Enough of these Data Science Myths & Misconceptions
Get rid of all the Data Science myths
LET’s BEGIN…
Unfortunately, it is certain to have myths & misconceptions for anything which is trending around the world. Data Science being the overflowing bandwagon, still confuses a considerable number of folks.
Dearth of sufficient knowledge results in failure of people to fully understand what exactly is Data Science & what does Data Scientists do. All these only leads to circulation of misinformation & thus escalation of misconceptions & myths among young Data Science enthusiasts.
Begin your Dream Journey of Data Science
This article is an attempt to end the commonly held myths & misconceptions about Data Science & getting you acquainted with reality.
MYTH #1
Data Science only fits best for large organizations with large resources
A large number of business & entrepreneurs have a false opinion about Data Science that it is only suited well for big organizations. They think that Data Science is not for small & medium sized organizations. It is because of the misconception that Data Science requires a sophisticated infrastructure to process & get the most value out of your data.
Reality!!
It requires only a group of people who knows how to extract the valuable information out of the available data. Considering the data-driven approach, it is not needed to invest a handsome amount of money for setting up analytics infrastructure in the organization.
There are multiple open-source tools out there that can help in processing the large scale data with accuracy & efficiency.
MYTH #2
Data Science is difficult to adopt because it’s complicated
A lot of people think that it is quite challenging & even problematic to integrate the process of Data Science with organization workflow. It may be probably because of this reason that many businesses resist adopting it.
Reality!!
While Data Science can be a complex undertaking, it is not necessarily meant to be. The easiest way to adopt it can be simply starting and endeavor to achieve success with that. Expand your capabilities once succeeded.
The common mistake committed by most of the organisations is they collect the data they think is valuable, derive insights & push them to decision-makers via reports. And later they start building models on those data to extract the finest insights.
Although there is no single right way to adopt Data Science, the wrong path is essentially over complicating the problem when there is an effective & elegant simple solution available.
MYTH #3
A Ph.D. in Statistics is essential to be a Data Scientist
Many people are of the opinion that in order to become a Data Scientist you need to have a fancy Ph.D or hold a Master’s Degree in Data Science.
Reality!!
No, it is not mandatory. Data Science is all about crunching numbers to get meaningful insights & it makes the use of statistics to better understand the results.
Although when we have to perform high level tasks such as Machine Learning & Deep Learning, an advanced knowledge of statistics is required. But it totally doesn’t mean that one who does not possess a degree in Math or Statistics cannot become expert Data Scientists.
MYTH #4
More accurate results only if you possess more data
Many organizations think that collecting large sets of data & analyzing them by using modern tools will result in greater accuracy.
Reality!!
Unfortunately it is not the same & more data does not guarantee accurate results. The amount of data one possesses is not significant, it’s the way we work on it i.e. more effectively & precisely.
MYTH #5
Acquiring an expertise in the Data Science tools is enough
Many people are of the wrong opinion that learning a statistical tool such as SAS or mastering Python & its associated Data Science libraries are enough to get the tag of a Data Scientist.
Reality!!
While learning a skill or a tool is essential, but it doesn’t mean that it is the only requisite to do effective Data Science. It is essential for an individual to go beyond the tools & master other skills such as problem-solving, understanding of business domain & knowledge of correct application of tools for any business problem.
Along with this, excellent communication skill is also required to present the findings & insights in the most easiest possible way.
MYTH #6
Data Science is all about building models
A majority of people have a wrong opinion that Data Science is all about building models & that Data Scientist’s work all day over building those models.
Reality!!
As a matter of fact, Data Science is much more than building models & there are multiple phases in the overall Data Science process. For instance, it includes data cleaning, data collection, exploratory analysis, verifying the data, etc. Thus, building a model is a single layer of the Data Science project & it is far beyond that.
MYTH #7
Data Science will soon be substituted by Artificial Intelligence
Because of the increased adoption of automation in Data Science, most people think that it will totally be replaced by Artificial Intelligence in the coming future. One of the reasons behind such an opinion may be probably because of the task of finding patterns. With no doubts machines tend to perform better than humans.
Reality!!
But this is not the case. While there’s quite a possibility that AI will carry some of the repetitive & tedious tasks such as data cleaning & data preparation. However, Data Scientists will always play an essential role to carry out the advanced operations & instructing the machine what needs to be done.
There’s a continuous drive among people to automate Data Science. And as a result they are involved in building more sophisticated algorithms to eliminate the need for Data Scientists. But it is most unlikely to happen as even the most advanced AI systems will require human guidance & instructions.
MYTH #8
Data Science is just a hype, it won’t last long
This is the most common misconception or even a debate that Data Science is just a fad & it won’t last for too long. There are many people who consider the domain of Data Science as a bubble that’s going to burst too soon.
Reality!!
Data Science has already become the most essential aspect of any organization irrespective of the domain. And it is known by everyone that we are generating huge amounts of data per day.
With this continuous increase in new sources of data, it is Data Science which will help us in data structure, data analysis, drawing hidden patterns for business. And such processes will only help in building solutions & solving the crucial real-world problems.
MYTH #9
To excel in the field of Data Science, you need to be a hardcore programmer
People often confuse that being a Data Scientist involves writing lines of code & algorithms. They think that in order to be an expert in the field of Data Science, an individual needs to be an excellent programmer.
People also believe that it is necessary to have a computer science or programming background to pursue a career in the field of Data Science.
Reality!!
If we pay attention to the routine tasks of Data Scientists, then we’ll realize that there is no significant coding involved. In fact, most of the methods or algorithms are readily available which just needs a little tweaking. However, for this purpose a logical bent of mind is needed.
BOTTOM LINE…
Lack of information or misinformation leads people to make assumptions which prove wrong the majority of times. And such experiences often result in doing more harm than good.
Challenge your diligence in Data Science
Data Science being one of the most talked topics of the town, is one of the most popular skills to have in your resume at present. But to harness the full potential of Data Science it is equally important to do the necessary research & clear all the confusions & misconceptions before actually getting involved in it.
Just remember these tips & get certain that you possess the right knowledge & BOOM! You’ll be on your way to success in no time.
