Myths About Data Science
Transitioning into the data science domain is easy if you get the right mentorship and technical skills that this field requires. Among the hype going on in the market about data science, there are hundreds of myths about data science that we hear in the current scenario.
In this article, we’re going to discuss the 10 myths about data science. Here are the most popular ones:
1. Data Science is tough to learn, and you need hardcore knowledge of programming languages.
This is one of the most common & popular myths about data science. Most working professionals or students who don’t have prior knowledge of any programming languages often get confused when they are told to move to data science. Many people avoid pursuing data science as a career because they fear programming/coding.
This myth about data science is only partially true as tasks related to data analysis, data visualization, data mining, and data cleaning require knowledge of specific tools & technologies. The most commonly used tools in data analysis are NumPy, Pandas, Matplotlib, and Seaborn. The tool used in big data analysis is PySpark. All these tools are libraries of Python programming language. To get your hands on these tools, you must have only a basic knowledge of Python programming that will get you across the line.
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The most used programming languages in data science are Python, R, Scala, and Java. To perform data analysis, you don’t have to learn end-to-end programming languages but only those concepts that are used in data science.
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2. You need a degree in data science, computer science, or statistics to become a data scientist.
While talking to most of the students who are from Non-tech backgrounds, we got to know how this myth is stopping many talents to move to data science.
Out of all the myths about data science, this one holds the most significant amount of popularity. If you’re told that you need a computer science degree, data science degree, or a statistics background if you want to move into data science, this is totally incorrect.
Anyone from any field can pursue data analytics and data science as a career. Whether you are a BA graduate, a BBA, or a BCA graduate, you can pursue data science as a fulltime career.
At Console Flare, we help Non-IT professionals to move to data science.
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3. Only knowledge of tools will make you a good data professional.
Among many myths about data science, this one is very common as you’ve been told that if you learn all the tools & have technical skills for data science, your career can grow significantly. That is partially true statement, and FYI, only technical skills won’t take you through your career in data science.
Then What else?
As data professionals, companies look out for those candidates who excel in some specific domain and have knowledge of a particular industry. The job of a data professional requires a lot of common sense, curiosity, and vision. So, when companies hire data professionals, they prefer candidates who hold the key to a specific industry like Finance, Retail, Healthcare, FMCG, or Supply Chain.
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4. All your previous experience will be considered relevant in data science.
When you move into data science, your previous experience matters, but only if you have relevant experience in the data field. You will be considered a fresher in data science if you’ve worked in other jobs like customer support, sales, marketing, operations, HR, or any other.
The best option to overcome this situation is internships, where you learn to work on projects and data science tools. When you apply for a data science job, your internship is considered an experience, and you can get a slightly better package than a fresher.
5. Only big companies have requirements for data professionals.
Gone are the days when data was taboo in the field of Information technology. With the internet availability all around the globe, companies, whether small, medium-sized, or giants, are producing a massive amount of data.
In the past, tools like MS Excel helped companies manage their data and do the analysis and visualization. Advanced tools like Pandas and PySpark are being used to perform data analysis. Even startups are producing data that requires analysis on advanced tools.
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To help overcome this situation, companies are hiring data professionals who can do these things for them. 90% of the world’s companies are now hiring data analysts, engineers, warehouse managers, and data scientists.
6. Artificial Intelligence will replace data science in the future.
We are already in the future, and it is data science itself. With the emergence of data field, the opportunities and possibilities are infinite. Among all these possibilities, the machine learning and artificial intelligence are most hyped.
Artificial intelligence is still a topic of research and we cannot certainly tell which route it will take but one thing is for sure that every technology that is coming in the future, will generate humongous data that will require more data professionals i.e. produce more jobs in data science field.
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7. Knowledge of Maths is necessary to become a data analyst.
Really! who told you this!!! This statement is totally wrong, and you can consider that non-tech students can easily move to data science.
To become a data professional, you don’t have to be a mathematics wizard. Only a few concepts of statistics are used in data science, and when you learn them, you can perform complex tasks.
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8. Data Science will replace human jobs.
Talking about today, there is no such thing as a human replacement in data science. This field requires operating tools and knowledge of a domain that only a human can contain.
Technologies like Robotic process automation and artificial intelligence can definitely replace humans in the future. Still, data science will always need humans who can perform analysis, which requires a lot of emotions, sentiments, vision, curiosity, and manual input.
9. Data Science is about building models and predictive analysis using machine learning.
Yes, some data science tasks require machine learning and predictive analysis, but it is not like all data science is dependent on machine learning.
Building models and algorithms is a task performed when we work on ML tools, but machine learning is only a part of data science, and there are many more things to do like big data analysis, data mining, data cleaning, data importing, data manipulation, data visualization, and more.
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10. More data means higher accuracy.
When we work on data, sometimes we don’t get the expected results due to the small amount of data. But this myth that more data can give you higher accuracy can only be sometimes correct.
The accuracy of the data is not dependent on the quantity of data but on its quality.
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In this video by Edureka, you can learn more about the popular myths about Data Science domain.
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