Dispelling the Top 4 Myths About Data Science

Dispelling the Top Myths About Data Science

What are the most common myths about the field of data science? Perhaps the one that gets the most traction, unfortunately, states that the practice is an infographics-based discipline. Related tidbits of misinformation include equally off-kilter views and opinions. Here are the biggest offenders.

Analytics Produces Infographics

Graphics play a role, but the two are not the same. The challenge of data analysis and visualization is far more complex than constructing infographics images. The best way to acquire an understanding of the subject is to review a comprehensive guide on how to help your company succeed through the use of visual analytics. Currently enjoying a surge in popularity, this type of analytical tool is a core component of the process of data science. It refers to operating within a wholly visual interface with the goal of studying and exploring multiple sets of data. It employs colors, text, symbols, numbers, and other methods that can help people understand the essential message more clearly and without undue effort.

Scientists and Analysts Do the Same Things

One job is much broader than the other. In the real world of data science, there are three main categories of employment. They are engineering, analysis, and science. Engineers deal with the upkeep of the entire infrastructure. Usually, these experts have a strong background in database management tools and programming languages like Python. On the other hand, analysts use a varied set of tools to dig through the information in front of them. Their quest is to solve specific problems and answer questions that are relevant to the organization’s wellbeing. Finally, the scientists perform operations on the information, study it, and then deliver presentations in order to explain what they uncovered. They’re well-versed in subjects like machine learning, programming, math, and stats.

Only Huge Corporations Can Afford Data Science

Anyone can leverage the power of data science. Owners of startups and tiny businesses can do high-level data science work. The urban legend arises from a misconception about what the discipline actually consists of. There are no massive, complex computer systems involved, nor do the practitioners need special databases, tools, apps, or programs to get the job done. Instead, the core ingredients are things available to organizations of all sizes, like statistical information, big data, presentations, ordinary programs, free or low-cost apps, and intelligent employees, mangers, or owners. In the most basic form, all you need is a standard computer, a few apps, a handful of tools from open-source sites, and a person to do the work.

All Data is Useful

Most of it is dirty and needs to be cleaned up and organized. Most professionals in the field spend the majority of their workdays culling through dirty data, namely the stuff that’s duplicated, incomplete, spelled incorrectly, not accurate, or not related to the topic at hand. This time-consuming chore is necessary but not related to the more refined tasks like modeling or extracting information from sources. When information is dirty, the industry’s slang term, it needs to be organized and sanitized in one or more ways, and that’s after the miner spends precious hours collecting it in the first place.

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