After combing through the data, plenty of data science myths and misconceptions appear to be swirling around the Internet. While some misconceptions are understandable, others are completely baseless and downright ridiculous. Here are seven of the most ridiculous data science myths and misconceptions to ignore completely.
Data Science Requires An Innate Ability
One of the most limiting beliefs about data science is that it is an innate ability rather than something that can be learned. This myth presupposes that successful data scientists are born rather than trained. This is simply not true.
To believe that data science is a natural inclination is an insult to those who persevered to become a successful data scientists. It discounts all of their efforts and hard work. In truth, the field of data science requires some intuition for data and critical thinking, but these are both skills that can be developed.
A Ph.D. Is Required To Be A Data Scientist
Many falsely assume that a Ph.D. is a requirement to enter the field of data science. While some job postings require a MSc. Or Ph.D. degree, a graduate degree is not necessary to be get a job in data science. It may help you stand out as a candidate, but so will actual work experience. If you do not hold a graduate degree, focus on your experiences in similar roles to demonstrate your ability as a data scientist. Similarly, your portfolio of projects should be enough to convince employers that you are a capable data scientist. You can get Data Science Course online.
A Background In Computer Science, Math, Statistics, or Programming Is Essential
Although most data scientists have a background in engineering, computer science, statistics, or math, it is not necessary to become a data scientist. “While it may be advantageous to possess some knowledge in these fields,” advises Wade Hughes, a project manager at Academized. “It is possible to succeed as a data scientist without a technical background.” Many people transition from a non-technical background into data science and excel.
Data Science Is All About Tools
Data scientists employ various specialized tools and programs to collect, analyze, and model a solution. According to a survey conducted by BrainStation, tools such as Python, SQL, and Tableau are the most common tools used in data science. However, there is so much more to data science than just tools. At its core, data science requires problem-solving skills and communication skills. Mastering tools is not enough to become a data scientist.
Data Science Is All About Models
In the same way that data science is not just about tools, it’s also not just about models. Many wrongly assume that data science is limited to building predictive models. Although models are a part of the data science process and can be especially useful in extracting insights, it is one of several steps in the process.
Data Science Is Exclusive To Large Businesses
A particularly misleading myth about data science is that only large businesses with greater resources can benefit from it. In reality, small and medium businesses can greatly benefit from data science. It can equip smaller-scale businesses with the knowledge to create an effective strategy for growth and success. “Data science is a valuable tool regardless of the size of the organization,” according to Tommy Shirley, a business writer at UK Writings. “If an organization is not large enough to hire a data scientist, they should at least have someone who analyzes data regularly.”
Humans Will Eventually Be Replaced By Artificial Intelligence In Data Science
One of the more ridiculous myths is that human data scientists will eventually be replaced by artificial intelligence. While machines excel at finding patterns, human intervention is still required to consider ethics in data science and instruct machines to adapt to data changes. The truth is that human data scientists play an invaluable role in data science and are here to stay in the foreseeable future.
Data science can be overwhelming at times, and the myths listed above only make it worse. As a data scientist, misinformation is one of your worst enemies. Although data science is by no means an easy endeavor, the ability to identify a data science myth or misconception when you encounter it might provide a source of clarity and comfort.