Common Data Science Myths You Should Unlearn

Career Trends: March 26, 2023
Curated by the Knowledge Team of ICS Career GPS

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• Excerpts are taken from an article published on makeuseof.com

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Despite the recent buzz around data science, people still shy away from this field. For many techies, data science is complex, unclear, and involves too many unknowns compared to other tech careers. Meanwhile, the few who venture into the field constantly hear several discouraging data science myths and notions.

However, did you know that most of these tales are general misconceptions? It isn’t the easiest path in tech, but data science isn’t as terrifying as people tend to assume. So in this article, we’ll debunk 8 of the most popular data science myths.

Myth #1: Data Science is for Math Geniuses Only

• Although data science does have its mathematical elements, no rule says you must be a guru in math.
• Besides the standard statistics and probability, this field comprises numerous other, not strictly mathematical aspects.
• You won’t need to relearn abstract theories and formulas in great depth in areas involving math. Nonetheless, this doesn’t completely rule out the need for mathematics in data science.
• Like most analytical career paths, data science requires basic knowledge of certain areas of math. These areas include statistics (as mentioned above), algebra, and calculus.
• Thus, while mathematics isn’t the main emphasis of data science, you may want to reconsider this career path if you’d rather avoid numbers altogether.

Myth #2: AI Will Reduce Demand for Data Science

• AI may reduce demand for some fundamental jobs, but it still requires data scientists’ decision-making and critical thinking skills.
• Rather than replace data science, AI is significantly helpful, enabling them to generate information, collect, and handle much larger data.
• Moreover, most AI and machine learning algorithms depend on data, creating the need for data scientists.

Myth #3: Data Science Encompasses Predictive Modeling Alone

• Training data for predictive purposes looks like the fancy, fun part of data science. Even so, behind-the-scenes chores like cleaning and data transformation are equally, if not more important.
• After collecting large data sets, the data scientist must filter necessary data from the collection to retain data quality.
• There is no predictive modelling, but it is a tasking, non-negotiable part of this field.

Myth #4: Every Data Scientist is a Computer Science Graduate

• Here’s one of the most popular data science myths. Thankfully, the beauty of the tech industry is the seamlessness when switching to a career in tech.
• Hence, no matter your college major, you can become an excellent data scientist given the right arsenal, courses, and mentors.
• Whether you’re a computer science or philosophy graduate, data science is within your grasp.
• Although this career path is open to anyone with interest and drive, your course of study will determine the ease and speed of your learning.
• For example, a computer science or mathematics graduate is more likely to grasp data science concepts faster than someone from an unrelated field.

Myth #5: Data Scientists Only Write Code

• Although most data scientists write some code along the way, depending on the nature of the job, coding is only the tip of the iceberg in data science.
• Writing code only gets part of the job done.
• But, code is used to build the programs, and algorithms data scientists use in prediction modelling, analysis, or prototypes.
• Coding only facilitates the work process, so calling it the main job is a misleading data science myth.

Myth #6: Data Science is Necessary for Big Companies Only

• When studying data science, the general impression is that you can only get employment from major firms in any industry.
• However, qualified data scientists have many job opportunities, especially today.
• Any business that works directly with consumer data, whether a startup or a multi-million dollar company, requires a data scientist for maximum performance.
• Look at what your data science skills can achieve for companies around you.

Myth #7: Larger Data Equates More Accurate Results and Predictions

• Large data sets reduce your error margins compared to smaller ones, but accuracy doesn’t depend on data size alone.
• First, the quality of your data matters. Large data sets only help if the data collected is suited to solve the problem.
• Additionally, with AI tools, higher quantities are beneficial up until a certain level.

Myth #8: It is Impossible to Self-Learn Data Science

• Similar to other tech paths, self-learning data science is very much possible, especially with the wealth of resources available to us presently.
• Of course, it doesn’t matter what level you’re currently at, novice, intermediate, or pro; there’s a course or certification for you.
• So while data science might be a little complex, this doesn’t make self-learning data science far-fetched or impossible.

Despite the interest in this field, the data science myths above and more make several tech enthusiasts avoid the role. Now, you have the correct information, so what are you waiting for? Explore the numerous detailed courses on e-learning platforms and begin your data science journey today.

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(Disclaimer: The opinions expressed in the article mentioned above are those of the author(s). They do not purport to reflect the opinions or views of ICS Career GPS or its staff.)

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