You must be thinking to transition into Data Science or chances are there that you have already taken the path.
Now, you heard something about this field and took it to be true. Now what? You slowly turn out to be doubtful about your decision.
It is highly possible that these pieces of “sound advice” are myths and you should be aware of the reality.
I will explain the myths and the reality behind it. After all, the bubble should burst.
Myth 1: You can become a Data Scientist if you are a PhD.
You don’t need to be a super genius and have a PhD. to start your career in Data Science.
A sound knowledge of statistics and that unending love for numbers is all that you need to get the relationship started.
You need to decide before you proceed with preconceived notions.
- If your aim is the Applied Data Science role, i.e., at the entry-level, you can safely walk in without having a PhD. Here you are required to work with the packages and the algorithms built in the work space. The good news is that you are not required to build algorithms.
- In case you are eyeing the Research Role, or the advanced level, PhD. will become imperative. As a data science researcher, you are required to research the need to create an algorithm and write a research paper on it.
Myth 2: Data Science Means Science
Go beyond the words, sometimes…
Data science is not just science but an amalgamation of science and art.
In the first instance, it might seem that data science is all about applying scientific methods to solve problems in a business environment.
But this is the half-baked truth…
Along with the scientific aptitude, you have to have a great combination of logical, analytical and reasoning skills.
Data science isn’t just a specific skill but a practice on a whole
Analogous to the software development life-cycle, data science, too has its own life-cycle.
Myth 3: Master Tool and You have Mastered Data Science
The working knowledge of R or SAS doesn’t mean that you mastered data science.
These are mere tools that do no better than making you adept in its use; what you need to know is the practical application of the predictive modelling techniques.
Do not be like the people, who in anticipation of entering the world of data science, end up just learning the tool.
What works best and in your favor when going in for the profile of data scientist is to possess the lethal working combination of programming, business and mathematical skills.
No harm in filing your toolbox with some multipurpose tools but the catch is to know when and where to use it, along with the practical skills.
Myth 4: You should be a Master Coder
Yet another grave misconception that potential data scientists have.
You don’t need to be a pro at programming or coding.
Many data scientists, the masters of the field, do not code. Thanks to the existing algorithms, coding is mostly all done previously.
But yes, learning to code or getting the hang of it won’t hurt you. Instead, it will help you in placing your career on the path to rapid progression.
What do you need essentially? Possess exceptional analytical capabilities.
The role you wish to fit in might require an exclusive skill that is not the same with your present business. Research and know what your business demands from you and acquire the skills accordingly.
Let us look at the numbers that show who all transition into data science.
Myth 5: Data Scientists are fast becoming a rarity
This is one of the most unbelievable myths surrounding data science!
Whether you are a certified data scientist or not, a fresher or an experienced professional, you will surely learn on the job.
What would work best and profitable for a business? Instead of getting outsiders trained, focus on the people in your workforce who have exceptional analytical skills and are disciplined software professionals.
Train them to become data science specialists and see your business reach heights.
Be wise and create a pool of data scientists that know your business, in and out, instead of investing in the people new to your business and the realm.
Myth 6: Artificial Intelligence will oust data scientists
Poor data scientists… Right? Absolutely wrong!
Though artificial intelligence is a hot topic and machines are believed to do some of the tasks of data scientists, ousting them is simply out of the question.
Because data scientists will be anyways required to give instructions to the machines on how to perform a specific task.
Even when the most complex of algorithms are being built with the intention to completely automate the most manual tasks, such as data cleaning, there is and will always be an ever-increasing demand for skilled data scientists with sound judgment to supervise the work and refine the algorithms.
A machine can’t convince people and ask questions based on the collected data. A data scientist can.
Myth 7: Data Science is Nothing More than a Craze
Understandably the most common misconception.
A known fact that although data science is an infant, yet research is an experienced old man.
The use or research dates back to around 50 years; just then the use was not on a large scale as it is now.
‘Data Mining’ and ‘Data Science’ were coined much earlier and were in vogue since the late 19th century.
Now you might ask why data science became the rage? This is because an enormous amount of data is being generated every minute.
And, quite obviously there was a need to interpret this data and make optimum use of it. This gave rise to data science and made it a wild-cry.
Now, when IoT and Big Data are raging like wild beasts, expect the amount of data produced every minute and consequently the requirement of its analysis, to just escalate.
Still thinking data science to be a craze like one movie wonder actor….. THINK AGAIN!
Myth 8: Data Science only means making predictions
“Don’t you think how powerful will it be to be able to predict future events or occurrences?”
This is what attracts the newbies in the field of data science.
Creating a model that will predict the customer’s next purchase, sounds like an important skill… Agree?
This is what happens when a non-technical person is apprised about data science. There is an unrivaled hype created in this field.
I know what you’re thinking…Possibly, the data scientist must be just creating predictive models, the whole day at work.
Well in 2012, the Harvard Business Review called data science “sexiest job of the 21st century.” But there is more to it.
Here are the various stages in the data lifecycle.
Data science is a mix of association rules and clustering techniques; abnormality detection and the ability to find out the deviation in the data.
You can learn so many things!
Myth 9: Data Science is the slice of cake for large businesses
Data science is that piece of cake that every business- whether an infant or the veteran in the industry- would love to have and can have it.
“Businesses need intricate infrastructure and processes to get the maximum benefit out of their data.”
This is a complete misconception.
You need a couple of genius brains who know the how, why and what of the data science business.
No organization should shell out a fortune to set up analytics infrastructure, just in order to adopt a data-driven approach.
There are enough open-source tools that can be used to process large data accurately and efficiently.
Just have the right understanding of the tools and approach.
Myth 10: Data Scientist is the only Career Option in Artificial Intelligence
No! There are many of them.
When asked about the ideal artificial intelligence team, an enthusiast replied- “Get top data scientists on-board, as many of them as possible.”
But this is again what?
Yes! You guessed it right… A plain, simple data science myth.
Of course, it will be good to have the best talents in the industry, but this doesn’t seem to be a viable option for many of the businesses.
It is an arduous task to search for that one skilled and trained data scientist, how can you even think of getting many of such rare gems on-board?
Will your data scientists be acquainted with the non-AI components of a project? Will they have a hands-on or even knowledge about the hardware part?
In a nutshell, there are innumerable career options in an AI project and not just data scientist.
Here are a few roles that exist in the multidisciplinary realm of AI.
You may not find all of these roles in an organization. The roles and number of people hired, depends completely on the project.
Hear the alarm bells ringing, if you are on-boarded for a project that just has data scientists. There will be different people catering to the different aspects of a project.
To sum up, “don’t believe everything you listen”.
Data Science isn’t just the rage. It is one of the most useful things that can happen to a business. You can analyze the data and make predictions.
Finally, there is something that can put large data to good use; instead of leaving it as a mere pile of useless numbers.
Know it, get trained and reap the extensive benefits out of DATA SCIENCE.