p>\n Since the term \u201cdata science\u201d directly suggests working with data, it\u2019s 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.<\/p>\n However, it\u2019s still a myth, and for good reason.<\/p>\n \u00a0<\/p>\n Just about every company currently operating generates data in some capacity. For example, if you\u2019re using contact center software, you\u2019ll necessarily be generating data on your customers. Alongside that, your agents\u2019 performance can be measured through the data they generate, creating data that describes your company\u2019s reputation and customer satisfaction and so on.<\/p>\n Simply put, everyone can generate enough data to merit employing a data scientist.<\/p>\n \u00a0<\/p>\n If you\u2019re unsure whether you can make the most of a data scientist, you\u2019re unlikely to invest in one. After all, why take a risk if you\u2019re unsure whether it would pay off?<\/p>\n The thing about data science is that it can always be made to pay off, so long as you\u2019re using it correctly. That\u2019s why it\u2019s 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\u2019re not a group that works with big data regularly.<\/p>\n \u00a0<\/p>\n It\u2019s easy to take what you\u2019ve 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.<\/p>\n The best way to avoid falling prey to myths is to fact-check at every opportunity.<\/p>\n If you\u2019re not sure whether something is true, make sure to double-check. If you\u2019re reasonably sure, there\u2019s no harm in confirming that you\u2019re right. And even when you\u2019re completely certain, it\u2019s always good to be ready to be proven wrong.<\/p>\n After all, it\u2019s always better to learn from myths than to believe them, and we hope this list of data science facts has helped with that.<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n<\/p><\/div>\n
\nMyth: the most important thing for data scientists is coding<\/h2>\n
\n![\"Chart](\"https:\/\/ieeecs-media.computer.org\/wp-media\/2022\/07\/25214634\/Data-Science-Facts-1-300x257.png\")
The truth<\/h3>\n
Why it matters<\/h3>\n
Myth: data scientists and developers are more or less the same<\/h2>\n
\nThe truth<\/h3>\n
![\"Chart](\"https:\/\/ieeecs-media.computer.org\/wp-media\/2022\/07\/25214744\/Data-Science-Facts-2-300x266.png\")
Why it matters<\/h3>\n
Myth: there\u2019s a limited demand for data scientists<\/h2>\n
\nThe truth<\/h3>\n
![\"Char](\"https:\/\/ieeecs-media.computer.org\/wp-media\/2022\/07\/25214834\/Data-Science-Facts-3-300x178.png\")
Why it matters<\/h3>\n
Myth: data scientists don\u2019t need to know how to gather their own data<\/h2>\n
\nThe truth<\/h3>\n
Why it matters<\/h3>\n
Myth: as long as predictions are accurate, it doesn\u2019t matter how they\u2019re generated<\/h2>\n
\nThe truth<\/h3>\n
Why it matters<\/h3>\n
![\"Grid](\"https:\/\/ieeecs-media.computer.org\/wp-media\/2022\/07\/25214957\/Data-Science-Facts-5-300x150.png\")
Myth: data science projects start with data<\/h2>\n
\nThe truth<\/h3>\n
Why it Matters<\/h3>\n
Myth: data science is only useful to companies that work with big data<\/h2>\n
\n![\"Graph](\"https:\/\/ieeecs-media.computer.org\/wp-media\/2022\/07\/25215053\/Data-Science-Facts-6-300x223.png\")
The truth<\/h3>\n
Why it matters<\/h3>\n
Fact and fiction: key takeaways<\/h2>\n
\nAbout the Writer<\/h2>\n
\n![\"Pohan](\"https:\/\/ieeecs-media.computer.org\/wp-media\/2022\/06\/22000948\/pohan-lin-headshot-150x150.jpg\")
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.<\/p>\n