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7 Top Common Fallacies Of Data Science – The Ultimate Truth Revealed

7-Top-Common-Fallacies-Of-Data-Science---The-Ultimate-Truth-Revealed
Data science is a booming industry, but it’s also relatively new. As such, many people are still learning the ropes and figuring out what works and what doesn’t. One thing to be aware of is some common pitfalls that can doom your data science project before it even begins. On the other hand, when confronted with real-world data science problems, new hires and aspiring data scientists often struggle to cope with their idealistic depiction of the real world in the workplace.

Several individuals often misunderstand data science. Many people think that it’s just a bunch of complicated math to solve business and marketing problems with no connection to real-world applications. But data science is so much more than that — it requires creativity and critical thinking just as it does statistics and machine learning algorithms. This is where taking a data science certification training comes in handy.

What Does Data Science Entail?

Data scientists are data-driven, but they are not necessarily scientists; they are driven by their intuition and creativity, which may or may not be rooted in science.

As a data scientist, one must work with individuals from many career backgrounds to complete real-world data science tasks. One person may not have all the abilities required to complete a data science project properly. A core responsibility of a data scientist is to analyse a large amount of data from various sources and interpret it for their respective employers. Experts in this field understand how to devise new data-analytics methods and exploit the existing ones to their benefit. They employ their broad research skills to extract meaningful insights from specific datasets.

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As you can see, even though data scientists do work with data, they don’t just draw conclusions and rush to publish catchy headlines. Instead, they explore their data sets, find meaningful results, and write reports that are often meant to serve as a basis for decisions by other people—in other words, they translate and explain their results in a language that everyone can understand.

So What Are The Major Fallacies Faced By These Data Scientists?

Fallacy 1 # Datasets Are Accessible And Relevant:

As a budding data scientist, you shouldn’t expect easy access to relevant datasets. This process is time-consuming and requires lots of effort and patience. It is recommended to go through all the information available to make sure it makes sense and is up-to-date to be on the safe side. Moreover, it is nearly impossible to derive any useful conclusions from a dataset that you haven’t spent time analysing.

Fallacy 2 # Datasets Are Consistent:

It is very important to find a consistent dataset that is well-structured, self-consistent, and well-defined. However, the reality is that those datasets can only be found if they were created by a data engineer or data scientist, which is not always the case.

The basic rule to keep in mind, especially for data pipelines that rely on old systems, is that if a data scientist didn’t design the data feed, it’s likely to return all kinds of useless information in response to various operating conditions. As a result, not all datasets are consistent.

Fallacy 3 # Data Is Intuitively Understandable:

An unnamed or missing header field, truncated text fields, or missing lookup tables make a dataset difficult to understand. To avoid this hurdle and better understand the data, the data needs to have a well-documented description. You can’t tell if you’re measuring apples or oranges until you have a well-documented explanation of the data you’re using. Right?

Top-Common-Fallacies-Of-Data-Science

Fallacy 4 # Analyses Can Be Easily Re-Executed:

While assigning tasks to data scientists, clients should keep in mind that it requires a lot of time to complete the analysis successfully. Furthermore, it takes time to re-run an analysis project after it has been completed. These people need to stop pressuring data scientists by setting unrealistic deadlines for their work.

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Often, Data analysis begins with the formulation of research questions followed by selecting the most important variables to assess. Data gathering, data analysis, and the interpretation of outcomes are all part of the time-consuming process.

Fallacy 5 # Analytics Outputs Are Easily Shared And Understood:

The majority of the audience will have not a single idea about how to read any basic yet detailed analysis. To cover their confusion, they will ask you to add more features and claim that analysis must be mathematically proven before it can be used. Some people will also rely on their “gut instinct,” but your extensive analysis will be doubted, questioned, and ignored. Hence, it is your primary responsibility to translate the results into a language that can be easily understood regardless of whether or not you have answered the question that has been presented. As a result, one needs to have data analytics skills to analyse the analytics output.

Fallacy 6 # Encryption Isn’t Important In Data Science:

Now that you’ve finished the analysis and put together a nice report and a few presentations on the subject, it’s time to send the data to someone else for evaluation or review. But the problems arise when a client requests plain-text analysis to a specific email address. In this case, the client fails to realize that the analysis should contain encryption to secure it from unauthorized access. People should be aware that technical jobs that include data analysis are prone to various security threats. Data scientists must encrypt their work in order to protect it from security risks like information leakage.

Fallacy 7 # The Goals Of Data Science Are Always Achievable:

A successful data science project may be accomplished with the right strategy and tools. However, we should stop pretending that all data science projects are achievable.

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Due to large datasets, some projects take time, and it is totally okay if it doesn’t work out.

When it comes to a project’s success or failure, the level of experience of the data scientist also plays a role. In addition, factors such as lack of financial resources also make it difficult to get adequate processing tools to handle specific data science projects.

Wrapping Up:

As you’ve seen, Data science is a fast-growing field that’s revolutionizing the way we look at the world around us. Unfortunately, it’s also a field that has its share of common mistakes and pitfalls—and it can be difficult to avoid them when you’re just starting out.

The idea of data science is a growing trend, and there are still many who do not understand the full extent of its power. The implications of valuable information in large and diverse datasets are enormous, and industries that do not yet understand an all-encompassing approach to data are bound to be left behind. On the other hand, the data world is experiencing substantial growth in terms of job opportunities. Thus, it is high time to brush up on your data science knowledge and skyrocket your career in this field. If you’re interested in developing your data science skills, a data science course in Hyderabad can be the ideal way to start your data science journey and stay ahead of the competition.

Phurba Doma SherpaAbout the Author:

I’m Phurba Doma Sherpa, a passionate blogger who loves writing about the latest technologies. I also write educational and technical content regarding data science courses, Artificial Intelligence (AI) and ML.  I’ve always believed in smart learning processes that help readers to understand concepts, and writing are one of the ways.  I always prefer articles that will encourage tech enthusiasts in growing their careers.

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