Today, the world as a whole is producing enormous amounts of data every day at an accelerating pace. As a matter of fact, the tech industry is hoping to find optimal ways to address and retain data. Data science comes into play here. Let’s face it, who hasn’t heard this jargon percolating all around? As the buzz grows, there is a lot of speculative discussion about what data science actually entails.
In fact, the actual, some broadly held “facts” are really mere speculation, misguided assumptions, or, outright, blatant myths. To counter these kinds of fallacies, we’ll debunk 5 of the most pervasive data science myths by leveraging actual data science facts. You’ll see why it’s important to comprehend the truth that lies beneath each myth that we dispel.
1. Data scientists must be very proficient programmers
It should go without saying that data scientists require programming skills, however, they don’t always need to have years of coding experience. If you’re not a coder, you can learn to code quite quickly and improve with time. Being a dedicated coder is not necessary, but having a logical approach is essential. Many talented data scientists have built successful careers in the field without any formal programming knowledge or experience.
Thus, it is evident that data science does not just rely on coding, but data scientists still require basic coding abilities in order to use data science software, particularly Python, which is initially simple to master. Data scientists reportedly prefer Python over other programming languages, according to Zdnet.com. With a good one or two months of training, this skill set is simple to pick up.
2. Experienced professionals should start afresh when they switch to data science
According to a Deloitte Access Economics survey, a staggering 76% of organizations aim to expand their expenditures on their data science and data analytics capabilities within the next two years, demonstrating that there are multiple industries that are using data science pretty thoroughly.
The aforementioned myth may hold true if you completely change your field to pursue a career in data science, but it is far simpler to find employment in data science if you continue with your current field. Given that data science is applied to virtually every industry out there, professionals with experience in a certain domain can take advantage of that competence towards applying data science into the domain. All experiences are considered when switching to data science.
Having a solid understanding of the field you want to work in will influence your transition plan. You will need to select domain-specific projects and necessary data science abilities in order to make things happen. The second step is to evaluate your current skill set in light of your training and employment history. By being aware of this, you will be able to pinpoint your options more clearly and recognize your strong and weak points, which will elevate your transition plan. Likewise, degrees that aren’t quantitative are permitted. If you have a business background or an economics degree, for instance, you may be able to target data science prospects in the fintech industry.
3. Beginners are Not Eligible for data science job roles
The basic answer is everyone eager to learn data science, whether they are seasoned professionals or complete beginners. To add to that, many job portals indicate that 45 to 50% of the available job openings are for data science freshers.
The subject of data science is both challenging and lucrative. You would need to be very tenacious to succeed in this sector because it is difficult to find employment as a data scientist. Particularly if you’re trying to start a career in data science as a novice, it needs a lot of learning, experience, and conceptual knowledge.
But luckily, numerous educational sites offer data science learning tools, including boot camps, online courses, finished end-to-end data science and machine learning projects, free video lectures, etc. This has made it considerably simpler for someone without any prior knowledge of mathematics or data science to begin a career in the field. Plus, data scientists are paid handsome salaries reaching 10,00,000 LPA in India at an entry-level according to Ambitionbox.com.
4. Be from an IT background like computer science, mathematics, or statistics or fall short
Only those with an IT background should pursue data science. Many people continue to hold onto this persistent fallacy. Analytics is a discipline that is open to anyone without a background in programming or IT, despite the fact that several IT experts do desire to develop their analytics skills. Without any prior coding or IT skills, many prominent data scientists started their careers.
Although it is undeniable that excellent data scientists need strong mathematical and statistical skills in addition to strong logical and critical thinking skills, lacking advanced degrees in mathematics or computer science does not necessarily prevent one from advancing in their job.
According to industry reports, more than 70% of candidates who switch to data science careers are from non-IT backgrounds. Indeed aspirants from Mechanical, Civil, Arts, and other Non-IT backgrounds also can seek a data science career, given that data science is cutting through almost every industry out there and its applications go beyond just information technology or computer science domains.
You will need to take into account a few things that persons with this background have already, though. Data science is an intricate field with many facets. You’ll need to start from scratch learning fundamentals as a newbie. And this is where the qualities of commitment and perseverance come into play.
5. You cannot become a data scientist in a year, it takes a very long time
With a growth forecast of 31.4% by 2030, the U.S. Bureau of Labor Statistics lists data scientists as one of the top 10 fastest-growing professions in the country. There has never been a better moment to enter the area of data science. But remember that becoming a true data scientist may not be as simple as grasping the theoretical underpinnings of the field. It is most likely to take a period of 8 months to 1 year for any data science aspirants without prior coding skills to learn the necessary skills and successfully get started in a career in data science.
Students who enroll in data science boot camps receive in-depth, career-specific instruction from specialists in the field to gain extensive, practical experience in data science. By the time they’re done, they have a number of networks to use to get employment and even support for career coaching. Taking up data science training from reputed institutions that also offer internship opportunities and job assistance corresponding to industry-centric training is the key here.
Final Say
Numerous aspects of our lives and business procedures have been improved by data science and its numerous uses. Data science appears to be a field that will endure for many years to come, given the significant impact it has had. The extremely powerful and positive impacts that data science can have on our daily will eventually force those who disregard the profession to face reality.
Doing frequent fact-checks is the strongest defense against believing myths. Always double-check the validity of any information if you are unsure. There’s no harm in validating your accuracy if you’re at least somewhat certain. Many more fallacies are trailing data science like a cloud.
DataMites is the global institute offering the best data science courses in the current market. Having trained over 60,000 students in the domain of data science, artificial intelligence, python, machine learning, data analytics, and MLops, DataMites courses are accredited by internationally recognized IABAC, NASSCOM, and Jain University. Our data science courses are up-to-date with the growing industry demands and are meticulously designed by industry experts. You will also be given access to data science internship opportunities and job assistance which is indeed a catch! Therefore, if you want to pursue a career in data science whilst also dispelling all of these unfounded notions, feel free to check out our courses.
Watch this video: 5 Common Myths about Data Science
